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EXTENDING GEOMATICS CONCEPTS AND

CAPABILITIES FOR SCIENTIFIC VISUALIZATION AND COMMUNICATION: INTEGRATING

PHOTOREALISM WITH GEOVISUALIZATION

by Zoran Reljic

A thesis submitted to the Faculty of Graduate and Postdoctoral Studies in partial fulfillment of the requirements for the degree of

Master of Science of Geography

Department of Geography UNIVERSITY OF OTTAWA

© Zoran Reljic 2006

Preface ii

Abstract

This thesis evaluates the importance and impact of photorealistic visualization in

contemporary geomatics and identifies and operationalizes state of the art technology for

integrating photorealistic visualization within geomatics. Currently, the creation of

photorealistic visualization is a challenge due to present deficiencies in geomatics

technology. The visualization capabilities of state of the art geomatics platforms are at

least a decade behind contemporary 3D visualization packages and workflows. Therefore,

this thesis identifies a need for an integrated approach where the advantages of both

technologies can be combined to improve communication in geomatics. To illustrate the

integration, a case study underlines the benefits of photorealistic visualization as a

geomatics communication tool and clarifies the benefits of this approach over the

capabilities of current geomatics visualization approaches and technologies. The case

study applies a novel integrated approach combining geomatics, earth observation data

and 3D visualization technologies to create photorealistic visualizations in select

Canadian National Parks. The potential of these visualizations as communication tools

for public outreach on various levels (e.g. local, national and specific populations, such as

students) is made explicit by example. Finally, this research provides a demonstrated

workflow that integrates earth observation data, geomatics and 3D visualization

technologies as a vehicle for extending contemporary geomatics into the realm of

contemporary photorealistic geovisualization.

Preface ii

Résumé

Cette thèse évalue l'importance et l'impact de la visualisation photoréaliste dans la

géomatique contemporaine et identifie et opérationnalise une technologie dernier cri pour

intégrer la visualisation photoréaliste dans la géomatique. Actuellement, la réalisation de

la visualisation photoréaliste est un défi dû aux insuffisances technologiques actuelles de

la géomatique. Les possibilités de visualisation des plateformes de technologie récente en

géomatique sont au moins une décennie derrière les applications contemporaines de la

visualisation 3D et de leurs déroulements des opérations. Par conséquent, nous avons

identifié un besoin pour une approche intégrée où les avantages des deux technologies

peuvent être combinés pour améliorer la communication en géomatique. Pour illustrer

l'intégration, une étude de cas souligne les avantages de la visualisation photoréaliste

comme outil de communication en géomatique et clarifie les avantages de cette approche

au-delà des possibilités des approches et des technologies courantes de visualisation en

géomatique. L'étude de cas applique une nouvelle approche intégrée combinant la

géomatique, les données d'observation de la Terre et les technologies de la visualisation

3D pour créer des visualisations photoréalistes pour certains parcs nationaux canadiens

sélectionnés. Le potentiel de ces visualisations comme outils de communication pour la

diffusion publique vers des populations de niveaux divers (par exemple locaux, nationaux

et plus spécifiques, tels qu’auprès des étudiants) est rendu explicite par ces exemples. En

conclusion, cette recherche démontre un déroulement des opérations qui intègre des

données d'observation de la Terre ainsi que des technologies géomatiques et de

visualisation 3D comme véhicule pour prolonger la géomatique contemporaine dans le

domaine de la géovisualisation photoréaliste contemporaine.

Preface iii

Acknowledgements

During the course of this work, I was blessed with the support of many people.

Here is my acknowledgement of the most important of them – those who shaped my

work and me as a person.

Dr. Sawada, I am thankful to you for your teaching, mentoring, encouragement,

and above all friendship during this work. You are the teacher who made the difference in

my career and life. The decision to join your research group was one of the best I have

ever made. You encouraged me to grow as a researcher and as a person, to discover great

happiness in my work and to achieve success that I have never dreamt possible.

I am also grateful to Dr. Konrad Gajewski and Dr. Luke Copland, who

contributed to shaping this work into its final form.

Very, very special thanks go to my wife, Renata: The winner of the best wife

award for unconditional love “since clocks kept time”. Without you, I would have never

dared to explore the world beyond my cocoon and have never grown into the person I am

today.

Thanks are also due to the members of the LAGGISS team who convinced me

that there are no strangers in the world just friends that we haven’t met yet. Thanks, our

discussions and friendship are precious to me.

For the financial support during this project, I am grateful to Parks Canada

Agency and Dr. Michael Sawada.

Preface iv

Table of Contents

ABSTRACT ..................................................................................................................................................II RÉSUMÉ.......................................................................................................................................................II ACKNOWLEDGEMENTS ....................................................................................................................... III TABLE OF CONTENTS ........................................................................................................................... IV LIST OF FIGURES.................................................................................................................................... VI LIST OF TABLES...................................................................................................................................... IX GLOSSARY ..................................................................................................................................................X CHAPTER 1 INTRODUCTION .................................................................................................................. 2

1.1. RESEARCH OBJECTIVES................................................................................................................ 4 1.2. THESIS STRUCTURE...................................................................................................................... 6 1.3. REFERENCES............................................................................................................................ 7

CHAPTER 2 PHOTOREALISTIC GEOVISUALIZATION: A REVIEW.................................................. 8 2. ABSTRACT.......................................................................................................................................... 9

2.1. INTRODUCTION............................................................................................................................. 9 2.2. VISUALIZATION OF GEOSPATIAL DATA: GEOVISUALIZATION ..................................................... 11

2.2.1. Definition.............................................................................................................................. 11 2.2.2. History .................................................................................................................................. 11

2.3. IMPORTANCE OF GEOVISUALIZATION ......................................................................................... 13 2.4. IMPACT OF GEOVISUALIZATION ON GEOMATICS SCIENCE.......................................................... 15

2.4.1. Landscape visualizations ...................................................................................................... 15 2.4.2. Geovisualization in urban planning and development ......................................................... 18 2.4.3. Other applications of geovisualization ................................................................................. 19

2.5. PRESENT CHALLENGES IN PHOTOREALISTIC GEOVISUALIZATION.............................................. 20 2.6. CONCLUSIONS ............................................................................................................................ 22 2.7. REFERENCES .............................................................................................................................. 24

CHAPTER 3 INTEGRATION OF 3D VISUALIZATION AND GIS FOR MONITORING AND COMMUNICATION OF ECOLOGICAL INTEGRITY IN CANADA’S NATIONAL PARKS............... 28 3. ABSTRACT........................................................................................................................................ 29

3.1. INTRODUCTION........................................................................................................................... 29 3.1.1. Objectives of the study.......................................................................................................... 30

3.2. PHOTOREALISTIC GEOVISUALIZATIONS CHALLENGE .................................................................. 33 Why use photorealistic visualizations as a public outreach tool? ...................................................... 34

3.3. VISUALIZATION CASE STUDIES: NATIONAL PARKS IN CANADA................................................... 36 3.3.1. Auyuittuq National Park, Nunavut........................................................................................ 37 3.3.2. Nahanni National Park Reserve, Northwest Territories....................................................... 39 3.3.3. La Mauricie National Park, Québec..................................................................................... 42 3.3.4. Key features to be visualized ................................................................................................ 44

3.4. AN INTEGRATED APPROACH TO PHOTOREALISTIC LANDSCAPE VISUALISATION....................... 45 3.4.1. Data Collection and Evaluation ........................................................................................... 46

Preface v

3.4.2. Modeling............................................................................................................................... 50 3.4.3. Terrain modeling techniques ................................................................................................ 55 3.4.4. Animation (Photorealistic dynamic visualization)................................................................ 65 3.4.5. Light and camera positioning............................................................................................... 68 3.4.6. Rendering ............................................................................................................................. 68 3.4.7. Compression of output data.................................................................................................. 70 3.4.8. Post-processing .................................................................................................................... 70

3.5. PUBLIC OUTREACH: TOOLS AND RESULTS .................................................................... 73 3.5.1. Photorealistic fly-through presentations .............................................................................. 74 3.5.2. Public outreach: Various Levels........................................................................................... 81

3.6. CONCLUSIONS AND RECOMMENDATIONS .................................................................... 84 3.7. REFERENCES .............................................................................................................................. 86

CHAPTER 4 CONCLUSIONS AND RECOMMENDATIONS................................................................ 89 APPENDICES ............................................................................................................................................ 93 APPENDIX 1 COMPUTER GRAPHICS FOR PHOTOREALISTIC LANDSCAPE VISUALIZATION 94 APPENDIX 2 A SIMPLE STUDY EVALUATING THE POTENTIAL OF VISUALIZATIONS AS A COMMUNICATION TOOL IN GEOMATICS ........................................................................................ 127 APPENDIX 3 INTERNATIONAL ENVI CHALLENGE 2005 AWARD............................................... 146 APPENDIX 4 CANADIAN INSTITUTE FOR GEOMATICS 2005 CONFERENCE PAPER............... 154

Preface vi

List of Figures

CHAPTER 2 PHOTOREALISTIC GEOVISUALIZATION: A REVIEW............................................ 8 Figure 1 Moore’s Law.................................................................................................................................. 12 CHAPTER 3 INTEGRATION OF 3D VISUALIZATION AND GIS FOR MONITORING AND COMMUNICATION OF ECOLOGICAL INTEGRITY IN CANADA’S NATIONAL PARKS....... 28 Figure 2 Proposed National Parks of Canada for the Government Related Initiatives Program (GRIP)

Project ................................................................................................................................................. 32 Figure 3 Auyuittuq National Park, Buffin Island, Nunavut, as shown in a Landsat 7 ETM+ scene from

Geobase (www.geobase.ca) ................................................................................................................ 38 Figure 4 Nahanni National Park, NWT ........................................................................................................ 41 Figure 5 La Mauricie NP, Québec as shown in a Landsat ETM+ mosaic based on data from Geobase

(www.geobase.ca) ............................................................................................................................... 43 Figure 6 Workflow for the integration of GIS and scientific visualization .................................................. 45 Figure 7 An example of a DEM structure: a) raw data Auyuittuq NP, b) 400% zoomed, c) 800% zoomed 47 Figure 8 Cartographic model of DEM data pre-processing.......................................................................... 50 Figure 9 DEM Pre-processing. a) 4 adjacent raster datasets b) merged raster dataset. Data source:

www.geobase.ca, Scale 1: 50 000....................................................................................................... 51 Figure 10 Different channel combinations lead to various composite images. Modified from Aronoff

(2005). Numbers below composite images represent Landsat band combinations, e.g. 321 is a color composite of band 3 - red, band 2 – green and band 1 – blue. Band numbers are explained on a left panel.................................................................................................................................................... 53

Figure 11 Image fusion. Example from Auyuittuq NP. a) panchromatic image (15 m resolution); b) true color composite LANDSAT 7 ETM + image (30 m resolution); c) the results of image fusion (15 m resolution) ........................................................................................................................................... 55

Figure 12 a) NTDB contour lines (scale 1:50 000) and b) LANDSAT image of the same area of the Auyuittuq NP ...................................................................................................................................... 56

Figure 13 Cartographic model of contour lines pre-processing.................................................................... 57 Figure 14 a) Imported CAD drawing into 3ds Max as editable splines; b) Generated triangulated mesh in

3ds Max based on contour data of the Auyuittuq NP.......................................................................... 57 Figure 15 A 3ds Max terrain model with applied color for elevation zones of the Auyuittuq NP. Left: top

view; Right: oblique view. .................................................................................................................. 58 Figure 16 An example of a gray scale image used as a displace map obtained from................................... 59 Figure 17 A screen capture of imported DEM data of Auyuittuq NP in 3Dem............................................ 60 Figure 18 Dreamscape Terra Editor Workspace........................................................................................... 61 Figure 19 Different tools that can increase detail and realism of terrain. a) Terrain erosion; b) Elevation; c)

Slope; d) Texture map paint................................................................................................................ 62

Preface vii

Figure 20 Vegetation distribution in La Mauricie NP. a) SPOT 5 panchromatic image(resolution 5m); b) resulting forest distribution determined according to the SPOT 5 image ........................................... 64

Figure 21 Vue 5 Infinite: a) Fir tree instance in the tree toolbox; b) A rendered example of photorealistic fir tree ...................................................................................................................................................... 65

Figure 22 Populating sparse vegetation areas covered with grass and shrubs with Vue 5 Infinite............... 65 Figure 23 Story board for Thor peak in Auyuittuq NP................................................................................. 67 Figure 24 A screen capture of Adobe Encore DVD workspace ................................................................... 71 Figure 25 DVD Main Menu ......................................................................................................................... 72 Figure 26 Visualizations on mobile dissemination devices; a) Apple® iPod video mp3 player; Hewlett

Packard iPaq® PDA (personal digital assistant)................................................................................. 73 Figure 27 Major glacial geological features of Auyuittuq NP...................................................................... 75 Figure 28 Four fly-through routes in Auyuittuq NP, green dots represent start and red dots are the end of

the routes............................................................................................................................................. 76 Figure 29 A winter scene from Auyuittuq NP with procedural texture (computer generated snow) ........... 78 Figure 30 Photorealistic fly-through routes through Nahhani NP ................................................................ 79 Figure 31 La Mauricie NP: Different views of a clear-cut area.................................................................... 81 Figure 32 DVD for educational outreach: Main menu-NPs ......................................................................... 83 APPENDIX 1 COMPUTER GRAPHICS FOR PHOTOREALISTIC LANDSCAPE VISUALIZATION...................................................................................................................................... 94 Figure 33 Elements of computer graphics used for photorealistic landscape visualization.......................... 94 Figure 34 A 3D Cartesian coordinate system ............................................................................................... 95 Figure 35 A scene with global (a) and local (b,c) coordinate systems ......................................................... 96 Figure 36 An example of a triangle structure ............................................................................................... 97 Figure 37 Elementary modelling primitives: A) Points; B) Triangles; C) Wireframe; D) Polygons............ 98 Figure 38 Increasing the number of polygons improves the object smoothness .......................................... 99 Figure 39 B-spline curve with its vertices (3D Studio Max 2003) ............................................................. 100 Figure 40 NURBS Surface; A) control vertices, curves and surface mesh; B) rendered surface. .............. 101 Figure 41 a) Contour lines; b) Zoomed-in segment with splines; c) Triangluated surface; d) Rendered

terrain model ..................................................................................................................................... 101 Figure 42 Examples of 3D procedural fractal terrains................................................................................ 103 Figure 43 A tree rendered with: A) ray-tracing algorithm in 3ds Max; B) basic OpenGL......................... 104 Figure 44 Landscape without and with clouds ........................................................................................... 106 Figure 45 Geospecific and computer generated textures. a) IKONOS (Resolution: 1m); b) QuickBird

(Resolution: 0.6m); c) LANDSAT (Resolution 15m); d) Procedural texture ................................... 108 Figure 46 Two different camera types in a 3d scene: a) Target camera; b) Free camera with predefined

motion path ....................................................................................................................................... 111 Figure 47 Example of bump mapping. a) 3D model without bump mapping, b) Gray scale image as a bump

map; c) 3D model with applied bump mapping................................................................................ 115 Figure 48 Factors influencing the effectiveness of visualization. Modified from Clark and Lyons (2004) 117 APPENDIX 2 A SIMPLE STUDY EVALUATING THE POTENTIAL OF VISUALIZATIONS AS A COMMUNICATION TOOL IN GEOMATICS................................................................................ 127 Figure 49 Key frames for animations: a) Latitude/Longitude, b) Graticule, c) Geoid, d) Map projection e)

GPS................................................................................................................................................... 134 Figure 50 Average response of all students on different questions in the questionnaire ............................ 136 Fgure 51 Typical working space in 3D Studio Max................................................................................... 138 Figure 52 Reponses by gender.................................................................................................................... 141 Figure 53 Responses of the 2nd year geography students ........................................................................... 142 Figure 54 Evaluation of dynamic visualizations by different year of study ............................................... 143 Figure 55 Evaluation of textbook presentations by different year of study................................................ 143 APPENDIX 3 INTERNATIONAL ENVI CHALLENGE 2005 AWARD........................................... 146

Preface viii

Figure 1 Converting contours to DEM…………………………………………………………………….147 Figure 2 a- panchromatic image (15m spatial resolution), b-color image (30m spatial resolution), c-fused image (15m spatial resolution)…………………………………………………………………………….147 Figure 3 a-color, b-wireframe, c-Landsat image draped, d- IKONOS image draped……………………..148 Figure 4 The Band Math tool………………………………………………………………………………149

Figure 5 NSDI images in Auyuittuq National Park obtained by utilizing Band Math………………….....149 Figure 6 A map of Penny Ice Cap in Auyuittuq NP made with ENVI’s QuickMap………………………150 Figure 7 3D view of the part of the Auyuittuq NP showing the Fork Beard Glacier in the summer of 1991

(a) and the summer of 2000(b)…..…………………………………………...……………………..151 Figure 8 a–Nerutusoq Glacier (Ikonos), b–Summit lake(Ikonos), c–NerutusoqGlacier with vector snow line

and rivers, d-Summit lake with vector snow line and Rivers, e–Thor peakand Fork Beard Glacier (Landsat 321 Composite), f–Crater lake (Landsat 321 composite)…………………...………...….152

APPENDIX 4 CANADIAN INSTITUTE FOR GEOMATICS 2005 CONFERENCE PAPER ........ 154 Figure 1 Auyuittuq National Park…………………………………………………………………………155 Figure 2 La Mauricie National Park……………………………………………………………………….155 Figure 3 Methodology and process flow for scientific visualization……………………………...………157 Figure 4 Example key frames for different visualizations, a) ice-berg near Pangnirtung Fjord; b) Snow

accumulation in Auyuittuq; c) Crater Lake in Auyuittuq with glacier……………………………..158 Figure 5: Textured vegetation, a) Visualization of a single tree using 3ds Max; b) textured ground

simulating grass and a forest canopy using Vue 5 Infinite* ecosystem generator…………………159 Figure 6 An example of a terrain generated and showing a view of Auyuittuq National Park..…………..160 Figure 7 A terrain visualization example of the Summit lake region of Auyuittuq National Park in Nunavut

using IKONOS and pansharpened and fused Landsat datasets as a texture base………………….160 Figure 8 a) 2004 SPOT 5 false colour composite of campground near entry of parkway in La Mauricie

National Park. The red area indicates smaller spectral response to vegetation structure/canopy; b) NDVI derived from SPOT 5 image where dark colours indicate less green vegetation; c) 1999 Landsat TM NDVI for same region illustrating darker colours around campground. Note that the SPOT 5 and Landsat derived NDVI are not radiometrically equivalent so the intensities are not directly comparable; This area of the park has had problems with spruce budworm infestations over the past years. d) Same as a) but illustrating an area razed in 2003; e) same as d) but for 1999 Landsat true colour…………………………………………………………………………………162

Preface ix

List of Tables

CHAPTER 3 INTEGRATION OF 3D VISUALIZATION AND GIS FOR MONITORING AND COMMUNICATION OF ECOLOGICAL INTEGRITY IN CANADA’S NATIONAL PARKS....... 28 Table 1 A comparison of basic building elements of animation................................................................... 33 Table 2 Comparison: Geometric, photorealistic and original scene (Angsuesser and Kumke 2001)........... 35 Table 3 Key features to be visualized in order to develop a workflow integrating photorealistic

geovisualization with contemporary geomatics. ................................................................................. 44 Table 4 Comparison between rendering algorithms (3D Studio Max 2003) ................................................ 69 APPENDIX 1 COMPUTER GRAPHICS FOR PHOTOREALISTIC LANDSCAPE VISUALIZATION...................................................................................................................................... 94 Table 5 Comparison: Ray tracing and radiosity. Reproduced from (3D Studio Max 2003) ...................... 114 Table 6 Combined various classifications of preattentive features............................................................. 118 Table 7 Comparison between different presentations reproduced from (Angsuesser and Kumke 2001)... 121 Table 8 Advantages and disadvantages of visualizations. Adapted from (Libarkin 2002)......................... 129 Table 9 Statistical Summary: All responses ............................................................................................... 141 Table 10 Summary statistics for male and female students: Visualizations vs. Textbook ......................... 142 Table 11 Summary statistics for differences in judging the usefulness textbook and the dynamic

visualization by different peer groups............................................................................................... 142 Table 12 ANOVA: Evaluation of dynamic visualizations, differences among different years of study .... 143 Table 13 ANOVA: Evaluation of textbook, differences among different year of study ............................ 144

Preface x

Glossary

2D: two-dimensional geometry characterized by Cartesian (x,y) coordinates.

3D: three-dimensional. Descriptive of a region of space that has width, height and depth.

Characterized by Cartesian (x,y,z) coordinates.

3DS: a file format in 3ds Max which contains only details of the geometry and surface

properties of an object.

Aerial oblique: a view taken from above looking down at an angle.

Albedo: a measure of the brightness of a reflective object or surface.

Ambient light: surrounding or environmental light that is everywhere equally intense

and has no directionality.

Animation: a medium that creates the illusion of movement trough the projection of a

series of still images or ‘frames’.

Anti-aliasing: an algorithm to prevent the jagged appearance of edges in an image,

which works during rendering by averaging adjacent pixels with sharp variations in color

or brightness.

Aspect ratio: the ratio of the width (x-axis) of an image to its height (y-axis).

Atmospheric effect: components of a 3D software solution that produce effects like fog,

fire and volumetric lighting effects.

AVI: Audio Video Interleave; a file format for animations and multimedia developed by

Microsoft Corporation.

Bitmap: a digital raster image. Strictly speaking it is a 1bit black and white

(monochrome) image. However the term is often applied to any two-dimensional image,

regardless of bit depth.

Preface xi

Boolean: an object created by combining two objects using mathematical operators.

Bump map: a black and white image used in computer rendering to simulate the three-

dimensional detail on the surface of an object.

CAD: Computer Aided Design software, design for creating digital representation of 2D

and 3D objects and space.

Camera: a virtual viewpoint in 3D space that possesses both position and direction. In a

3D scene, the camera represents the viewer’s eye.

Camera move: a movement of the virtual camera within a 3D software analogous to one

in real world cinematography.

Camera path: the path in virtual space along which the camera moves during the course

of animation.

Compression: a technique for reducing the quantity of data required to make up a digital

image.

CPU: central processing unit (processor) a computer’s component that processes data

contained in computer programs.

DEM: Digital Elevation Model. The representation of continuous elevation values over a

topographic surface by a regular array of z-values, referenced to a common datum.

Typically used to represent terrain relief.

Displacement map: a black and white image that modifies the actual underlying

geometry.

Depth of field: a way to enhance the realism of a rendering by simulating the way a real-

world camera works. With a broad depth of field, all or nearly all of a scene is in focus.

With a narrow depth of field, only objects within a certain distance from the camera are

in focus.

DPI: dots per inch, used to measure the resolution of images either on screen or on paper.

Drape: projecting an image onto a 3D surface such as DEM to create a realistic

representation.

DVD: Digital Versatile Disc, a format and optical medium for storing large amounts of

digital data (e.g. 4.7 GB).

EO: Earth observation. EO data are data collected by satellites, aircrafts or land based

environmental stations.

Preface xii

Extrusion: a modeling technique in which a two dimensional profile is duplicated

outwards along a linear path, and the set of duplicated profiles joined to create a

continuous three-dimensional surface.

Face: the smallest possible mesh object: a triangle formed by three vertices. Faces

provide the renderable surface of an object. While a vertex can exist as an isolated point

in space, a face cannot exist without vertices.

Fall-off: the way in which the intensity of a light diminishes with the distance from its

source.

Flythrough: a type of animation in which the camera moves around a scene, rather than

object moving in front of a stationary camera.

Frame: a still two-dimensional image.

Frames per second (fps): a measurement of the number of still frames displayed in one

second of real time to give the impression of a moving image. The standard rates are as

follows: NTSC video—30 frames per second, PAL video—25 frames per second, Film—

24 frames per second.

Geo-reference: to assign accurate real-world coordinates from a known reference

system, such as latitude/longitude, UTM to the page coordinates of an image or a planar

map.

Geo-TIFF: a file format for images, which contains geo-referencing information.

Global illumination: enhances the realism of a scene by simulating radiosity, or the

interreflection of light in a scene.

GUI: Graphical User Interface. An icon based interface that controls a 3D software

package.

Hardware rendering (display rendering): previews a 3D scene within the viewports on

a 3D software package providing real-time on-screen feedback about the effects of

change made to the scene.

Immersion: a method for projecting images such that the viewer’s peripheral vision is

engaged.

Keyframe: an image or set of attributes for a 3D scene, used as a reference point in

animation.

Preface xiii

Level of detail: lets you specify objects with varying face counts that are appropriate for

different viewing distances. Browsers display the less detailed objects when the viewer is

far away from them and substitute the more detailed objects at closer ranges

Light: a point or volume that emits light onto a 3D object. Types of light supported by

within 3D packages include point, spot, directional and area lights.

Maps: the images that are assign to materials are called maps. Examples are standard

bitmaps (such as .bmp, .jpg, or .tga files), procedural maps, such as Checker or Marble,

and image-processing systems such as compositors and masking systems.

Material: a set of mathematical attributes that determine the ways in which the surface of

a model to which they are applied reacts to

Mb: megabit, a unit of information storage ( 1Mb = 1000000 bits).

Mesh: a digital representation of a surface consisting of multiple, possibly curved, line

segments whose intersections form a regular grid.

Model: Used as a verb, to model means to build a 3d object. Used as a noun, it means the

3D object created as the end product of the modeling process.

Morph: to transform a shape image or object smoothly from initial state to a different

final state.

MOV: a file format for QuickTime movies and animations, developed by Apple

Computers Co.

Network rendering: is the rendering of animations using more than one computer

connected by a network. Large and complex animations take many hours to render, even

on the fastest PCs. Network rendering allows us to use the power of other computers to

speed up the process.

NTSC: (National Television Standards Committee) is the name of the video standard

used in North America, most of Central and South America, and Japan. The frame rate is

30 frames per second (fps).

NURBS: (Non-Uniform Rational B-Splines) are a technique for interactively modeling

3D curves and surfaces.

Object: an object in the scene, such as primitive geometry like boxes and spheres, more

complex geometry such as Booleans, and so on.

Orthorectification: correcting distortion in satellite images caused by uneven terrain.

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Particle systems: are objects that generate non-editable sub-objects, called particles, for

the purpose of simulating snow, rain, dust, and so on.

Plane: a two-dimensional surface in Cartesian coordinate space.

Photo-realism: the effort to create synthetic images such as computer renderings,

indistinguishable from photographs or real objects or scenes.

Pixel (picture element): the smallest unit of information in an image or raster map.

Polygon: a geometry element formed by connecting three or more points.

Polyline: a line created by a series of shorter line segments.

Post production: the manipulation of a rendered image, either to improve the quality of

that image, or to create effects that cannot easily be achieved directly within 3D software.

Primitive: a simple three-dimensional form used as the basis for constructive solid

geometry modeling techniques.

Quick Time: a digital technology and file format for animations, developed by Apple

Computer Co.

Radiosity: a technique for rendering 3D scenes. Radiosity simulates the way in which

light bounces from surface to surface within a scene.

Raytracing: a technique for rendering 3D scenes. Raytracing traces the path of every ray

of light from its source until it either leaves the scene or becomes too weak to have an

effect.

Rendering: the process of converting the 3D data into the two- dimensional image ‘seen’

by the camera within the scene. Rendering brings together the scene geometry, Z-depth,

surface properties. Lighting set-up and rendering method to create a finished frame.

Resolution: the size of the final image in pixels when rendering out a scene.

RGB: Red, Green, Blue; a method for representing colors as mixtures of the tree primary

colors of light.

Scene: a set of 3D objects , including the models themselves and the lights and camera

that will e used when rendering them out.

Shading: the mathematical process of calculating how a model’s surfaces react to light.

Specularity: a surface property of an object that determines the way in which

Texture: a 2D raster image used in computer rendering to give color and other apparent

surface characteristics to 3D objects.

Preface xv

TIFF: a file format for color image data, which enables ‘loss-less compression”.

Timeline: a fundamental element of the graphical user interface of most modern 3D

software packages which shows the timing of the keyframes in a sequence of animation.

Tweaking: the process of moving individual vertices of a 3D geometric object.

UVW Coordinates: Most material maps are a 2D plane assigned to a 3D surface.

Consequently, the coordinate system used to describe the placement and transformation

of maps is different from the X, Y, and Z axis coordinates used in 3D space.

Viewport: the region of the interface of a 3D software package in which the scenes

displayed to the user.

Virtual reality: simulated environments and the methods used to create them.

Visualization: the process of creating images using computers

Wireframe: a shading method in which a simple grid of lines is used to represent he

basic contours of the underlying model.

Word space: world space is the universal coordinate system used to track objects in the

scene. When you look at the home grid in the viewports, you see the world-space

coordinate system.

Chapter 1

Introduction

Chapter 1 Introduction 3

1. INTRODUCTION

We live in the most visually oriented society in human history. Today, visuals are the

predominant way of communication because our perception and understanding of visual

information is more efficient when compared to the perception and cognition of

numerical or textual data. Visuals are multifaceted tools that mould and define us and

influence every aspect of our lives. Contemporary science is not excluded from this trend.

Visualization of scientific information is a tool that enables us to “see the unseen” (NRC

2003) and is changing the way we interact, analyse and present information.

Visuals in geomatics science are used to enhance the performance, usability and

accessibility of geospatial information (NRC 2003). However, depending on the purpose

and intended audience, some visuals are more effective than others (e.g. a high level of

scientific abstraction will not be effective for communication with the general public).

Determination and utilization of the right level of abstraction and the appropriate medium

for presentation are the major challenges faced by geomaticians when using visuals to

communicate their results.

The main motivation for this thesis work is the growing indication that photorealistic

visualizations could play a major role in the future of geomatics science not only as a data

exploratory tool but also as a communication tool. Studies have shown that photorealistic

visualizations are the most effective means of communicating with the general public. A

high degree of realism enables the audience to bond easily with the objectives of

visualization and to understand almost instantaneously and universally the intended

message with little or no ‘design-side’ interpretation (Tress and Tress 2003). The

identification of major challenges and the improvement in the efficiency of photorealistic

visualizations as communication tools by integration of GIS and 3D visualization

technology are the major motivations for this research.


1.1. RESEARCH OBJECTIVES

Current geomatics software lack the concepts and capabilities of today’s 3D visualization

software and standards. By way of illustration, it was not until 1997 that ESRI released

their 3D Analyst extension for ArcView, an extension with landscape visualization

capabilities. Within the field of geomatics there are very few individuals actively trying

to extend or upgrade the geomatics toolbox to contemporary visualization standards. This

is surprising given the benefits of modern 3 or 4D (3D + time) photorealistic animation to

both the communication and representation of earth-based data. Thus there exists a

technological, theoretical and practical gap that begs the questions of why and how to

integrate geomatics and contemporary photorealistic visualization. In response, the

objective of this thesis is to determine how current geomatics visualization software

and procedures can be extended and integrated with state of the art 3D photorealistic

visualization. As such, this research will identify weaknesses of current geovisualization

approaches in geomatics, show the benefits of contemporary 3D photorealism to

geomatics, and provide by example a framework to enhance the workflows necessary to

bring state of the art geovisualization into geomatics practice.

To achieve this main objective, a literature review and a case study are presented.

Developing a framework to integrate geomatics and modern 3D visualization concepts

and capabilities first requires reviewing and justifying the potential utility of

geovisualization in geomatics science, and the value added by photorealism. Therefore,

this thesis begins with a comprehensive literature review outlining the importance and

impact of contemporary photorealistic visualization on geomatics science. The working

hypothesis is that visualization plays an important role in the process of scientific

discovery in geomatics (e.g. as an exploratory and confirmatory tool), but more

importantly, photorealistic geovisualization can increase the communication of complex

geomatics concepts, capabilities and datasets, especially to audiences with various

backgrounds. In other words, photorealism can add a significant new dimension to the


geomatician’s toolbox and provide tangible benefits beyond what currently exists in

geomatics packages.

With the potential of photorealistic visualization identified, the current state of 3D

visualization concepts and capabilities in geomatics software are enumerated and

compared to state of the art 3-4D visualization capabilities. Through this enumeration, the

concepts of photorealism are explored and the specific question addressed is: How are

photorealistic visualizations created at present, where are the challenges and the

possibilities for integration with geomatics, and how will this improve the discipline? As

such, the need for an integrated approach brings geomatics up to date with the state of the

art visualization technologies through identification of fundamental concepts and

capabilities that should be included in effective geovisualizations. This review,

enumeration and comparison provides a substantive framework for integration of the two

fields.

Subsequently a case study is undertaken to illustrate the workflow necessary to integrate

geomatics and photorealistic geovisualization. Here, photorealistic geovisualizations of

three Canadian National Parks are used for public outreach. This case study utilizes an

integrated approach where advances in GIS, remote sensing and visualization technology

are combined in order to produce photorealistic animations of the major attractions in the

Auyuittuq, Nahanni and La Mauricie National Parks. The hypothesis was that the

integrated approach will lead toward value added visual tools that can be used in

exploratory research and for public outreach and communication, for example,

communication of the need for the preservation of ecological integrity of these National

Parks. The first step in this communication process was to engage the interest of the

public by presenting them with photorealistic visualizations of the areas that are in some

cases rarely visited by humans. This integrated case study provides a concrete example of

the photorealistic geovisualization framework and extends this framework operationally.


1.2. THESIS STRUCTURE

This thesis consists of four chapters that can be read separately. Each chapter, with the

exception of the first and last, is an independent scientific publication to be submitted to a

refereed scientific journal. Chapter I (Introduction) and Chapter IV (Conclusions and

Recommendations) relate to Chapter II and III.

Chapter II - Photorealistic Geovisualization: A Review. This chapter is a review of

contemporary research in geovisualization. The emphasis is on the importance and

impact of photorealistic landscape visualization in contemporary geomatic science due to

its relevance to the research objectives of the thesis. The use of photorealistic landscape

visualization as an analysis and communication tool in various application areas is

summarized. The areas relevant for our research such as preservation of ecological

integrity, and communication of climate change etc., are especially of interest.

Chapter III - Integration of 3D Visualization and GIS for Monitoring and

Communication of Ecological Integrity of Canada’s National Parks. The objective of

this chapter is to present a case study on the utilization of photorealistic visualization for

three of Canada’s National Parks. The emphasis is on two research questions: 1. How to

bridge the gap in contemporary geovisualization technology by the integration of GIS,

remote sensing and visualization for creation of photorealistic visualizations of Canada’s

National Parks. 2. The use of photorealistic animations created in the first part of this

work for public outreach. The aim of this chapter is to address the central research

question of the thesis by providing a specific workflow and solution to the integration of

geomatics concepts and capabilities with contemporary state of the art photorealistic

visualization.

A DVD with sample visualizations and detailed tutorials is part of this thesis. The

detailed step by step guide for creation of the visualizations that is given in the tutorials is

omitted in the written work due to space limitations.


The work outlined above was partially presented at the following conferences:

1. Z. Reljic, M. Sawada. (2006) Value added mapping: 3D modeling and photorealistic

representation of Arctic landscape. GeoTech, Ottawa, June 18-21.

2. Z. Reljic, M. Sawada, J. Poitevin, and G. Saunders. (2005) Integrating GIS and 3D

Visualization for Dynamic Landscape Representation in Canada’s National Parks.

Canadian Institute for Geomatics, Ottawa Chapter, Ottawa, May 2005

A part of Chapter III was presented at the International ENVI Challenge 2005 where it

won 2nd place for an innovative integration of scientific visualization and geomatics for

improved monitoring and communication of ecological integrity. This work was used as

the basis for a one-day workshop on the integration of GIS and photorealistic

visualization as a part of the program for GeoTech 2006.

1.3. REFERENCES

NRC (2003). IT Roadmap to a Geospatial Future. Washington D.C., The National Academies Press: 1-16.

Tress, B. and G. Tress (2003). "Scenario visualisation for participatory landscape planning - A study from Denmark." Landscape and Urban Planning 64(3): 161-178.

Chapter 2

Photorealistic Geovisualization: A Review

Chapter 2 Photorealistic Visualization: A Review 9

2. ABSTRACT

Geovisualization is one of the emerging areas of geomatics science. It is a multidisciplinary

field integrating exploratory data analysis, GIS, remote sensing and computer graphics in

order to help geomaticians efficiently analyse, understand and communicate geospatial data.

The ever increasing volume and complexity of geospatial data along with advancements in

computer technology have powered the ascent of geovisualization. An additional factor is the

fact that human perception and cognition of visualised information are more effective than the

perception and cognition of numbers alone. Furthermore, the absorption of complex

contextual information is better facilitated when computer generated models closely resemble

reality. Therefore, photorealistic landscape modeling has promise as an effective visual

method that can potentially address several major geomatics challenges simultaneously:

georeferenced data proliferation, data exploration and data communication. This review

summarizes ongoing research efforts in photorealistic landscape modeling and evaluates their

impact in various application fields. Moreover, evidence suggests that there is an existing gap

between 3D visualization and geomatics technologies. The existence and consequences of this

technological gap for the integration of photorealistic visualizations with contemporary

geomatics workflows provide significant impetus for further study.

2.1. INTRODUCTION

It is estimated that about 2 exabytes (= 2 Million terabytes) of data are generated every year

around the world (Keim et al. 2005). In terms of data generation, the 20th century was the

information age, while the 21st is the hyper-information age (Bishop 2000). It was recognized

as early as the 1960s that modern science would need an alternative to communication via

numbers since most of the large datasets generated from then on could no longer be described

by numbers and traditional scientific methods (NSF 2006). “A technical reality today and a

cognitive imperative of tomorrow is the use of images” (NSF 2006). Combining science and

art, and relying on the premise that human perception and cognition of visual data is more

effective than perception of textual or numerical data (Tufte 1990), scientific visualization has

become a corner stone of modern science. Whether medicine e.g. brain mapping

(Panchaphongsaphak et al. 2005), virtual surgeries (Lamadé et al. 1999), engineering e.g.


fluid flow visualizations (Sims et al. 2000), virtual oil drills, emergency scenarios, biology

(e.g. decoding DNA structure (Vernikos et al. 2003), chemistry e.g. molecular modeling

(Morrissey 2005) or business e.g. visualization of various business concepts (NSF 2006),

progress in human knowledge today depends on emerging methods of visual data

presentation, analysis and communication (NSF 2006).

The implementation of scientific visualization in geomatics science has been so intensive that

a new term was devised for this visualization type, namely, geovisualization (MacEachren

1994). This term was a direct consequence of the nature (e.g. different types, dimensions,

scales and sources) and large volume associated with georeferenced data. While there are

different geovisualization methods, for communication among various interest groups,

photorealistic geovisualization could be the most efficient one (Appleton and Lovett 2005). It

includes a high degree of realism that appeals to the audience and provokes natural perception

of the modeled landscapes (Hirtz et al. 1999). Photorealistic and other geovisualizations help

policy makers and the general public in decision making (Al-Kodmany 2001; Kubota and

Kubota 1994), in the preservation of ecological integrity (Hardin et al. 2005) and the

management of real landscapes (e.g. forests) using virtual models (Qi et al. 2004). Taking

tours through virtual cities (Shiode 2000), improving infrastructure, developing better traffic

networks and visualizing preparedness in the case of natural (Brodlie et al. 2005) and other

disasters (Naphtali and Naphtali 2003) are also supported by geovisualization.

The major objective of the work presented here is the evaluation of the current state of

photorealistic landscape visualization with a specific application to public outreach. This

objective involves close examination of various application areas such as landscape and urban

planning, with implications for environmental monitoring and management. Of interest are

those studies that indicate the use of photorealistic landscape visualization as a

communication tool for public outreach and decision making since such intentions provide a

reference frame for the intended applications of high-quality geovisualizations. In addition,

visualization technology itself is examined. The advantages and disadvantages of particular

approaches are collected and evaluated in order to identify the most efficient way of

generating a photorealistic visualization as a precursor to developing a workflow for the

integration with current geomatics technologies.


2.2. VISUALIZATION OF GEOSPATIAL DATA: GEOVISUALIZATION

Geomatics science was among the first scientific disciplines to embrace and benefit from the

utilization of scientific visualization. Geospatial data (e.g. raster or vectors with known

location) are a combination of scientific data with a spatio-temporal component requiring

large volumes of storage and the consequent need for numerous levels of presentation (e.g.

different dimensions, views and level of details). These requirements combined with the

human preference for the visual medium and limitations in our innate spatial thinking (NRC

2003) make scientific visualization an ideal tool for data exploration, analysis and

presentation in geography and GIS.

2.2.1. Definition

The definition of the term geovisualization reflects the multidisciplinary and dynamic nature

of this field. Geovisualization is a new field that combines human visual potential and

technology in order to make spatial contexts and/or problems visible (MacEachren et al.

1999). MacEachren and Kraak emphasised the multi-disciplinary nature of geovisualization

by defining it as a science that combines GIS, cartography, scientific visualization, and

exploratory data analysis that have considerable potential to “provide theory, methods, and

tools for the visual exploration, analysis, synthesis and presentation of data that contains

geographic information” (MacEachren and Kraak 1997). Going beyond exploratory

visualization, Kraak (1999) extended the role of geovisualization in scientific discovery to

include its potential as a confirmatory visual method contributing to the formulation of a

scientific question and finally contributing to general knowledge. Contemporary definitions

usually emphasize various advantages and/or attributes of geovisualization. For instance,

Jiang defined geovisualization as a method that “serves two purposes: communication and

analysis” (Jiang and Li 2005).

2.2.2. History

Since the dawn of civilization, humans have been using pictures to convey information

(Bishop and Lange 2005). Conveying spatial data through pictures is as old as the first

drawing a plan of attack in the sand before hunting or a battle. Starting with the first


visualization of a landscape recorded on a clay tablet 4000 years ago (Lange 2002), the

elements of landscape modeling can be found throughout history. For instance, perspective

was known to the Greeks as early as 500 A.D. and was re-invented a thousand years later

(1500) by the Renaissance painter Durer (Bishop and Lange 2005). Similarly, presentation of

scale was pioneered by Rampton in the 1800s. Ever since, sketches, drawings, maps, and later

photomontages and physical models dominated the communication of geographic concepts.

Although they contained a limited amount of information, these traditional visualization

methods were surprisingly effective and well appreciated (Wood 1994). The beginning of

more advanced and realistic geovisualizations started in the late 1960s. The development of

geographic visualization was influenced by the development of the computer capabilities

which in turn is known to follow Moore’s law (Moore 1965) of integrated circuits. Moore, the

co-cofounder of Intel (the world leader in processor technology), predicted that the

complexity (i.e. power) of integrated circuits will double itself every 18 months while

decreasing in cost two-fold (Figure 1). Computers on which geovizualization is based have

doubled in power every 18 months.

Figure 1 Moore’s Law

In the late 1970s, the first dynamic, 3D computer-generated surfaces were presented

(Moellering, 1978). In the 1970s, intensive work on the fundamentals of computer graphics


e.g. color, texture, half tones) was paramount. In the 1980s, the diversification of rendering

techniques occurred (Nakamae and Tadamura 1995). Enabled by the implementation of the

newly created ray tracing algorithm (Whitted 1980), the fractal (Smith 1984) and particle

models (Reeves and Blau 1983), in the 1980s natural objects such as trees obtained more

realistic attributes and combined with more natural lighting resulted in more realistic

landscape elements and scenes. Today, with numerous applications such as landscape and

urban modeling and planning, environmental monitoring and assessment, disaster scenario

visualization and emergency preparedness visualization, geovisualization has become an

irreplaceable tool for modern geoscientists (NSF 2006).

2.3. IMPORTANCE OF GEOVISUALIZATION

Judging by the proliferation of the topic within the scientific literature, geovisualization has

established itself as an integral part of contemporary geomatics science. However, the

question remains as to how important geovisualization is in the context of the present and

future developments in geomatics. In general, the answer lies in the following three areas:

1. Geovisualization can handle current and incoming georeferenced data proliferation.

2. Geovisualization is a method to accelerate scientific discovery.

3. Geovisualization is a method to improve communication.

Geovisualization can handle current and incoming georeferenced data proliferation. The

rate of georeferenced data proliferation is the strongest justification for the research and

development of new visualization tools. By way of illustration, one satellite alone (e.g. Terra)

in the NASA’s Earth Observing System daily collects approximately 3 TB (terabytes) of data.

This data must be effectively stored, transferred, re-referenced, analysed, and presented in

order to be useful. At present, traditional data exploration and presentation methods are

becoming more and more ineffective or in some cases obsolete. Thus far, geovisualization has

shown considerable potential in dealing with present and expected data volume and

complexity. Present research indicates that geovisualization is a multi-faceted tool that can be

used as an exploratory visual method, confirmatory visual method (e.g. to assist in rejecting

or accepting working hypotheses) and as a synthetic and presentational visual method

(DiBiase 1990).


Geovisualization is a method to accelerate scientific discovery. In today’s world there is a

constant need for acceleration of scientific discovery. It has already been reported that the

utilization of visualization can accelerate new discoveries (Sims et al. 2000). The authors

claim that the acceleration requires synergism of expertise between computation and

visualization scientists in order to accelerate modeling of natural phenomena. Although it is

hard to quantify the importance of a scientific field, two of the measurable variables can be

the number of institutions that have an interest in developments in the field and the financial

support given to these developments. In that regard, geovisualization is judged as a fast

growing and well-funded area of research (Ma 2004). In the United States of America, the

National Research Council established its geovisualization development program in 1987

(NRC 2003). Subsequently, more and more agencies are recognizing the importance of

geovisualization. For example the establishment of the Commission on Visualization of the

International Cartographic Association, the Environmental Simulation Center and various

other institutions such as the Scientific Visualization Studio at NASA are clear evidence of

the importance of this field in accelerating discovery within modern geomatics science.

Geovisualization is a method to improve communication. The science of geomatics is

unique in its needs for effective communication methods. Firstly, to model a geomatic

concept, usually a large amount of complex data is necessary and secondly, the modeling

results need to be presented to an audience with a broad range of backgrounds and interests.

Using 3D photorealistic modeling for communication enables “non-spatially aware” users in

the audience to closely examine various scenarios using static (Appleton and Lovett 2005),

animated or immersive photorealistic applications (Orland et al. 2001). For instance,

photorealistic landscape scenarios are the state of the art support for participatory planning

and development (Tress and Tress 2003), environmental monitoring, urban and landscape

planning as well as other disciplines. Highly realistic presentation of various landscape

scenarios improves the bonding of the audience with the concepts and milieu presented and

thus plays a key role in finding a balance between the various and sometimes mutually

exclusive interests of planners, clients, and the public (Muhar 2001).


2.4. IMPACT OF GEOVISUALIZATION ON GEOMATICS SCIENCE

Geovisualization has transformed the way in which geoscientists model landscapes.

Drawings, paintings, wood, gypsum and other models of our landscapes have been replaced

with three dimensional, interactive and often immersive, user-centric representations1. The

strongest impact of photorealistic geovisualization in geomatics science has occurred in:

1. landscape visualization, and

2. urban geovisualization.

Impacts are also being felt in other applications such as industrial applications and emergency

preparedness support scenarios.

Although landscape and urban geovisualization each have various objectives such as analysis

of environmental risks (Kraak 1994) or generation of photorealistic virtual cities (Pietsch

2000), both areas of geovisualization have the common need to visualize multi-dimensional,

large volume, dynamic data with the ultimate objective of communicating results to other

scientists, law and policy makers or the general public.

2.4.1. Landscape visualizations

The landscape is constantly changing due to natural forces (e.g. water, fire, earthquakes) and

human influence (e.g. agriculture, mining, urban developments, infrastructure). Visualization

as a technique used to evaluate changes in the landscape has been employed in various forms

(e.g. sketches, photographs, photomontage, physical models…) throughout history (Lange

2001). In 1803, Rampton in a work that can be considered pioneering in landscape

visualization, compared “before” and “after” scenarios for the evaluation of his proposed

changes in the landscape (Lange 2001). Today, due to the shift in natural resources

management toward more detailed landscape models on a larger scale, more demanding

environmental regulations, and the emphasis on data exploration and communication during

the planning stage, interest in highly realistic landscape visualizations has increased (Orland

et al. 2001). In order to enable data exploration, support discussion and facilitate decision

making (Orland et al. 2001), contemporary landscape visualization cannot rely on 2D 1 One can liken the development of geovisualization in geomatics to the development of ergonomics (a.k.a. human design) within industrial design and engineering. By way of illustration, ergonomics is user-centric, fitting the workplace to the worker rather than making the worker fit the workplace. Likewise, geovisualization is designed to bring geospatial data into conformity with a user or viewer's world view, reducing mental fatigue, visual fatigue and maximizing the absorption of knowledge.


visualizations where the terrain is usually represented as a set of contour lines with circles as

trees and rectangles as buildings (Lange 2001). With increasing capabilities of computer

graphics and the availability of powerful personal computers, highly realistic 3D landscape

visualizations are fast becoming the norm in landscape assessment and planning. Although

estimations of the current number of users are not reported, it can be said that more and more

professionals in corporate and government agencies as well as the public sector are relying on

3D photorealistic landscape visualizations in their work (Sheppard 2001). Approximately

91% of the participants in a large study on the utilization of 3D geovisualizations in landscape

planning in Germany confirmed that they integrate 3D landscape visualization in their daily

work and expect to reap even more benefits from its utilization in the future (Paar 2005).

In the last decade there has been an increase in the number of works applying landscape

visualizations for environmental monitoring and assessment (Bishop and Lange 2005).

Landscape models of various levels of realism assisted in the preservation of biodiversity

(Hardin et al. 2005; Hehl-Lange 2001), the monitoring of climate change (Dockerty et al.

2005; Nicholson-Cole 2005; Sheppard 2005), supported sustainable forest management (Bell

2001) or assisted in the management of natural disasters (Brodlie et al. 2005; Salter et al.

2005).

One of the largest ongoing projects utilizing photorealistic geovisualization for environmental

monitoring is the project SERVIR (Hardin et al. 2005). The project involves geovisualization

of Mesoamerica (Central American countries and part of Mexico) and is undertaken with

NASA support. This small percentage of the world contains approximately 8% of the planet’s

biodiversity. NASA and the SERVIR partners such as Oak Ridge National Laboratory,

USAID, various USA-based Universities, the World Bank, and others are developing

interactive geovisualizations of the region in order to monitor and understand various factors

affecting its ecological integrity. The occurrence of natural disasters such as volcanic

eruptions, hurricanes, earthquakes, and landslides, as well as human-induced changes to this

rain forest region, severely influence ecological integrity and endanger this unique pool of

biodiversity on Earth. Thus, there is a global and regional interest in the utilization of state of

the art scientific methods for the data analysis and geovisualization of the region at various

scales. Combining 15 m resolution LANDSAT 7 and 1 m IKONOS imagery with a DEM and

adding vector data and linking the results to the World Wide Web is a highlight of user-

centered geovisualization (MacEachren 1994). Such communication media allow users


(researchers, decision makers, educators, students, non-government organizations) to display

any data of interest for a particular area and control the way that they view it (static,

interactive, animated).

Visualization of the landscape has assisted in the preservation of endangered species. For

example, in order to investigate feeding habits and paths toward the feeding areas of bats,

amphibians and the green woodpecker in the watershed of Lake-Laurez (Switzerland) a DEM

of the area was combined with LANDSAT TM, ortophotographs and digital topographic

maps to create non- and photorealistic landscapes (Hehl-Lange 2001). After the

geovisualizations were released, the visual form of data presentation positively encouraged

public participation in the issues of the biodiversity preservation.

Common in the works of Nicholson-Cole (2005), Dockerty et al. (2005) and Sheppard (2005)

is the use of geovisualization for increasing public awareness of climate change. Dockerty et

al. (2005) demonstrated in detail how to generate a GIS-based visualization of a rural

landscape to present changes to the landscape caused by climate change. It is not only the

direct influence of climate change on the landscape, but also indirect factors such as changes

in hydrology or fluvial geomorphology that can be visualized and communicated to the public

(Brandt and Jiang 2004). Nicholson-Cole (2005) suggested that the visualization of climate

change could establish a link between the abstraction of the topic and everyday experience

making the people aware of its importance. Sheppard (2005) agreed with this statement and

further explored the benefits as well as the potential risks of using visualizations to induce

behavioural changes and prompt people to not only be aware but to take action regarding

climate change. The author concluded that photorealism, the existence of relevant local and

recognisable details, and the demonstration of future consequences among others will reach

the emotional side of the viewers and have a greater potential to trigger an action when

compared to facts alone. Among the potential risks, the author singled out the risk of biased

responses, disbelief, confusion and overkill or even increase of the acceptance of the climate

change. Hence, the need to adhere to a set of ethical standards when creating

geovisualizations for public outreach and communication.2

2 The ability to produce untrue visuals in geography has been recognized for some time, and in particular, the common knowledge of how cultural and world views affect individual societies’ map production is known and other political motivations are clear in the maps of various propaganda efforts throughout the centuries. Ethics in geovisualization generally follow those exhorted by Monominer in his book, "How to Lie with Maps" which is a tenet of cartography education (Monominer 1991).


Sustainable forest management utilizes landscape visualizations for forest planning and

design as well as the communication of changes to the general public (Bell 2001). The

visualization of forest landscapes is multi-faceted (Salter et al. 2005). The challenge is not

only visualization of large data sets with specific attributes at various levels of detail and

extent of realism, but also to balance economical and environmental requirements in

conveying complex short and long term changes in the forest landscape (Salter et al. 2005).

The evaluation of existing resources and the impacts of proposed forest operations are among

the main objectives of forest landscape visualization (Honjo and Lim 2001). In most cases,

the focus is not on individual trees but on the growth and management of the forest stands

(e.g. groups of similar trees of similar structure and age) (Honjo and Lim 2001). It is not only

man-made changes such as planting, thinning and harvesting (Honjo and Lim 2001) but also

changes induced by natural factors such as forest fires (Ahrens et al. 1997) or forest blow-

downs (e.g. uprooting of a large number of trees due to strong winds) (Orland 2005) are

visualized. Forest landscape visualization simultaneously provides justification of various

management scenarios for forest managers as well as helps the general public to concentrate

not only on short-term changes (e.g. highly controversial “clear-cut” practices) but also to

examine how long-term impact of the current policies can affect the visual landscape (Orland

2005).

2.4.2. Geovisualization in urban planning and development

The utilization of photorealistic geovisualization in urban planning and development can be

coarsely divided into four main categories: planning and design, infrastructure and facility

services, commercial sector and marketing, and finally promotion and learning (Shiode 2000).

Anything from road construction, emergency planning, traffic control, determination of the

optimal placement of pipes, cables or wireless stations is covered by 3D visualizations of

urban environments (Lee et al. 2003). Based on terrestrial, panoramic, aerial, ranging or

satellite images, urban models (visualizations) of various degrees of reality can be developed:

from low-detailed 2D ortho-photographic, panoramic image-based models, prismatic block

models that combine 2D building footprint with airborne survey data, and block models with

image-based texture mapping to the highest full volumetric models (Shiode 2000).

Photorealistic virtual city models such as Virtual Los Angeles, San Francisco, Atlanta, and

Tokyo just to name a few of the 60 large scale urban models being currently developed world-

wide (Pietsch et al. 2005) are based on GIS data such as LIDAR, 3D Doppler data and aerial

photography. The aforementioned are just examples of things to come in the future of urban


development and planning (Ribarsky 2005). 3D models with the addition of animated views

such as walk- and/or fly-through usually do not require special training of the participant

viewers / users prior to their use (Al-Kodmany 2000). However, in some cases it is possible

for community participants to receive basic training in the software that enables them to

rearrange the proposed model according to their wishes (e.g. arrangement of schools,

apartment buildings, townhouses et) (Al-Kodmany 2000). Virtual and augmented reality tools

in urban planning enable highly engaging and interactive environments when appropriate

(Levy 1995). Although more expensive, the immersive models have an advantage over

animated, interactive 3D models: they enable group participation and interaction. However,

one of the challenges to be solved is recording the feedback of the participants (e.g. their

thoughts, emotions and preferences) in such an environment (Al-Kodmany 2001).

Hypermedia and Internet technologies as in other areas are shaping urban development and

planning (Al-Kodmany 1999). The release of the Google Earth project gives a new tool for

the integration of GIS, remote sensing, and visualization in urban design and planning (Butler

2006; Pietsch et al. 2005).

2.4.3. Other applications of geovisualization

Geovisualization also helps in various industries, for example Akzo Nobel Saltz B.V., a

mining company, used 3D visualization to improve various aspects of its mining operations

(Jagt et al. 2003). Using 3D geovisualizations, they increased the knowledge of the mining

site and improved the safety of the cavity by visualizing potentially dangerous scenarios. In

addition, the company utilized 3D visualizations as an effective communication tool

internally, in multidisciplinary teams during the planning activities and externally, to

communicate the effects of their mining activities on the environment to the stakeholders.

Shell, one of the world leaders in oil supply, has established a series of scientific models that

are able to predict and visualize various emergency scenarios following intentional (e.g.

terrorist attack) or unintentional release of hydrocarbons in the air (Shell 2006).

The Department of Homeland Security of the United States of America is using 3D

geovisualization data for examination of various “what if” scenarios, emergency management,

contingency and event planning (EON 2006). Visualization of planned events such as a visit

of the President enables detailed examination of terrain and buildings in the 3D environment

in order to establish, for example, line of sight, sniper positions and escape routes.


Visualisations of unplanned events such as terrorist attacks are also supported by the

utilization of 3D geovisualization (EON 2006). A larger GIS-based study involving

geovisualization was performed after September 11, 2001 in New York (Naphtali and

Naphtali 2003). The New York GIS community and public officials agreed that GIS and

geovisualization are valuable tools for timely response as well as data transfer and easy

communication among different teams involved in the emergency response (Naphtali and

Naphtali 2003).

Geovisualization is also used as a platform for data analysis related to various natural

disasters (Stern et al. 2006). Since, in these cases, data analysis and ‘what if’ scenario

development include various combinations of geological, hydrological, terrain, human and

other factors, the utilization of geovisualization to model and visualize these complex systems

is on the rise. For example, emergency efforts during hurricane Katrina were supported by

geovisualization (Nourbakhsh and Sargent 2006). Geovisualization was used to visualize the

potential impact of the hurricane as well as to improve communication among various

emergency response teams (Nourbakhsh and Sargent 2006). The Pacific Disaster Center

(PDC) is developing models that will enable it to predict and visualize various natural

disasters relevant to that region (PDC 2006). For example, dynamic visualizations of tsunami

travel time helps to communicate the potential danger to community officials and to increase

their preparedness level (PDC 2006). Real-time visualizations of costal flooding or lava flows

are also undertaken by PCD with similar objectives. On the other side of the world, in

Switzerland natural disasters in Alpine areas are common e.g., land slides, snow avalanches,

etc. The impact of these is also analysed and communicated to the public using

geovisualization (Stern et al. 2006). Consequently, it is clear that the utilization of

geovisualization for evaluation of natural disasters and emergency preparedness is becoming

a general trend across the world.

2.5. PRESENT CHALLENGES IN PHOTOREALISTIC

GEOVISUALIZATION

Despite its successful application across various geomatics and Earth Science fields,

photorealistic visualization technology is still facing a major challenge: There is a large gap

between contemporary 3D visualization technology and the current geomatics technology


used in geovisualization despite their common reliance on computer graphics, ability to

handle large data sets and approximately same number of decades of development.

Although considerable improvements occurred in the last decade, even state of the art

geomatics software are still producing 3D visualizations of low visual quality compared to

professional 3D visualization software (e.g. 3ds Max, Maya and Vue 5 Infinite). Why? There

are a number of factors including the perceived needs of the geomatics user community and

functionality demands. In addition there is the extended learning curve required for

development of high-quality geovisualizations. Moreover, geomatics software companies do

not have the resources to build effective photorealistic geovisualization modules for their

software from scratch (short of a corporate merger or takeover) – that would take decades and

even then geomatics companies would still lag decades behind state of the art visualization

companies. The only company currently in existence that has both technologies is Autodesk

Inc. who owns 3ds Max and Maya in addition to AutoDesk Map and AutoCad among others.

Within that milieu, potential cross-fertilization of geovisualization functionality into their GIS

software is possible but has not yet been realized. This is most likely due to the market trends

in geomatics as previously mentioned. By analogy, the market driven decisions of profit

maximization companies did keep spatial statistics out of mainstream GIS packages like

ArcGIS for decades. Rowlingson and Diggle (1993) commented clearly on the lack of

integration of spatial statistics in GIS and this was echoed prior to and following that time by

many others. However, it was not until the release of ArcGIS 9.0 in 2005 that a few point

pattern analytical routines were incorporated into a GIS toolbox. Even then, the functionality

was basic compared to the advances in spatial statistical analyses in the preceding decades.

Likewise, and now returning to photorealistic geovisualization’s connections with

contemporary geomatics, the inherent complexity of the technology necessary for modeling

and rendering photorealistic scenes is beyond a bottom-up solution for a company focusing on

GIS development. For the production of high quality photorealistic visualizations, it is

necessary to apply multi-resolution terrain models and dynamic terrain texturing and these are

current challenges facing geomatics technology developers (Doellner 2004). While the

strength of geomatics technology is in integrating and analyzing data from multiple sources,

at present this technology utilizes the low-level graphic systems such as OpenGL. These

graphic systems do not support the computational algorithms required for rendering complex

photorealistic scenes where usually multi-pass, advanced rendering technology is required.

Further details on rendering are given in Appendix 1.


The major advantage of 3D visualization technology is in its sophisticated rendering

capabilities that enable modeling and rendering of highly photorealistic scenes. On the other

hand, when working with 3D technology one has to be aware of the special nature of the

georeferenced data. This data has an exact location in the real world based on various

geographic / geodetic / projected coordinates system. In addition, there is a lack of data

connectivity between current geomatics technologies and 3D technology because most state

of the art photorealistic visualization technologies do not employ the data formats or

coordinate systems utilized in geomatics. For example, a land parcel in geomatics software is

a polygon, while in 3D software the same parcel is represented by four independent lines (e.g.

their movement is not necessarily mutually coordinated, but has to be specified as so if

desired).

It is evident that both technology platforms could benefit from an integrated solution that will

utilize the advantages of geomatics and 3D visualization approaches. Since the

implementation of advanced level rendering in the geomatics platform at present is

technically difficult and in addition, not supported by the software developers, this present

research on integration is mostly concentrated on improving the shortcomings of 3D

visualization software when handling georeferenced data. Data exchange between platforms is

a major challenge. Unavoidably, this step can result in the loss of data precision and accuracy.

Accuracy is the degree to which a visualization matches the actual values it represents, while

precision refers to the level of measurement and exactness of a visualization (Wallace and van

den Heuvel 2005). At present, there is no general integration strategy within geomatics

software that is applicable for all required photorealistic landscape visualizations.

2.6. CONCLUSIONS

Rapid advancements in information technology have changed the way we collect, analyse,

display, interact with and communicate georeferenced data. While spatial statistics and other

traditional approaches (e.g. analytical modeling) are certainly helpful, their effectiveness and

efficiency are limited by the rate of data gathering (Jiang and Li 2005). Termed

geovisualization, a multidisciplinary approach merging computer graphics, exploratory data

analysis, image analysis, and GIS is increasingly replacing and/or complementing traditional


methods. With its roots dating back to the 1960s (Nakamae and Tadamura 1995) and

intensive development since the 1980s (Bishop and Lange 2005) geovisualization is

becoming a corner-stone of contemporary knowledge discovery and the decision making

process in geomatic science (Jiang and Li 2005). This work has briefly assessed contemporary

photorealistic landscape visualization and its impact on contemporary geomatics and defined

the gap between contemporary geomatics and 3D photorealistic visualization technology.

Advanced modeling, lighting, and texturing tools as well as sophisticated rendering methods

(e.g. ray tracing, radiosity) have enabled the creation of models with an increased extent of

realism (Nakamae and Tadamura 1995). More details are given in Appendix 1. However, one

challenge remains, the integration of these state of the art advancements in 3D visualization

technology with geovisualization technology. This has proven to be the most important

obstacle to the implementation of photorealistic visualization methods in geomatic practice

for those practitioners who need a solution now.

Despite the above challenges, photorealistic geovisualization has shown an impact in various

fields such as the preservation of ecological integrity, communication of climate change and

urban planning. It is expected that in the future, the “traditional” applications of

geovisualization such as those in landscape and urban development and planning and

environmental planning will continue to grow while new ones will continue to emerge (e.g.

data analysis and subsequent development of emergency preparedness scenarios in case of

natural disasters, terrorist attacks, etc.).


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Chapter 3

Integration of 3D Visualization and GIS for Monitoring and Communication

of Ecological Integrity in Canada’s National Parks

Chapter 3 Integration of 3D Visualization and GIS 29

3. ABSTRACT

In an effort to evaluate and protect the ecological integrity of Canada's National Parks and

increase public awareness of the subject, several government agencies have joined forces

through the Government Related Initiatives Programme (GRIP). One of their objectives is to

create 3D photorealistic visualizations of Canada’s National Parks and use them as data

exploration and communication tools. Towards that end, the first step in this research is to

establish a workflow necessary to achieve the objectives and to assess how far photorealism

can be taken when utilizing geomatics datasets.

In order to generate highly photorealistic 3D geovisualizations, an integrated approach

consisting of georeferenced data, geomatics and state of the art 3D visualization technology is

developed in this research. As a basis, digital elevation models (DEM) with various levels of

detail are used. Different 3D terrain modeling and texturing techniques are evaluated. Multi-

spectral and panchromatic satellite imagery with different geometric and radiometric

properties are utilized to increase the level of realism.

In the second part of the study, these photorealistic visualizations are used for public outreach.

The objective was to increase awareness, understanding and knowledge about Canada’s

National Parks and the need for the preservation of their ecological integrity.

3.1. INTRODUCTION

As the second largest country in the world, Canada has large uninhabited or rarely visited

areas. However, these areas are not as “untouched” as they seem. Climate change may

dramatically alter the 42 Canadian National Parks and National Park Reserves (NPC 2003).

Among these, Canada’s Arctic National Parks are the most vulnerable (Suzuki 2000).

Northern climate change is marked by glacier retreat, the movement of permafrost boundaries

northwards, and rapid changes in arctic ecosystems (Suzuki 2000). Reducing Canada’s overall

vulnerability to climate change is one of the major objectives of the newly introduced

Government Related Initiatives Program (GRIP) (Wong and Chilar 2004).


The work reported here is a part of the project undertaken within the scope of GRIP. Parks

Canada, the Canada Centre for Remote Sensing and the University of Ottawa joined forces

with two main objectives:

1. Application of new tools for the assessment and monitoring of the ecological integrity

of Canada’s National Parks using Earth Observation data (EO).

2. Application of new tools for effective communication of the results to the general

pubic.

Ecological Integrity as defined by the Canada National Parks Act is “…a condition that is

determined to be characteristic of its natural region and likely to persist, including abiotic

components and the composition and abundance of native species and biological

communities, rates of change and supporting processes (NPC 2003).” For various parks the

stressor and ecosystem indicators of interest for assessment and monitoring are different (e.g.

Arctic and prairies ecosystems differ greatly).

While contemporary data acquisition technology provides access to a large amount of

information that could be potentially used for the preservation of ecological integrity, the

complexity of the data is such that making any inferences using traditional data exploration

methods is highly limited. There is an indication, however, that novel visual data exploration

and presentation methods could be beneficial for the assessment and monitoring of ecological

integrity.

Visualization of endangered areas can assist us in data exploration by providing cognitive

support (e.g. enhanced recognition, allowing monitoring of a large number of potential events

or recognition of patterns) (Tory and Moeller 2004). This cognitive support is a basis for the

engagement of the audience in the decision making process.

3.1.1. Objectives of the study

Canada’s 42 National Parks and National Park Reserves cover 224,470 km2 in total (NPC

2005). This 2% of Canada’s land coverage does not include numerous provincial parks. Ten

out of 42 National Parks were selected for the GRIP project (Figure 2). The first to be

assessed using the 3D photorealistic modeling approach were Auyuittuq, La Mauricie and

Nahanni National Parks. Due to the projected influence of climate change, assessment of the

ecological integrity of these three parks was of the highest priority. Effective communication


of the park environment and potential changes within the park boundary are fundamental to

understanding the mechanisms of change and in soliciting the engagement of the public.

In the first part of this work the objective was to establish an integrated approach for using

geospatial data, geomatics technology and scientific visualization to create 3D photorealistic

visualizations of Canada’s National Park (NP) environment. The sub-objective was the

exploration of various methods of 3D, terrain modeling and texturing to achieve highly

photorealistic visualization of mountain terrains with snow and glacier structures as well as

vegetation coverage. To achieve this objective we had to develop a new, integrated approach

that combines Earth observation data, geomatics science and contemporary 3D visualization

technology.

The second objective was to use the resulting photorealistic visualizations as a

communication tool for public outreach. That is to say, we needed to assess whether the

visualizations were seen as important and useful by the non-project and project individuals.

Our intended audience was a broad range of geomatics professionals, local high-school

students, and the general public. The hypothesis was that presenting highly photorealistic

visualizations of the areas to the general public will have two effects. First, photorealistic

visualizations of NPs will engage the general public and induce an interest in preserving

ecological integrity in the parks. Second, with an increased awareness of these remote areas,

the general public will have more understanding and support for the actions taken on the

preservation of ecological integrity.


Figure 2 Proposed National Parks of Canada for the Government Related Initiatives Program (GRIP) Project


3.2. PHOTOREALISTIC GEOVISUALIZATIONS CHALLENGE

Despite considerable improvements and increased research efforts, geomaticians are still

facing a challenge in the area of visualization since there is a big gap in the visualization

quality and capabilities between current geomatics software and professional 3D visualization

technology (Appleton et al. 2002). The work presented here intends to overcome this gap by

development and implementation of an integrated solution leveraging on the advantages of

both technologies. Even state of the art geomatics software (e.g. ArcGIS, Mapinfo) offers

limited capabilities when 3D photorealistic visualization tools are in question. Not only is the

number of available tools limited but also the quality of the generated visualization is inferior

to those provided by professional 3D modeling/ animation software (Table 1).

Table 1 A comparison of basic building elements of animation

Professional 3D Geomatics

Geometry complex simple

Lighting complex simple

Camera complex simple

Shading advanced basic (flat)

Rendering advanced basic

In terms of technology and complexity, one can estimate that current geovisualization

applications are at least 10 years behind professional 3D applications3, leaving geomaticians

to evaluate different state of the art visualization software and find a way to integrate them

with powerful GIS and remote sensing technologies. Since there is no “universal landscape

visualization solution”, it is inevitable that geomaticians will need to be familiar with at least

several visualization packages to compensate for various trade-offs in level of detail or

interactivity (Appleton et al. 2002).

3 This observation comes from a review of feature films, considering only high-budget “A” films from the last thirty years, industry design, architecture and comparing special effects with those produced by geomatics software over the same time period with regard to the geographic representations of land and cityscapes.


Walsch et al. (2003) visualized the influence of geomorphic processes on the alpine terrain

ecotone in Glacier National Park, Montana, USA. The authors claimed that the combination

of GIS, remote sensing and scientific visualization for assessment of the effects of

geomorphic processes and patterns at alpine treeline improved alpine slope models and

enabled the determination of alpine landscape attributes within a spatio-temporal context.

They concluded that the use of visualization (e.g. image flyovers, rotations, and animations)

of cellular automata can lead to new hypotheses and insights. The authors suggested that the

use of visualization in the future could potentially result in multiscale models of vegetation

patterns in alpine terrain that include topography, geology and climate changes.

High resolution photorealistic visualization of Mount Everest was another attempt to visualize

alpine terrain (Gruen and Murai 2002) by combining remote sensing imagery and scientific

visualization. The heavy pressure that tourism and lodging place on the UNESCO protected

area of Sagarmatha (i.e. an area with dramatic mountains, glaciers and steep valleys,

dominated by Mount Everest) has initiated action for the preservation of ecological integrity

in this unique area. As such, the first task was recording the current situation by producing

high-resolution visualizations of the area. The authors emphasized the challenge in the

texturing of steep faces of 3D mountain models since the pixel information from aerial

imagery is only coarsely available. In addition, they acknowledged the importance of shadows

for realistic model appearance and suggested careful lighting of shadow regions.

These studies provide helpful insights for the design of an integrated approach to

photorealistic visualizations. First, the similarity of the objectives (e.g. protection of

ecological integrity of a protected area) and the tools utilized in this work provided a good

basis for the development of an integrated workflow. We have carefully examined the

problems and challenges the above authors experienced, such as coarse aerial imagery or

shadows mode lining, and considered new strategies such as pansharpening to address them.

Why use photorealistic visualizations as a public outreach tool?

It has been shown that a high level of abstraction and complex scientific descriptions are not

efficient tools when there are non-experts in the audience (Tress and Tress 2003). On the

other hand, there are suggestions that photorealistic visualization during the decision making

process can improve communication among the interest groups, professionals and non-

experts, and thus increase the efficiency of the process (Appleton and Lovett 2005; Tress and


Tress 2003) since the audience appears to readily understand high quality visualizations

(Hehl-Lange 2001). The more closely the visualization resembles the real world and our

perception of it, the more likely the audience will establish a connection with the project and

claim ownership (Appleton and Lovett 2005). A comparison (Table 2) between the original

scene (nature) and low and high realism scenes reveals that photorealistic presentation scores

closer to the original scene as compared to a geometric presentation.

Table 2 Comparison: Geometric, photorealistic and original scene (Angsuesser and Kumke 2001)

Criterion Geometric Presentation

Photorealistic Presentation

Nature (Original scene)

Origin artificial artificial natural Level of details low high infinite Generalisation degree e.g. abstraction degree high low none

Individualisation degree low high only individuals Time dependence low high complete

Information perception little (selected) information in a

short time

much information over a long time

infinity of information in infinity of time

Photorealistic visualizations at a small scale with a high level of detail and realistic

appearance enable easy identification of an area by non-expert stakeholders and require little

additional interpretation to convey the message (Tress and Tress 2003). Despite differences in

individual responses to visuals, to understand how visualizations improve our communication

we have to understand some general mechanisms by which visuals support human cognition

(Tory and Moeller 2004):

1. Visualisations increase the resources of our perception system. Visualized data are

processed faster with less demand on the human memory and perceptual system.

2. Visualizations enhance recognition. The process of information recognition is easer

and faster compared to the process of information recall.

3. Visualizations support perceptual monitoring. Visualization supports the use of pre-

attentive visual characteristics (e.g. color difference, motion, form) to attract human

attention and allows monitoring of a large number of potential events.

In addition, visualizations can present large amounts of data in a form that is easy to access


and manipulate by the end user. The user can selectively omit, aggregate, rearrange or

emphasize the data in order to recognize higher levels of patterns in the data

A study from Denmark (Tress and Tress 2003) provides a representative example of the

effectiveness of photorealistic visualization in the communication of ecological integrity.

During a debate on the future landscape development of a small rural area in Denmark, the

stakeholders were presented with photorealistic visualizations of four scenarios (i.e. the

preservation of ecological integrity, industrial farming, recreation/tourism, and residential

expansion) (Tress and Tress 2003). Although only one scale and perspective were offered,

photorealistic scenes enabled non-expert stakeholders to easily identify known landmarks and

key features in the projected scenarios. Thus, only a small amount of clarification was

necessary from the experts in the field. The stakeholders were able to take an active role in the

decision making process with less misinterpretation and confusion. These results are in

accordance with the finding of Appleton and Lovett (2005) who stated that non-experts in the

audience will likely rely on visualisations during their decision making. Furthermore, this

paper indicates that photorealistic visualization can be an effective tool for the communication

of ecological integrity.

3.3. VISUALIZATION CASE STUDIES: NATIONAL PARKS IN

CANADA

Before effective communication of ecological integrity within regions can take place the

communication tools must be furnished. As such, an integrated approach and workflow for

using geospatial data, geomatics technology and scientific visualization to create 3D

photorealistic visualizations of Canada’s National Parks (NP) environments is a prerequisite

to the development of visualizations for communicating ecological integrity. The

development of this workflow concentrates on using geospatial datasets in three National

Parks, namely Auyuittuq, Nahanni and La Mauricie. Therein, the exploration of various

methods of 3D, terrain modeling and texturing are provided in order to achieve highly

photorealistic visualization of mountain terrains with snow and glacier structures as well as

vegetation coverage. The result of this exercise is a new, integrated approach that combines

EO data, geomatics science and contemporary 3D visualization technology.


3.3.1. Auyuittuq National Park, Nunavut

The area of “the land that never melts” (the meaning of the word Auyuittuq in Inuktitut

language) covers 19,098 km2 and was established in 1976 as a National Park (Figure 3).

Located in the eastern Arctic on southern Baffin Island, the park is described by National

Parks Canada as land “where sweeping glaciers and polar ice and sea meet jagged granite

mountains” (NPC 2005).


Figure 3 Auyuittuq National Park, Buffin Island, Nunavut, as shown in a Landsat 7 ETM+ scene from Geobase (www.geobase.ca)


Auyuittuq is an area containing (NPC 2005):

a) Moraines - ridges formed by rock debris that were transported by moving ice and

deposited at the margins of glaciers.

b) Cirques - bowl-like hollows carved out by glaciers in the tops of mountains. Some

cirques are ice-free, while others are still occupied by glaciers.

c) Sand Deposits - created by the erosive actions of ice, wind, and water

d) Perched Boulders - large boulders sitting atop small rocks, deposited by retreating

glaciers

e) Talus or Scree Slopes - cone-shaped accumulations of rocks pried loose from steep

glacier-scoured valley walls by frost action.

The plethora of glacial features makes this Arctic park of particular interest for research on

the preservation of its ecological integrity in light of continuing climate changes. The effects

of climate change can be observed from satellite imagery taken in 1991 and 2000. Since this

is a remote area, it is essential to effectively communicate these changes to the general public.

Towards this end, the first step is to address how photorealistic geovisualizations of such

phenomena and environments can be achieved through integration with geomatics.

3.3.2. Nahanni National Park Reserve, Northwest Territories

Located in the Northwest Territories, Nahanni (Figure 4) was established as a National Park

Reserve in 1972 and declared a UNESCO heritage site in 1978. Four great canyons of the

South Nahanni River are surrounded by alpine tundra, mountain ranges and forests in this area

of the Mackenzie Mountains.

The biggest challenge to the ecological integrity of the Nahanni National Park Reserve is the

fact that it covers only 1/7 of the total area of the South Nahanni River watershed. Because

the park covers the lower portion of the watershed, upstream activities such as mining, oil and

gas exploration in addition to climate change can influence the ecological integrity of the

park. In the light of the pending discussion on the expansion of this national park, public

outreach is especially important.


The area covered by Nahanni National Park Reserve in its present form is the result of three

major geological events in the past (NPC 2005). First, 200-500 million years ago the area was

covered by a sea. Up to 6,000 m of sedimentary rocks consisting of sandstone, mudstone and

shale were deposited in the area during this period. Approximately 100 million years ago

mountain-building started in the area. The areas in the eastern part of the national park are still

rising (NPC 2005). Between 2 and 8 million years ago began a third geological era for the

Nahanni region: the glacial era. Short periods of glacial erosion (the last one ending 10,000

years ago) were combined with long periods of river erosion.

Extensive glaciers, hot springs, tufa mounds (i.e. geological features created by the

precipitation of dissolved minerals, primarily calcium carbonate, from thermal spring water)

and the four canyons of the South Nahanni River are not the only areas of research interest in

the area (NPC 2005). In comparison to Auyuittuq, the National Park Reserve is rich in flora

and fauna. Among 700 plants, boreal forest and alpine tundra species are dominant. Grizzly

bears, caribou and moose share the area with wolves, small mammals and over 170 bird

species. As such, there are numerous aspects of the environment within Nahanni NP that can

be potentially communicated to stakeholders using photorealistic geovisualizations, if such

visualizations can be formed by integrating geospatial data and state of the art visualization

technologies.


Figure 4 Nahanni National Park, NWT


3.3.3. La Mauricie National Park, Québec

Created in 1970, this national park (Figure 5) protects the Laurentian area and the

southernmost part of the Canadian Shield. Almost 955 million years ago the Canadian Shield

was as high as today’s Himalayas (NPC 2005). However, a number of erosion events, the last

one being the Wisconsinan glaciation of 25,000-12,000 years ago, reduced the height of the

Shield considerably and left numerous lakes. After the retreat of the Wisconsinan glacier the

lowest part of La Maurice NP was covered by a marine incursion for approximately 2,000

years (NPC 2005).

The preservation of ecological integrity of this National Park is a priority due to the

abundance of flora and fauna in this “land of lakes and brooks”. Among 400 plant species

inhabiting the NP, 70 are considered rare (NPC 2005). Approximately 40 species of birds of

the total of 180 depend on the park’s aquatic environment for survival (NPC 2005). Numerous

geological events shaped today’s topography, soil types and altitudes that host more than 30

tree species, covering approximately 93% of the total 592 km2 of the park’s area. Black and

white spruce, balsam fir and white birch are the dominant tree species. However, the

surrounding area of the Park is being defragmented by intensive, clear cut forest management,

endangering the species in the surrounding areas and the park itself. In addition, intensive

tourism also has a negative impact. For instance, wolves are now only present during the

winter when the number of visitors is low.


Figure 5 La Mauricie NP, Québec as shown in a Landsat ETM+ mosaic based on data from Geobase (www.geobase.ca)


3.3.4. Key features to be visualized

Since the output of the research objective is to increase public awareness and interest in the

National Parks as well as to communicate the need for the preservation of their ecological

integrity, representative areas were selected for visualization, such as the glacier features of

Auyuittuq NP affected by climate change, the clear-cut forest areas of La Mauricie NP and

the topography and overall landscape of Nahanni NP. In Table 3, the key features to be

visualized, their relevance and the EO data used for the visualizations are outlined.

Table 3 Key features to be visualized in order to develop a workflow integrating photorealistic geovisualization with contemporary geomatics. FEATURE RELEVANCE DATA

Auyuittuq National Park Akshayuk

Pass 97 km long canyon created by the movement of continental glaciers during the last ice age.

Crater lake Marks the limit of the last advance of the glaciers.

Summit lake Located at the highest point of Akshavuk Pass.

Mount Tohr The longest uninterrupted cliff face in the world.

LANDSAT (30 and 15m), IKONOS (1m)

Nahanni National Park Reserve

1,2 Topography and landscape of another representative region containing highlands and low lands.

LANDSAT (30 and 15m), QuickBird(60 cm)

La Mauricie National Park

1 Clear-cut forest areas. LANDSAT (30 and 15m), SPOT (5m)


3.4. AN INTEGRATED APPROACH TO PHOTOREALISTIC

LANDSCAPE VISUALISATION

The methodology used to integrate photorealism with contemporary geomatics that is

developed in this work can be broken down into five major steps (Figure 23): identification of

the scientific concept, model building, animation, rendering and postproduction.

Figure 6 Workflow for the integration of GIS and scientific visualization

Similar to other scientific approaches, the photorealistic modeling process starts with the

problem definition (Figure 6A). At this stage an outline of the modeling procedure is

envisioned. Data collection and evaluation is the next logical step. The first task in the

modeling step (Figure 6B) is data pre-processing. In most cases, the available data requires

pre-processing within GIS and RS software to achieve the desired coverage, resolution or

quality of presentation. At this stage the scientific concept is transformed into key-frames.

Texturing, shading and terrain modeling are then performed. There is no general modeling

procedure. Each concept is unique in the way the scientist will approach the required

visualization. In addition, there is not a single unified software applicable to all models. At

present, the situation is similar to the animation step (Figure 6C). Various commercial

software packages are available to produce dynamic visualizations (eg. Vue 5 Infinite,

Terragen, 3ds Max) with various compatibility levels to GIS software. Thus, it is up to the

geomatician to select the most efficient software combination for the given objective. Camera,


light positioning and incorporation of lighting models (direct, indirect, diffuse, caustic effects

etc.) in the visualization software are the key elements to achieve a high extent of realism in

the final product. Subsequently, the selection of a rendering algorithm (Figure 6D) is based on

a good balance between the rendering speed and the level of detail required for the

photorealistic visualization. A suitable file compression utility is necessary to reduce the file

size of the final video. In the final post-production step (Figure 6E), various other effects such

as text and/or music are added to the photorealistic visualization to increase the information

level of the presentation.

3.4.1. Data Collection and Evaluation

For this work a wide variety of primary geographic data in digital format were obtained from

many diverse sources. For Auyuittuq NP, Nahanni and La Maurice NPs, DEM and

LANDSAT imagery were obtained from GeoBase (www.geobase.ca). GeoBase is a

geomatics database established and maintained by the Canadian Council on Geomatics and

Natural Resources Canada that provides data at no cost and with no restrictions for the users.

For Auyuittuq NP, IKONOS imagery, LANDSAT TM 1991-07-12, Path/Row 17/13 and

LANDSAT ETM 2000-08-13 Path/Row 17/13 were provided by GeoBase. For La Mauricie

NP, Spot imagery was acquired from EADS MATRA Systems and Information and vector

data from Parks Canada. For Nahanni NP, LANDSAT and Quickbird imagery were obtained

from GeoBase and Digital Globe, respectively. The Nahanni DEM was produced prior to

visualization using the National Topographic Database (NTDB) and the ANUDEM 5.1

algorithm. La Mauricie visualizations were supported by the use of LANDSAT and SPOT

(Spot Image).

Imagery obtained from GeoBase was free of charge. Quickbird panchromatic and

multispectral imagery was purchased for CAN $ 6 per square kilometre (educational price for

archived imagery).

DEM data

Digital Elevation Models (DEM) are data files that contain the elevation of the Earth’s surface

(z-coordinate) over a specified area (x,y coordinates), usually at a fixed grid interval. The

intervals between each of the grid point are always referenced to a geographical coordinates

system (e.g. NAD83). It is called a model because a DEM is a generalized representation of


reality where reality is true elevation at a point. A DEM contains a resolution or cell size over

which elevation is an average of the elevation values observed within a region defined by the

raster cell. Since a DEM is a raster representation, its structure (an array of pixels) represents

a defined area of Earth (Figure 7).

Figure 7 An example of a DEM structure: a) raw data Auyuittuq NP, b) 400% zoomed, c) 800% zoomed

A DEM of the Canadian landmass is stored in GeoBase (www.geobase.ca) as the Canadian

Digital Elevation Data model (CDED). The North American Datum 1983 (NAD83) is used as

the reference system. Elevations are orthometric and expressed in reference to Mean Sea

Level (Canadian Vertical Geodetic Datum 1928 (CVGD28)) (CTI 2005). Currently available

data, extracted from the hypsographic and hydrographic elements of the National

Topographic Data Base, is an average scale of 1:10 000 to 1:250 000 (CTI 2005) which is

denoted as GeoBase Level 1. At present only the 1:250 000 CDED provides complete

seamless coverage of the entire Canadian landmass (CTI 2005). This scale is sufficient for

visualization purposes when only qualitative visual data analysis is performed. For the 1:250

000 scale, the grid spacing is based on geographic coordinates at a maximum 3, 6 and 12 arc

seconds depending on latitude.

The concept of geographically referenced grids deserves closer attention because when

imported into the ArcGIS working environment (ArcGIS, ESRI) as raw data, grids registered

using arc seconds must be reregistered and reprojected for further use in ArcGIS workspace

since ArcGIS does not consistently recognize arc second units. Details of this process will be

explained in the data pre-processing section.


Four remote sensing datasets (e.g. LANDSAT, IKONOS, QuickBird, SPOT) were utilized in

the project to create geospecific photorealistic texture maps in accordance with the workflow

outlined in Figure 6b. These datasets represent two radiometric resolutions: 8 bit or 11 bit, and

five different spatial resolutions: 0.6 meters, 1 meter, 4 meters, 15 meters and 30 meters. The

combination of these data ensured the balance between large areas of the parks that were to be

visualized and also allow for a high level of detail required for fly through animations.

The radiometric resolution refers to the number of and width of spectral bands that is stored in

an image. In order to work efficiently with images (e.g. compressing and stretching) it is

important to understand bit depth. Computers work with binary data, which means that every

number has a value of 0 or 1. More complex numbers are represented by a sequence of binary

digits. For example, 2 bit data would result in 4 possible values: 00, 01, 10, and 11. Thus, 8

bit data can store 256 (28) possible values in each pixel or band, and 11 bit (211) allows 2048

possible values for each spectral band. More information can be extracted from 11 bit data but

some software does not support that format and it requires more disk storage space.

LANDSAT-ETM+ (LANDSAT 7 satellite), satellite images were obtained from the GeoBase

portal. LANDSAT ETM + data are collected from a nominal altitude of 705 kilometres in a

near-polar, near-circular, sun synchronous orbit at an inclination of 98.2º, imaging the same

183-km swath of the Earth’s surface every 16 days (CTI 2003). Radiometric resolution is 8

bit. That is to say, each band captures a given range of light such as the blue part of the

radiometric spectrum and the variation in this spectrum is reduced to a range of values from 0

to 255. Landsat ETM orthoimages are stored as raster data and consist of 9 spectral bands: a

panchromatic band with a pixel size of 15 m, 6 multispectral bands with a pixel size of 30 m.,

and 2 thermal infrared bands with 60 m ground resolution. These have been produced in

accordance with NAD83 (North American Datum of 1983) using the Universal Transverse

Mercator (UTM) projection (CTI 2003). Bands 1, 2 and 3 are used for producing true color

composite image. The panchromatic band 8 is used for a pansharpening process.

IKONOS data

IKONOS data are collected from a nominal altitude of 681 km in a sun synchronous orbit at

an inclination of 98.2º. The orbit time around the Earth is 98 min. Image swaths are 11 km at

nadir (the point in the sky with an inclination of -90°) and 13 km off-nadir. Image bands are

panchromatic, blue, red, green and NIR (near infra-red). Spatial resolution at the nadir is 0.82


meters panchromatic and 3.2 meters multispectral, and off- nadir resolutions are 1 and 4 m for

panchromatic and multispectral mode, respectively. The radiometric resolution for the

multispectral bands is 8 bit and for the panchromatic band is 11 bit. The 11 bit allows for

more detailed extraction in areas of low contrast and shadows. The temporal resolution is 3

days, which means an image of the same geographical space can be collected every 3 days.

QuickBird data

QuickBird imagery (Digital Globe) is the highest spatial resolution imagery commercially

available. This high spatial resolution was used to increase the resolution of the fly-through

3D photorealistic visualizations. The QuickBird satellite collects data at a nominal altitude of

450 km in a sun synchronous orbit at an inclination of 97.2o with an orbit time of 93.5

minutes. Swath width at and off-nadir is 16.5 km. The spatial resolution of the panchromatic

imagery is 61cm at nadir and 72 cm at 250 off-nadir, while the resolution of multispectral

imagery is 2.44 m at nadir and 2.88 m off-nadir. The temporal resolution is 1-3.5 days

depending on latitude. The radiometric resolution is 11 bit. Image bands are: panchromatic,

blue, red, green and NIR (near infra-red). A selected image included as many characteristics

of the Nahanni region as possible, such as rivers, mountains and topography. An important

factor for image selection was atmospheric quality. Images with less than 10% cloud coverage

or other atmospheric effects were selected since they can be used for visualization without

pre-processing. The cloud coverage and atmospheric effects such as haze, rain or snow affect

the accuracy of data retrieved from the satellite imagery (Sjoberg and Horn 1983). While the

presence of haze, rain and snow reduce contrast and thus affect the level of detail in a

photorealistic visualization, clouds appear to lay flat on the ground looking like snow

coverage (Hirtz et al. 1999).

SPOT data

Spot SYSTEM SCENE level 1A (SpotImage) was used for the determination of vegetation

distribution for visualization of La Mauricie NP. The Spot orbit is polar with an inclination of

98 degrees, circular, sun-synchronous and phased. The satellite has 832 km altitude and 14.19

revolutions per day with a period of 101 minutes. Cycle duration is 26 days with westward

drift between successive ground tracks of 2823 km. SPOT imagery had 4 channels and ground

location accuracy better than 350 m. Imaging swath is 60 km x 60 km. Geocoding tables

identification was EPSG 5.2 (European Petroleum Survey Group) and the geographic

horizontal coordinate system was WGS 84.


3.4.2. Modeling

Data pre-processing

Three major pre-processing steps were undertaken to obtain the data at the necessary level of

detail for the subsequent animations. The pre-processing steps were:

a) DEM data preparation

b) Production of LANDSAT composites

c) Data fusion (pan-sharpening)

Pre-processing of DEM within a GIS environment. The DEM pre-processing consists of

several steps as shown in Figure 8.

Figure 8 Cartographic model of DEM data pre-processing

The area covered for every downloaded raster file corresponds to half an NTS (National

Topographic System) map, which means that there are western and eastern parts to the

CDED1 for each NTS map. Therefore, there are multiple raster datasets. Because all of them

have the same spatial reference, pixel size and no differences in overlapping regions of

adjacent tiles, they can be merged into a single raster dataset that will be further used in the

visualizations. The process is presented in Figure 9 for the Auyuittuq NP.


Figure 9 DEM Pre-processing. a) 4 adjacent raster datasets b) merged raster dataset. Data source: www.geobase.ca, Scale 1: 50 000

Because the ArcGIS platform does not consistently support arc seconds, such units are

converted into decimal degrees in the second step of DEM data pre-processing prior to DEM-

data projection into UTM Zone 19. The polar geographic reference system considers the

globe to be an ideal sphere divided into 360 equal parts called degrees where each degree is

further subdivided into 60 minutes and these are subsequently composed of 60 seconds.

According to this representation, one arc second is the distance of latitude or longitude

traversed on the earth’s surface while traveling one second (1/3600th of a degree) (ESRI

2006). An arc second of longitude is equal to an arc second of latitude at the equator.

However, the closer to the poles one goes, the shorter the ‘real world’ distance that one arc

second longitude becomes in a cosine-based fashion due to convergence of longitude lines at

the poles. In comparison, the distance traveled in one arc second latitude remains almost

constant. Therefore, the easiest transformation to polar coordinates is around the equator.

There, one arc second of longitude corresponds to 1/60th of a nautical mile (1825 m) which

equals 30.87 m. This is regardless of whether one travels along latitude or longitude lines.

Toward the poles the transformation must consider the latitude of the point (e.g. 1 arc second

of longitude at 49o north latitude is equal to 30.87 × cos 49o = 20.25 m). As such, the

consequence of the different distances one unit of longitude represents at different latitudes is

that the resolution of an image varies in the x orientation for large regions like Nahanni NP


and this has to be taken into account when creating the DEM or when merging the Landsat

imagery.

Pre-processing LANDSAT Imagery

To obtain the desired information various composite images usually need to be created. The

results of various band combinations from Auyuittuq NP are presented in Figure 10.

Depending on the objective of the work, various composites were found to be useful. True

color composite represents the most realistic view of the earth’s surface, closely resembling

what is seen by the human eye. To create an image that is close to a photograph a true color

composite combines band 3 (visible red), band 2 (visible green), and band 1 (visible blue).

For generation of 3D photorealistic representation, the true color composite was the most

useful composite. On the other hand, if one’s purpose is the monitoring of glacier retreat rate,

the normalized difference snow index (NDSI) was the most useful composite image. The

NDSI is useful to distinguish snow and ice from similarly bright features like clouds or rocks.

It is calculated using Landsat TM2 (green band) and Landsat TM5 (mid-infrared band) as:

NDSI = 2 52 5

TM TMTM TM

−+

The pre-processed images can then be used as textures to illustrate and highlight different

aspects of the Arctic landscape.


Figure 10 Different channel combinations lead to various composite images. Modified from Aronoff (2005). Numbers below composite images represent Landsat band combinations, e.g. 321 is a color composite of band 3 - red, band 2 – green and band 1 – blue. Band numbers are explained on a left panel.


Pansharpening (Image fusion)

Image fusion integrates color information from a low-resolution multispectral image with the

geometric detail of a high-resolution panchromatic image to increase the spatial resolution of

the multispectral imagery. This technique, also called pan-sharpening, is important because

most satellites such as Landsat, IKONOS, SPOT and QuickBird provide both panchromatic

(higher spatial resolution) and multispectral (lower spatial resolution) images. Therefore, data

fusion can increase the application potential of remotely sensed images. Two technical

limitations are the major reason why most satellites do not collect high-resolution

multispectral images directly:

1. The incoming radiation energy to the sensor (pan image covers a broader wavelength

range), and

2. The data volume collected by the sensor is larger for multispectral images.

Thus, an effective image fusion technique is an optimal solution for providing high spatial

resolution and high spectral resolution remotely sensed images for use in visualization. Since

pansharpening increases both the spatial and spectral resolution (Walsh et al. 2003) of the

original panchromatic image, the fusion is an integral part of the data pre-processing. The

high level of detail at variable scales required by photorealistic animations can be achieved

only when the original image is of a high resolution.

At the present, the quality of the fused imagery depends on the method used for the fusion as

well as on the experience of the user. The main problem for this technique is color distortion

of the fused imagery (Zhang 2004). Although there are various image fusion techniques (e.g.

intensity-hue-saturation (IHS), hue saturation value (HSV), texturization, principal component

analysis (Walsh et al. 2003)), in this work a hue saturation value technique was used. Here a

traditional, red-green-blue (RGB) model is changed to the HSV model. The value band is

replaced with the high-resolution image. Hue and saturation bands are automatically re-

sampled to the high-resolution pixel size using a nearest neighbour, bilinear, or cubic

convolution technique. Finally, the image is transformed back to an RGB image that has the

pixel size of the input high-resolution data.

A Landsat ETM+ panchromatic image (spatial resolution 15m) was used to enhance the

spatial resolution of a natural color composite image [blue (band 1), green (band 2), and red

(band 3)] (spatial resolution 30 m) covering Auyuittuq National Park (Figure 11). The images

are georeferenced, which is a requirement for successful pansharpening. The image fusion


resulted in a high resolution color composite image with a spatial resolution of 15 m. In this

case, the image sharpening technique used a hue-saturation value (HSV) to automatically

merge the lower-resolution color and higher resolution panchromatic images.

Figure 11 Image fusion. Example from Auyuittuq NP. a) panchromatic image (15 m resolution); b) true color composite LANDSAT 7 ETM + image (30 m resolution); c) the results of image fusion (15 m resolution)

3.4.3. Terrain modeling techniques

Three different texturing methods (with reference to the workflow given in Figure 6b) were

employed in this work with the aim of testing the effectiveness and to offer recommendations

for future work. Simultaneously, during this step we were able to evaluate various terrain

modeling software.

Modeling of Nahanni and Auyuittuq NPs

The approaches used for modeling of Nahanni and Auyuittuq NPs were the following:

a) Modeling with contour lines where photorealistic visualization (3ds Max 7) and GIS

(ArcGis 9.1) software were combined.

b) Modeling with displacement mapping.

c) Modeling with a commercial plug-in called Dreamscape® for 3ds Max.


Modeling with contour lines

For this method free contour data were obtained from the Geomatics Canada database, NTDB

(National Topographic Data Base) (Figure 12a). Data are based on the North American

Datum of 1983 (NAD83), scale 1:50 000. The measuring units are expressed in meters and

contour interval is 40 m. A high resolution digital elevation model was used to create contours

with lower intervals (i.e. 1m or 0.5m) in order to model a terrain with a higher level of detail.

In this case, the contour interval is appropriate because of the size of area being modeled.

Larger area size and smaller contour intervals will result in a 3D terrain model that contains a

large number of faces with significantly slower performance.

Figure 12 a) NTDB contour lines (scale 1:50 000) and b) LANDSAT image of the same area of the

Auyuittuq NP Contour lines were prepared for terrain modeling in four steps (Figure 13) using ArcGis 9.1

technology (ModelBuilder 9.1). Four different tiles covering the area of interest were merged

and projected to UTM Zone 19. Subsequently, topological errors such as coincident lines and

line crossing were removed by line smoothing. As a final step, the contour lines (Figure 29a)

were exported as a CAD drawing. Also, a georeferenced and pansharpened Landsat image

(Figure 29b) was exported with the same extent as contour lines.


Figure 13 Cartographic model of contour lines pre-processing

Within 3ds Max it is necessary to change the units to the metric system. The CAD drawing

file is then imported to 3ds Max which displays elevation contours as editable splines (Figure

14a). The imported editable splines behave independently and it was necessary to check for

and fix any existing gaps for each spline. Using Terrain tool in 3ds Max, a new triangulated

mesh surface (Figure 14b) was created based on the imported data. Furthermore, to check and

analyze the elevation change of the model compared to the contour lines, colors were assigned

to the model according to elevation zone values as presented in Figure 15.

Figure 14 a) Imported CAD drawing into 3ds Max as editable splines; b) Generated triangulated mesh in 3ds Max based on contour data of the Auyuittuq NP


Figure 15 A 3ds Max terrain model with applied color for elevation zones of the Auyuittuq NP. Left: top

view; Right: oblique view.

Modeling terrain with displacement mapping

Displacement mapping is an effective technique to increase the level of detail on a polygon

based surface while allowing a fewer number of polygons to model a surface when compared

to using contour data. A base geometry is displaced and modified using a displacement

function usually sampled and stored in an array, a so-called displacement map. Displacement

mapping creates new geometry by first dividing (tessellating) existing polygons into smaller

ones, and then perturbs the new geometry by displacing the created vertices according to a

displacement map. Figure 16 demonstrates these processes performed with 3ds Max software.

The simplest land surface, which is a flat plane, here presented with an editable mesh, was

modeled with a displacement map using planar mapping coordinates. A grayscale image

representing DEM data for the area being modeled is used as the displacement map. Lighter

colors in the grayscale image displace the base geometry more strongly than darker colors,

resulting in a 3D displacement of the geometry.


Figure 16 An example of a gray scale image used as a displace map obtained from

DEM data (source: www.geobase.ca ,scale 1: 50000)

Modeling terrain with DreamScape Terra®

A commercial plug-in for modeling terrain, DreamScape Terra for 3ds Max, was used for

visualization of Auyuittuq NP. A procedural texture was generated to texture snow coverage.

The term “procedural terrain” refers to a terrain generated by advanced procedural models

such as fractals or particular models with unlimited amounts of detail, meaning that when the

camera moves closer to the terrain, the terrain does not become blurry or pixelated because

more detail is added the closer the camera gets. Similarly to some previous works (Gruen and

Murai 2002), the elevation and slope were used as the main factors for the snow coverage

generation. A maximum slope of 45 degrees was chosen, which limits the snow retention to

mesh faces that are not steeper than 45 degrees. The advantage of the DreamScape Terra is in

a novel rendering technology that enables rendering of very large detailed terrains with

minimal memory consumption. In addition, since the terrain is procedural, a large amount of

detail can be added without slowing rendering speed.


Figure 17 A screen capture of imported DEM data of Auyuittuq NP in 3Dem

Dreamscape modelling was done using DEM data downloaded from Geobase. DEM data

were imported into 3dem® software (Figure 17). To obtain more information about the terrain

surface an examination of the elevation profile was conducted. The 3dem’s elevation profile

tool shows terrain elevation as a function of distance between two points and as a

familiarization tool it is helpful during subsequent modeling steps.

In a subsequent step, data were converted from 3Dem® format into Terragen format and then

imported into DreamScape Terra Editor (Figure 18). The resulting terrain is procedural,

created according a grayscale image that has the information about elevation. White areas are

displaced to an overall height setting, black areas are not displaced at all, and all other values

in between are displaced proportionally. Different filters and effects can be applied in the

terrain editor to further increase level of detail and the extent of the realism of the modeled

terrain (Figure 19).


Figure 18 Dreamscape Terra Editor Workspace


Figure 19 Different tools that can increase detail and realism of terrain. a) Terrain erosion; b) Elevation;

c) Slope; d) Texture map paint Because it does not require many pre-processing steps – only a gray scale image as an input –

the displacement mapping is the fastest to apply among the three methods. It is easy to

manipulate, and to decrease and increase the number of polygons. On the other hand, high

level of detail can be achieved only with an increased number of polygons, which in turn

require more processing power. This technique is very useful for pre-visualization of a terrain

when rendering speed is more important than quality of a model.

Modeling with contour lines is of interest because they are the most common data form for

terrain representation. Once converted into the triangulated mesh (also known as a Triangular


Irregular Network or TIN in GIS), it is possible to adjust the level of detail by interpolating

the number of points horizontally or vertically. A higher number of points require higher

processing power. The major disadvantages of the method are the errors that occur during the

data transfer from the GIS to the visualization software (e.g. broken lines). The weld tool in

3ds Max can be used to correct the errors. This tool connects two adjacent broken segments

within a user defined distance threshold. Since DreamScape uses procedural terrains and a

new rendering technology it is possible to render more detailed terrain with minimum

memory consumption. In Dreamscape there are also a set of sophisticated tools (e.g. terrain

erosion, texture map paint) for terrain modeling and manipulation that can be used to increase

the extent of realism. DreamScape is a commercial plug-in and thus represents an additional

investment for a visualization project.

Modeling La Mauricie NP

Since La Mauricie NP has different land coverage compared to the other two NPs, modeling

of this landscape required a different approach. Here, vegetation cover rather than snow cover

was of interest. For the texturing of La Mauricie NP, Vue 5 Infinite® software was used in

combination with 3ds Max.

After importing to the Vue 5 Infinite ® workspace, the DEM was populated with vegetation.

While the selection and population are just a click away in the Vue 5 toolbox, the problem is

the original vegetation distribution. Pansharpened SPOT 5 imagery covering the area of

approximately 30 ha of La Mauricie NP (resolution 5m), was analyzed to obtain the

information about the real vegetation distribution.

As the image suggests (Figure 20), vegetation in La Mauricie NP is not evenly or randomly

distributed. For an easier distinction between dense and sparse vegetation, a panchromatic

SPOT 5 image was transformed into a black and white image and used in Vue 5 Infinite

software as a vegetation distribution map (Figure 20).


Figure 20 Vegetation distribution in La Mauricie NP. a) SPOT 5 panchromatic image(resolution 5m); b)

resulting forest distribution determined according to the SPOT 5 image Using the Advanced Material Editor in Vue 5 Infinite, approximately 25000 instances of the

fir tree (Figure 21) were used to populate densely populated areas. Although in reality tree

density is not constant, in this work an arbitrary and constant population was used to simplify

visualization process. There are 30 tree species in La Mauricie NP. However, in the initial

visualization attempt only fir tree was used because of its readily available 3D photorealistic

model and low number of polygons which make it ideal for testing of vegetation distribution.

To improve the realism more dominant tree species should be modelled and incorporated into

visualizations. The Ecosystem functionality of the Vue 5 Infinite (now in release 6) allows

one to populate multiple species according to various parameters of distribution, for example

elevation, proximity to streams etc. The rest of the sparse vegetation areas which represented

clear-cut areas were populated with grasses and shrubs, as well for purposes of simplicity

(Figure 22). Next, atmospheric effects (e.g. clouds and haze) were added for more

photorealistic representation. Although considered non-essential for decision making,

atmospheric effects such as clouds and shadows contribute to the more photorealistic

presentation and natural perception of the scene. Elements like the atmosphere are important


in photorealistic visualization because one does not want the viewer to concentrate or notice

what is missing at the expense of the focus of the visuals being presented.

Figure 21 Vue 5 Infinite: a) Fir tree instance in the tree toolbox; b) A rendered example of photorealistic

fir tree

Figure 22 Populating sparse vegetation areas covered with grass and shrubs with Vue 5 Infinite

3.4.4. Animation (Photorealistic dynamic visualization)

Pre-visualization story-board

Relating the scientific concept of interest to a previsualization storyboard is one of the

fundamental steps in scientific visualization. For example, the relation between a glacier and


proglacial lake such as Crater Lake in Auyuittuq NP may be one such ecological/geomorphic

process that can be monitored using EO data. A storyboard is constructed that determines the

motion and timing at key points of interest that illustrate the physical or ecological

relationship. This is a visual process by which key-frames are established and rendered like

the examples in Figure 23. With these key frames and timing established, an OpenGL-based

previsualization or “previs” is created for direction purposes. It can be seen that even for short

movies the number of steps in previs can be considerable. The more steps that are defined, the

greater the control is over the content timing that is presented to the viewer, although at the

expense of rendering time and disc space.


Figure 23 Story board for Thor peak in Auyuittuq NP


3.4.5. Light and camera positioning

The transition between the key frames is determined by defining the movement of the camera

alone, movement of the whole 3D object or its parts, or both camera and the object/parts

(Fabio 2003) along the motion curves. Two types of cameras are used to generate computer

animation: a target which is a restricted camera, and a free camera (Boardman 2005). The

target camera has two components: a camera and a target. The camera is restricted since it is

always pointed toward the target. The free camera resembles the real camera: it can be freely

positioned in working space. Various types of splines utilized for the transition between the

two adjacent key-frames are left for the computer to interpolate. Control over velocity and

special constrains along the movement paths must also be predefined by the user.

While different media (e.g. movie, TV) in different countries (e.g. UK, USA) have fixed

speed of key-frames, a speed between 18-24 fps is usually used (Mealing 1998). Similarly to

the camera, the lights on the photorealistic scene are positioned and oriented in a particular

direction. Since in this work satellite imagery was used as texture, the scene lighting was

positioned in such a way as to match the shadows on the satellite imagery. The sun angle can

be quickly determined from the date of the image acquisition and so terrain effects in the EO

should be accounted for, otherwise shadow’s can confuse viewers. As a guideline for a more

natural appearance of the modeled scene, an iterative image enhancement is recommended.

Contrast, color saturation and hue should be adjusted until both very bright (e.g. snow,

glaciers) and very dark (e.g. shadows) areas on the image show clear structure (Hirtz et al.

1999). In most cases, the light was linked and animated together with the camera and its

intensity and dynamics was adjusted to closely resemble natural light on the scene.

3.4.6. Rendering

Two rendering algorithms were combined in the 3ds Max environment to obtain maximum

benefits from both approaches. In Table 4 advantages and disadvantages of both algorithms

are given.

The level of detail in dynamic visualizations is one of the present challenges for rendering.

Here, the traditional solution is applied. The object database is constructed at different levels

of detail, where more detailed presentation is used when the object projection becomes larger

and vice versa (Watt 1997).


Table 4 Comparison between rendering algorithms (3D Studio Max 2003)

ALGORITHM ADVANTAGES DISADVANTAGES

Ray-Tracing

Accurately renders direct illumination, shadows, specular reflections, and transparency effects.

Memory efficient

Computationally expensive. The time required to produce an image is greatly affected by the number of light sources.

Process must be repeated for each view (view dependent).

Does not account for diffuse interreflections.

Radiosity

Calculates diffuse interreflections between surfaces.

Provides view independent solutions for fast display of arbitrary views.

Offers immediate visual results.

3D mesh requires more memory than the original surfaces.

Surface sampling algorithm is more susceptible to imaging artifacts than ray-tracing.

Doesn’t account for specular reflections or transparency effects.

The speed of rendering is one of the issues to be examined here. For example, with

approximately 10 minutes of rendering time per frame, a 1 minute animation playing at an

average of 30 frames per second (fps) (a standard speed for USA and Canada) would require

12.5 days (30fps×10min×60s/min=18000min) for rendering on one local station. Therefore,

the use of a network rendering is recommended. Network rendering uses a number of

computers connected together over a network to perform a rendering task. This network is

also called a rendering farm. Usually, computers are used as standard workstations during the

day and for network rendering overnight. Each computer renders one frame at a time. One

computer is set up as the network manager which sends the work to render to other computers

(servers). The communication between them is ensured via a management interface for

rendering that splits the rendering project at the beginning and collects the rendered images at

the end in a common, shared directory. Our rendering farm consisted of 80 workstations. Ten

of those workstations are state of the art with system configuration: Dual Core Intel®

Xenon™ CPU 3.20 GHz, 4GB RAM, NVIDIA® Quadro FX4000 video cards of 256 Mb.


3.4.7. Compression of output data

Once rendered, even short movies of as little as 20 seconds created by the above mentioned

methodology can be very large in size (over 1GB) due to high data volume. This file size is

not suitable for web based applications and storage on CD or even DVD media could be a

problem. The usual methods for file size reduction such as lowering resolution, the number of

frames per second, or color format cannot be applied here since they will undermine the final

objective of the work: photorealistic representation. Therefore, one of the final steps in the

creation of 3D photorealistic dynamic visualization is the compression of output data. With

this method all movies retain their original photorealistic representation while reducing the

file size. Windows Media Encoder is used for data compression. It creates windows standard

AVI (Audio Video Interlace) format files in Windows Media 9 format. In our case, some of

the generated photorealistic visualization with over 2 GB in the original version were

compressed down to 42 MB. Thus, the compression with AVI was over 98% with little

influence on the quality of the presentation. There are other options for compression that rely

on compression formats similar to Windows Media 9. These are called MPEG4 compliant

formats. MPEG2 for example is the format used for compression on most commercial DVDs.

MPEG is an acronym for the Motion Picture Experts Group audio/video compression format.

QuickTime, DivX and Flash Video are compliant with those levels of compression and high

quality output. However, many of these compliant formats, including MPEG4 itself are costly

to purchase whereas the Windows Media Encoder and format are freely available from

Microsoft.

3.4.8. Post-processing

The final step in the process of making animations is post-processing. Usually, photo-

visualizations could be used as stand alone products. In most cases, however, value added

post-processing is the final step in the methodology (Figure 6e). Here, various aspects are

added to a photorealistic visualization to increase its multi-media potential. While

visualization accompanied with textual messages offers an increased level of information to

the audience, the visualizations with added music offer an audio-visual rich multi-media

experience for the audience while simultaneously conveying a serious underlying message.

In our case, both text and music ware added to the final photorealistic visualization. For music

creation Adobe Audition was used. In addition, original music was composed and performed


for the Auyuittuq scenes by a classical singer (Ms. Nathalie Paquette). Customized music that

is copacetic with the animation is very effective in conveying emotion with the scene. Various

toolboxes are available for creation of the desired audio effects which are then embedded with

the running video presentation. The final output product was a multi-media DVD (Digital

Versatile Disc) presentation since DVD technology offers the high quality and speed

necessary for video presentations with appropriate storage capacity. A DVD was produced in

the Adobe Encore DVD environment. A typical screenshot of this environment is shown in

Figure 24.

Figure 24 A screen capture of Adobe Encore DVD workspace

During this step it was necessary to consider various aspects that enable high interactivity and

a user friendly interface. Even during the creation stage it should be considered that the

visualization might be played on a standard definition or high definition television. The DVD

technology enables anamorphic video storage, e.g. each pixel stores as much video

information as possible. This format can later be transformed to the aspects required by

standard or high definition TV. Audio formats supported by DVD-Video technology are

MPEG-2, Dolby Digital, and linear PCM (LPCM). However, Dolby Digital is currently the

format most widely used for audio on DVD-Video.


For DVD authoring it is necessary to consider the following:

a) Identifying the media necessary to be incorporated in a DVD

b) Assembling the video and audio assets, identifying chapter points, titles, and title sets

c) Organizing the content to fit a flowchart

d) Defining the links between various presentation groups and defining interactivity and

accessibility (e.g. action following a particular selection on a menu)

e) Testing the menus and navigation.

An example menu from the DVD created for the three National Parks used in creating the

workflow for integration is presented in Figure 25.

Figure 25 DVD Main Menu

The menu offers three parks as the main selection and in the background the geographic

location of each park is presented. Various types of information about the parks are also

offered. With a click, the user will get to the selection of various photorealistic presentations

for each park.


Other dissemination mediums were used in the project. Visualizations can be played using

web-based players such as Windows Media Player or QuickTime. We have also used mobile

solutions such as video enabled cell phones, video mp3 players (e.g. iPod video) or PDA

(personal digital assistant) devices (Figure 26) for dissemination of visualizations. Because of

its screen resolution (320x240 pixels) to play visualizations on an iPod video device a file

conversion was necessary (from avi to m4v extension). The high quality of visualizations

remains the same after the conversion process because the m4v compression is MPEG4

compliant.

Figure 26 Visualizations on mobile dissemination devices; a) Apple® iPod video mp3 player; Hewlett

Packard iPaq® PDA (personal digital assistant)

3.5. PUBLIC OUTREACH: TOOLS AND RESULTS

Public outreach is a first step in the preservation of the ecological integrity of various

Canadian National Parks. Therefore, the visualizations of these environments were used to

engage the general public’s interest and imagination. The hypothesis was that the

visualizations would serve as a main tool for communicating the environment of the parks,


produce interest in the viewer, and therefore induce an appreciation of the need to preserve

the ecological integrity of these environments. As a result, the well informed public would

have an attachment to the issue after being visually introduced to the beauty of these

environments, thus prompting more concrete action for their preservation.

3.5.1. Photorealistic fly-through presentations

The selection of the National Parks for the photorealistic visualizations in this project was

based on the need for their preservation. Auyuittuq NP is a remote Arctic environment full of

glacial features that are currently in danger due to climate change. Visualization of this

environment will first record its current state and will be used in the future for the monitoring

of glacier retreat. The introduction of this NP to the general public should increase awareness

of the impacts of climate change in remote Arctic environments and the need for their

preservation. As a final product, various fly-through presentations were generated. For

Auyuittuq NP these involved several routes along major glacial features (Figure 27). In Figure

28, the four pre-design fly-through routes are presented.


Figure 27 Major glacial geological features of Auyuittuq NP


Figure 28 Four fly-through routes in Auyuittuq NP, green dots represent start and red dots are the end of the routes.


Four different fly-through photorealistic presentations were created for Auyuittuq NP. The

fly-through path Number 1 in Figure 28 is the visualization of Akshayuk Pass. This 97 km

long canyon was created by the movement of the continental glaciers during the last ice age.

Today, however, it is completely ice-free. The fly-through path follows these 97 km with

emphasis on various geomorphological features along the way. During the flight, the camera

is focused in on these futures to provide greater detail.

The second path (Figure 28) is the fly-through of Crater Lake and the surrounding ridge

which marks the limit of the last advance of the glaciers that occurred about 100 years ago.

Today, when the glacier melts, the ridge acts as a natural dam. The photorealistic visualization

presents the ridge along its length and finishes with a view of this unique circular lake.

Another lake presented here was Summit Lake, located at the highest point of the Akshayuk

Pass. It was also formed by the pooling of glacial melt waters. The glacial waters from this

lake flow into the rivers and drop about 500m before reaching the Arctic Ocean.

The visualization of Mount Thor, named after the Norse god of thunder, was of special

interest since this 1675 m high mountain, has a cliff face of 1 km, the longest uninterrupted

cliff face in the world. In the fly-through photorealistic presentation (Figure 28), fly path

Number 4) offers an in-depth, high resolution perspective of this unique geological feature of

high interest for public outreach. The major challenge was modeling of snow coverage

(Figure 29). Snow covered landscape is recognized as an issue in photorealistic image

synthesis due to the difficulty in utilizing common primitives (e.g. polygons, curves) to model

snow-like shapes (Yanyun et al. 2003). Some authors suggested unique techniques for dealing

with snow coverage (Yanyun et al. 2003) such as a hybrid multi-mapping technique where

displacement mapping is used to model snow on the object in the vicinity of the observer,

while a volumetric texture is used for modeling snow coverage of distant objects. This method

provided the most realistic snow cover for the visualizations created for this research.


Figure 29 A winter scene from Auyuittuq NP with procedural texture (computer generated snow)


The photorealistic fly-through presentation of Highway Glacier (Auyuittuq) demonstrates

how this great river of ice creeps down from the high plateau of the park's interior towards the

valley floor. Among the three visualized National Parks, the visual appreciation of the

audience was highest for these areas.

Another NP selected for visualization was Nahanni NP. The fly-through animations of

Nahanni NP (Figure 30) included two different routes. Here, photorealistic visualization was

used as a communication tool to engage the interest of the public and increase support for

park expansion. Pollutants from activities such as mining and logging performed in the

upstream areas outside the park boundaries are travelling downstream and affecting the

environment within the park. Thus, the 3D photorealistic visualizations represent two

different areas inside and outside the park, emphasizing the similarities and richness of both

areas.

Figure 30 Photorealistic fly-through routes through Nahhani NP

La Mauricie NP is an area rich with rare flora and fauna nested in a combination of lakes and

forest within the Laurentian area. Modeling the park offered a challenge of another type.

Here, the dominant feature is vegetation. While various options were attempted in modeling


the variety and distribution of vegetation, a combination of Vue 5 and 3ds Max was found to

be the most valuable approach. While the Vue 5 environment was excellent for modeling the

population of large areas with various vegetation types, 3ds Max was invaluable for terrain

modeling and rendering using the Mental Ray renderer. However, among the various models,

this one was found to be the most challenging in terms of texturing large landscape areas with

a sufficient level of detail for fly-through presentations. In particular, vegetation realism is

still not at the level of scientific scrutiny, but if camera motion is maintained during

visualization, the trees can be very realistic and details of leaf structure, branch order and

angle are not noticed by the observers. Some of the created visualizations are presented in

Figure 31.

While there are various models for creating vegetation, it should be pointed out that the tree

growth models, e.g. forest-gap models, and seasonal changes were not incorporated into the

current presentation. These are pure photorealistic landscapes based on remote-sensed

imagery aquired at a specific point in time. Thus, they capture vegetation distribution in a

particular space at a particular moment in time. Photorealistic presentation re-created this

distribution with the help of computer generated vegetation.


Figure 31 La Mauricie NP: Different views of a clear-cut area

3.5.2. Public outreach: Various Levels

The second objective was the utilization of the photorealistic fly-through presentations for

public outreach on the issue of the preservation of the ecological integrity of the modelled

National Parks. While we expected a mild curiosity given the novelty of the methods

responses were more than overwhelming.

Public outreach. Experts in the field. Immediately after the initial presentation to Parks

Canada, there were indications that the integrated approach was a success. Despite the lack of


measurable parameters, e.g. the number of people that have seen the presentation and their

recorded response, a general conclusion in the Parks Canada team was that the methods

employed here would be further utilized for the visualization of other national parks. This

initial presentation and several highly specialized conference presentations lead to the

conclusion that the method is indeed valuable for geomaticians since colleagues from United

States, Germany and Australia requested help with their visualizations or showed an interest

in collaboration on the subject. The best confirmation of the success of the approach applied

in this work came from the International ENVI Challenge 2005. The innovative methodology

on photorealistic visualizations as a tool for the preservation of ecological integrity won

second place in this international contest. The work also received an award from PCI

Geomatica, a leading geomatics software producer in Canada.

The interest in the integrated methodology applied here was confirmed by the high attendance

at a one-day workshop (the first of its type ever offered in North America) on the integration

of GIS and visualization technology that explained in detail the approach used this project. In

addition, the Canadian edition of National Geographic expressed interest in a collaborative

work on photorealistic visualizations for public outreach and education.

Public outreach. Local level. To promote National Parks in the Ottawa area, a local TV

station (CJOH) was approached. Several minutes on prime time local news covered the fly-

through presentation of Auyuittuq National Park, our approach, and various modes of

dissemination such as an iPod, PDA or cell phone. Approximately 150,000 viewers (CJOH

2006) normally view this newscast.

Public outreach. Secondary schools. A special component of the public outreach on the

local level was educational outreach. The target audience were students attending 149

secondary schools in the Ottawa metropolitan area. A DVD with the photorealistic

visualizations of the National Parks among other geomatics-related educational material was

created in the context of a research-based learning project. The main menu for the

visualizations of National Parks is shown in Figure 32.


Figure 32 DVD for educational outreach: Main menu-NPs

Public outreach. National level. The largest breakthrough in public outreach occurred when

the Canadian Broadcasting Corporation (CBC, Canada) became interested in the project.

Several minutes of prime time national news coverage ensured a Canada-wide, in-depth

introduction of our objectives in communicating ecological integrity and the geovisualization

approach. Broadcasting of the fly-through photorealistic presentations of Auyuittuq NP on

The National with Peter Mansbridge was the largest part of the coverage (>1.5 minutes of air

time). This broad public exposure confirmed that geovisualization is not only a technique of


the future. It is already here, and geoscientists should utilize its possibilities despite the

present challenges. It is estimated that The National attracts on average 1,000,000 viewers

(Burman 2006). This number indicates an enormous potential. We expect that visualizations

will promote the National Parks, communicate the need for their preservation, and ultimately

win audience support and action. At this point we concluded that our objective of assessing

whether such photorealistic geovisualizations would be of wide public interest for public

outreach was achieved.

3.6. CONCLUSIONS AND RECOMMENDATIONS

In this work, we have presented a systematic approach on the integration of GIS and scientific

visualization as a communication tool for the preservation of the ecological integrity of three

of Canada’s National Parks (Auyuittuq, Nahanni and La Mauricie).

While there is no unified method for landscape visualization, our experience has shown that it

is possible to generate photorealistic presentations with a high level of detail using the

advantages of contemporary geomatics and 3D visualization technology. The feedbacks we

have received from geomaticians around the world as well as an international award are

encouraging us to work further to improve this systematic, integrated approach. The following

are general conclusions and recommendations for the development of 3D photorealistic

visualizations:

1. When possible, the highest DEM resolution should be used to achieve the highest

level of detail necessary for providing high realism to the scene. In addition, we have

observed better results when the DEM resolution matches the resolution of the satellite

imagery used for texturing.

2. Pansharpening was found to be a valuable tool. Depending

on the visualization objective, mostly free of charge LANDSAT imagery (30 m) of a

coarse resolution was improved to 15 m resolution.

3. The 3ds Max visualization software platform was capable of addressing most of the

requirements for high end visualizations. However, its performance was considerably

improved by a commercial plug-in for terrain. Among the three tested terrain


visualization possibilities, the DreamScape Terra® has shown to have the best

performance. When budget is a constraint, a more affordable 3D visualization

platform, Vue 5 Infinite, developed in particular for landscape modeling can be used

instead 3ds Max. Moreover, Vue 5 Infinite can be used with Vue 5 xTreme as a plug-

in to 3ds Max allowing for full leverage of both applications.

4. We have found a professionally designed DVD to be an acceptable dissemination

media. We have also tested alternative, mobile options such as PDAs or video mp3

players that could be part of the Bill Gates vision of “anytime, anyplace” information

access (Gates 2001). When working with dissemination media, sound is the only other

sense that can be simultaneously stimulated for the generation of interest in the visual

content.

The second objective was the use of 3D photorealistic visualizations of three Canada’s

National Parks for public outreach. Due to climate change or human activity (e.g. clear-

cutting, mining) the ecological integrity of the Auyuittuq, Nahanni and La Mauricie National

Parks is endangered. To increase awareness of the problems, engage the interest and induce

action, 3D photorealistic visualizations were used. Photorealistic visualizations are an

effective means of communicating ecological integrity since the audience can almost

instantaneously recognize the effects of human activity and climate change with little or no

additional interpretation of the visuals required.

For Auyuittuq NP, the visualizations helped to inform the audience of the current status of the

glaciated areas of the park, emphasizing the most impressive geomorphological features such

as Mount Thor in order to engage their interest. For Nahanni NP, visualizations presented the

low lands and upland areas that need to be preserved from human activity occurring upstream

from the park boundaries. The La Mauricie visualizations communicated the impact of the

extensive clear cuts as an ecological integrity problem to be addressed within the greater park

area (GPA). By visualizing these clear cut areas and the rich neighbouring species, the

influence of clear cutting is visually outlined.

Our target audience was the general public locally and nationally, as well as local high-school

students. Locally we have accessed approximately 150,000 people through news coverage of

a local TV station, CJOH. This leading local news provider showed the 3D photorealistic


animations during its prime time news broadcast. Thus, our objective of reaching the local

audience was achieved.

Nationally, the national news provider, the Canada Broadcasting Company (CBC), showed an

interest in our visualizations. Over a minute of prime time news was devoted to these

visualizations. With an estimated 1,000,000 viewers, The National is the most watched news

coverage in Canada. This enabled us to convey the message on ecological integrity on a

national level.

Future work will involve the development of new tools that will increase compatibility

between geomatics and 3D visualization platforms and enable easer and faster visualization of

spatial data. The development of the new, improved tools will be useful in visualization of the

remaining National Parks. Furthermore, quantitative measurement of the effectiveness of

photorealistic visualizations on the public perception of the need for preservation of

ecological integrity should be conducted.

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Chapter 4

Conclusions and Recommendations

Chapter 4 Conclusions and Recommendations 89

4. CONCLUSIONS AND RECOMMENDATIONS

This work is a part of ongoing research in geomatics regarding the evaluation, integration and

benefits of photorealistic visualization technology for more effective communication of

geomatics concepts. This thesis has elaborated on several aspects of contemporary

geovisualization. To reiterate, the specific objective of this thesis was to extend current

geomatics visualization software and procedures with state-of-the-art 3-4D photorealistic

visualization applications. Therein, the weaknesses of current approaches to geovisualization

were made explicit and at the same time case studies clearly illustrated the benefits of

contemporary 3D photorealism. Finally, this work has provided guidelines, tools, procedures

and examples that enhance the workflows necessary to bring contemporary geomatics in line

with the state of the art of geovisualization.

The second chapter of this thesis reviewed the importance and impacts of photorealistic

visualization on contemporary geomatics science. The review indicated a number of

significant impacts of photorealistic geovisualization for geomatics, such as:

1. While traditional visual methods are challenged by the continuous proliferation

of georeferenced data, photorealistic visualization and animation tools enable

not only data analysis but also effective communication.

2. Photorealistic visualization has a potential to accelerate knowledge uptake. The

use of photorealism is becoming an integral part of discovery where it can act

as an exploratory, confirmatory, synthetic or presentation method.

3. Relying on human perception and cognition of visual data, photorealistic

visualization is gaining recognition as an effective communication tool for

spatio-temporal data.

A number of traditional applications of photorealistic visualizations were identified, such as

landscape and urban planning and development, and earth sciences. Also a growing trend in

the use of visuals in emergency preparedness situations will likely be a future avenue for

research. In this work we have also identified one of the major challenges in contemporary

photorealistic visualization: a gap between state of the art geomatics visualization capabilities

and those of state of the art 3D visualization technology. Despite advancements in the


development of visualization tools, geomatics software is still generating visualizations of a

low degree of realism and complexity. Advanced modeling, lighting, and texturing tools as

well as sophisticated rendering methods (e.g. ray tracing, radiosity) that enable the creation of

models with an increased realism that are employed in 3D visualization platforms are not an

integral part of geomatics technology today. Rather the development of geomatics has been

largely focused on analytical tools for spatio-temporal data analysis while data presentation

was left at the lower level of realism. In a related vein, even the cartographic capabilities of

current GIS software are rather poor and one requires expensive software for high-quality

automated cartographic design output.

It is therefore evident that a need exists for an integrated approach where the advantages of

3D visualization technology such as advanced texturing, lighting and rendering methods are

combined with advances that are offered by geomatics technology in handling earth

observation data in order to design 3D photorealistic landscapes. To develop an integrated

solution and workflow, two case studies presented in Chapter 3 applied knowledge gained

from the review in Chapter 2 for the creation of photorealistic geovisualizations.

The first case study in Chapter 3 devised an integrated approach that used earth observation

data to bridge the two technology platforms: geomatics and 3D visualization. The objective

was to create 3D photorealistic visualizations of three of Canada’s National Parks in order to

communicate the need for their preservation to the general public. A valid and operational

workflow was presented. We found that:

1. 3ds Max was the most suitable 3D visualization platform for generation of the

photorealistic landscapes.

2. The level of realism achieved is highly dependent on the quality of the DEM data as

well as Earth observation data.

3. To save the expense of high resolution imagery, it is recommended to use a

pansharpening method. Pansharpering is an invaluable tool for inexpensively

increasing image resolution.

4. Among the three methods tested in this work for terrain texturing (e.g. map

displacement, contour lines, DreamScape Terra®), DreamScape Terra® has shown the

best performance. Dreamscape Terra® enables creation of a procedural terrain and has

a set of tools for advanced terrain manipulation.


5. Among different models, it is recommended to use fractal for modeling of complex

terrains where a dynamic level of detail is required while Grammar-based models are

recommended for modeling of vegetation, like those within the software system Vue

d'Espirit® by E-On Software Inc.

6. Particle based systems are the most suitable to generate atmospheric effects, but too

processor intensive for average rendering. As such, volumetric effects and alpha

planes can be used as effectively to simulate atmospheric phenomena such as

inversions with their low-lying dense fog/haze effects.

7. Advanced rendering platforms (e.g. radiosity, ray tracing) are necessary to obtain high

levels of realism.

8. When creating dynamic photorealistic visualizations it is recommended to validate

speed of animated movements since too fast or too slow presentation can loose on its

efficiency.

The end-user is an integral part of photorealistic visualization. Therefore, in the second part of

the study we have investigated the role of human perception and cognition in photorealistic

visualization. In order to be effective, photorealistic visualizations should be free of clutter,

distracting colors and unnecessary details.

Based on the experience with 3D visualization platforms, in order to improve capabilities of

geomatics software in photorealistic modeling of landscapes, the following recommendations

are made:

1. An increased number of view ports (e.g. top, front, perspective) would be beneficial

for the creation, manipulation and preview of models and objects inside the 3D

environment.

2. More terrain modeling options should be included in geomatics software that can

increase level of details and realism of terrain such as terrain erosion.

3. Incorporation of advanced lighting methods with different light options such as global

illumination will enable the simulation of natural light and thus, improve the level of

realism.

4. Improvement of camera handling and increasing the number of camera options (e.g.,

free and target cameras) will enable fine tuning and better quality of animation.


5. Improve tools for the generation and animation of atmospheric effects (e.g. clouds,

mist…) that contribute to photorealism of the scene. These effects are available for

both OpenGL based systems and ray tracing systems.

6. Include advanced rendering platforms that will improve the quality of the final

visualization.

7. Increase number of available file type formats for saving static or animated images.

The visiualizations produced by the integrated workflow in this thesis sufficiently proved the

public outreach objectives on various levels. In our experience, a variety of dissemination

media e.g., DVD, video mp3 players, iPods, PDAs or video cell phones helped to achieve this

objective since the today’s audience has higher expectations and demands the convenience

that such media offer. The quality of the photorealistic visualizations generated by the

integrated approach was such that one local and one national broadcasting company became

interested in presenting them to a wide audience. With over 150,000 and 1,000,000 viewers

during prime time news, the local CJOH and the national CBC broadcasting companies have

contributed to the success of the outreach. New visualizations using the same methodology

are already planned for future parks.

The feedback and interest in the work presented herein, that has been received from

colleagues, other geomaticians, broadcasting corporations, government agencies and the

general public has demonstrated the effectiveness of a photorealistic approach in generating

interest in the subjects visualized. This is encouraging for future work on the improvement of

the integrated approach and serves as a reminder that geovisualization is a new area for

geomatics science. Geovisualization is here to stay, and geomaticians should explore the

boundaries of this new technology further while simultaneously utilizing its capabilities for an

improved exploration and communication of complex geomatics processes.

Appendices

Appendix 1 Computer Graphics for Photorealistic Landscape Visualization 94

APPENDIX 1

COMPUTER GRAPHICS FOR PHOTOREALISTIC LANDSCAPE

VISUALIZATION

The objective of this work is to provide a short summary of the state of the art computer

graphics for novices in photorealistic landscape visualization. Firstly, the three major areas of

computer graphics: modeling, animation and rendering were reviewed and a set of

recommendations for their use in 3D photorealistic landscape visualization were provided.

Secondly, the principles of human perception and cognition, which are influencing the

effectiveness of photorealistic visualization as a communication tool were summarized. A set

of guidelines for the creation of photorealistic visuals that have the capacity to engage the

interest of the audience and aid in the decision making process while never losing their

accuracy, visual clarity and legitimacy is also provided.

Figure 33 Elements of computer graphics used for photorealistic landscape visualization

In Figure 33, the elements of computer graphics used for photrealistic landscape visualization

are presented. Generation of photorealistic landscapes begins with the creation of a 3D

computer-based model. A model is a description of 3D objects with a certain viewpoint,

texture and lighting. Basic geometric primitives such as lines and polygons as well as

advanced models such as fractals, grammar or particle models suitable for modeling of


various aspects of the landscape such as terrain, vegetation and water, have been utilized with

various levels of realism for landscape modeling (Bishop and Lange 2005). Once a 3D

computer model of landscape is generated it is necessary to transform it into an image in a

process called rendering. The rendering process generates a 2D image from a 3D scene by

bringing together the scene geometry, surface properties and lighting. A general rule is that

the more complex the model and object attributes such as lighting and texturing, the more

computationally intense is the rendering. In addition, any walk-thorough or fly-over dynamic

features with a high level of detail, increases the demand on the rendering platform. Thus, for

photorealistic landscape rendering, it is necessary to select a rendering platform that provides

a balance between the desired level of realism and available computer power.

The effectiveness of visuals depends on the how audience perceives them (Tory and Moeller

2004). Therefore, in the last section of this review, I have emphasized which aspects of human

perception and cognition should be considered while creating photorealistic visualizations for

communication with the general public. These concepts are subject to research as much as the

technology behind the creation of photorealistic visualizations (Tory and Moeller 2004).

COORDINATE SYSTEM

Since a computer screen is 2D and modeling takes place in 3D virtual space, it is important to

understand 3D coordinate systems. Coordinate systems enable positioning and easier

manipulation of the elements of the 3D scene. The most common coordinate system in 3D

graphics technology is the 3D Cartesian coordinate system (Figure 34).

Figure 34 A 3D Cartesian coordinate system


The point in space where the three axes intersect is called world origin and represents the

main point of reference in 3D space. A location of each object in 3D space is defined by a xyz

triplet of coordinates of this global coordinate system of the working scene. In addition, all

objects in the scene can have their own local coordinate system (Figure 35 b and c), usually

placed in the center of the object. A local coordinate system in a 3D graphic system is

described by a set of operations called the affine transformations: translation (e.g. a change in

the position of the origin of the local system), scaling (e.g. a change in the scale of the

measurement in the local system), rotation (e.g. a change in the orientation) and shear (e.g.

transformation from an orthogonal to a non-orthogonal system and vice versa) (House 1997).

Such transformations are common in GIS and Remote sensing as a component of the

coordinate rectification process (House 1997).

Figure 35 A scene with global (a) and local (b,c) coordinate systems

GEOMETRIC 3D MODELING

The first step in producing a 3D photorealistic geovisualization is creation of a model.

Depending on the objective, various models of different levels of complexity can be created.


Photorealistic landscape visualizations require simultaneous use of various models (e.g.

terrain, vegetation, water…). Hence, it is useful to understand basic model characteristics in

order to select an appropriate model for a particular objective.

Geometric primitives

3D photorealistic visualizations can be created using a set of elementary “building blocks”

called primitives (Giambruno 2002; Rockwood 1997). Initially, these were points, line, faces

or triangles, however with the development of the computer hardware, the sophistication of

the primitives increased. Today almost all commercially available visualization software has

its own set of geometric primitives such as spheres, cubes, cones and cylinders. The simplest

primitives with low level of detail but easy for manipulation are points. A point, a

dimensionless entity, is defined by its xyz values.

A line dimension entity is defined by the xyz values of its two endpoints. Closing a polyline

by matching start and end points creates the most commonly used geometric primitive - a

polygon (Rockwood 1997). The most popular polygon is the simple triangle (Rockwood

1997). The points of a triangle are called vertices, and the sides are called edges (Figure 36).

An object is created by combining either filled or empty polygons.

Figure 36 An example of a triangle structure


Wireframe models are a system of complex curves generated by combining lines and arcs end

to end. A 3D landscape model in Figure 37 is generated with various primitives. The model

made with point primitives (Figure 37a) is easy to manipulate (e.g., rotate or zoom) but does

not support realism. The wireframe model (Figure 37c) on the one hand has good visual

precision. However, it does not support realism and light is not reflected from the 3D model.

The triangular facet (Figure 37b) supports more complex polygons and is fast to draw (House

1997). The 3D surface looks smoother as the number of faces increase. The more faces a

model has, the more complex it becomes. Because such a model takes more descriptive

information, it takes up more storage space and memory.

Figure 37 Elementary modelling primitives: A) Points; B) Triangles; C) Wireframe; D) Polygons

Most of the confusion when using polygons comes from the fact that they do not distinguish

between the inside and outside of the object (Rockwood 1997). Thus, for modeling of solid

objects where it is necessary to distinguish between the inside and outside of an object,

A B

C D


implicit primitives are used. An implicit primitive has both an inside and outside surface. On

the other hand, there are two main advantages of polygon modeling:

1. The polygon mesh can be of varying density, i.e. adjustable level of detail (Figure 38)

2. These are low-resolution models that are faster to render (Fleming 1999; House 1997).

This makes them suitable for the use in the multi-resolution (different level of detail)

landscape modeling of larger areas with different features (e.g., rivers, lakes and mountains).

The major disadvantage of polygon modeling is generation of truly smooth curves. To model

a smooth curve it is necessary to increase the number of polygons (Figure 38) which results in

longer rendering time and more storage space.

882 Polygons

7442 Polygons

421362 Polygons 611342 Polygons

Figure 38 Increasing the number of polygons improves the object smoothness

Parametric curves


Another major set of primitives for geometric modeling is parametric curves and surfaces

such as Bezier curves and surfaces and B-spline curves (Figure 39) and surfaces (Figure 40).

Developed by P. Bezier for computer modeling in automobile design, Bezier curves can be

controlled by “control points” called vertices that control the degree of curvature along the

line (Mealing 1998). Each vertex is controlled. The vertices control by two other points that

adjust the endpoint tangent vectors which enables a smooth appearance of curves at any scale

as opposed to polygonal curves which do not scale up properly (Rockwood 1997).The B-

spline (basis spline) curve is a generalization of the Bezier curve. The advantage of B-spline

curves over Bezier curves is that the control vertices of a B-spline curve affect only the local

region of the curve or surface (Figure 39). They provide more flexibility and compute faster

than Bezier curves (3D Studio Max 2003). A special form of B-spline curve, non-uniform

rational B-spline, (NURBS) is referred to as a standard tool due to its flexibility in designing a

large variety of shapes (e.g. standard analytical shapes such as cubes, cones, etc. as well as

free form shapes) (Rogers and Earnshaw 1991). The only disadvantage of the NURBS

algorithm is the need for additional storage to represent traditional shapes such as a circle

(Rogers and Earnshaw 1991).

A collection of B-splines can be used to define surfaces, produce a curved edge of an object or

generate the pathway of a moving object (e.g. pathway of a camera). The NURBS surface

(Figure 40) contains both a mesh and its control vertices. The control vertices can be

manipulated individually or as a group in order to model different shapes. Current 3D

modeling software has a very powerful set of tools for manipulating the NURBS (Figure 40)

surfaces. Thus, digital terrain modeling can closely resemble real-world sculpting.

Figure 39 B-spline curve with its vertices (3D Studio Max 2003)


Figure 40 NURBS Surface; A) control vertices, curves and surface mesh; B) rendered surface.

Geometric primitives from point elevations, contour lines, 3D meshes, triangulated surfaces,

and curved surfaces (Figure 41) such as those obtained by the use of various B-spline models

e.g. NURBs (Ervin and Hasbrouck 2001) are the basis for landscape modeling. They can be

used for modeling of simple landscape forms such as tilted plains and artificial shapes

designed by landscape architects as well as for modeling of the complex natural surfaces of

variable slopes, convexities and rough edges (Ervin and Hasbrouck 2001).

Figure 41 a) Contour lines; b) Zoomed-in segment with splines; c) Triangluated surface;

d) Rendered terrain model


Geometric primitives (e.g. point, lines, and polygons) on the one hand are effective in the

reduction of the points necessary for modeling of the terrain and thus, in reducing rendering

time and storage space. On the other hand the major disadvantage is that the generated terrains

are treated only as a surface rather than as a solid (e.g. when seen from a side the terrain is

thin and has no mass) (Ervin and Hasbrouck 2001). These so-called 2.5D representations can

present a problem if the terrain model needs to interact with other models such as

hydrological and geological models (Ervin and Hasbrouck 2001). In addition, geometric

primitives generate terrains of low level of realism.

ADVANCED 3D GEOMETRIC MODELING

Significant developments in computer hardware and CPU power have supported the

expansion of geometric modeling techniques that are now closer to representing the visual

complexities of nature (Ebert 1997). These advanced so-called procedural modeling

techniques store details about the model rather than explicitly storing numerous low-level

primitives (Ebert 1997). Used mostly for visualization of natural objects and phenomena, all

advanced geometric modeling techniques can be divided into two major groups: surface-based

modeling (e.g. fractals, grammar-based models, and implicit surfaces) and volumetric

modeling techniques (e.g. volumetric procedural models and particle systems).

Fractal models

Although fractals have a precise mathematical definition, in computer graphics fractals denote

models with a large degree of self-similarity: parts of the objects appear to be scaled down,

translated or rotated versions of the original object (Ebert 1997). A fractal is also defined as

“a geometrically complex object, the complexity of which arises through the repetition of form

over some range of scale” (Ebert et al. 2003). Many natural objects exhibit this characteristic,

for example plants, trees, coastlines, mountains etc.

Therefore, the utilization of fractal models in geovisualization for landscape modeling in

particular is extensive. Among the first models applied for modeling plant growth were fractal

models (Oppenheimer 1986; Voss 1988). When applied in 3D, fractals could be used for

modeling of complex moving objects such as leaves (Mealing 1998). Random fractals are the

most common technique used for modeling mountains (Figure 42) (Ebert 1997). The


principles of recursive subdivision and pseudorandom perturbation are used for modeling the

surface (Ebert 1997). In other words, the first iteration gives the large peaks on the surface

and later subdivisions add smaller-scale detail that reflect better the results of natural surface

processes such as mechanical and chemical weathering and long/short term erosion. Such

models are, therefore, particularly suitable for the geovisualization of complex terrain with a

higher level of photorealism.

Figure 42 Examples of 3D procedural fractal terrains

Grammar-based models

Similar to the fractals, grammar-based models reduce natural complexity to a simple number

of parameters (Ebert 1997). The most common grammar model is the L-system that was

originally developed for modeling of plant growth (Prusinkiewicz and Lindenmayer 1990).

An L-system is a formal language in which all the rules are applied in parallel to provide a

final word describing the object (Ebert 1997). These models are usually behind most

commercially available software for plant generation such as Vue 5 Infinite’s EcosystemTM.

Both fractal and grammar-based models are used for modeling vegetation. There are two

issues in modeling landscape: visualization of vegetation at a large scale (e.g. modeling of

individual trees) and at a small scale (e.g. modeling continuous fields or forests) (Ervin and

Hasbrouck 2001). Even modeling of a single tree is a daunting task involving millions of

elements (polygons), not to mention lightning, shadows or more complex plant attributes such

as growth, seasonal changes or the addition of movement of or through vegetation (Ervin

2001). The fact that the branching pattern for a genera is constant somewhat simplifies the


problem. Therefore, the above mentioned numerous elements can be modeled using

automatic, algorithmic plant generation (Ervin and Hasbrouck 2001).

The utilization of VRML (Virtual Reality Modeling Language) for vegetation modeling was

investigated by Lim and Honjo (Honjo and Lim 2001; Lim and Honjo 2003). In a three-step

procedure, forest cover can be visualized and presented using a local or network PC (Lim and

Honjo 2003). The steps involve the combination of vegetation and terrain data and their

conversion into VRML format. The authors successfully rendered up to ten thousand trees in

real-time. However, the visualizations were not photorealistic as shown in Figure 43B. While

even state of the art geomatics software is still using VRML or open GL type tools,

commercially available visualization software such as 3D Studio Max, Maya, and Vue 5

Infinite are equipped with advanced tools for highly realistic visualization of vegetation, as

seen in Figure 43A.

A) B)

Figure 43 A tree rendered with: A) ray-tracing algorithm in 3ds Max; B) basic OpenGL

When visualizing vegetation on a smaller scale, plants/trees are not considered as discrete

objects but rather as fields and can be modeled as a terrain texture (Muhar 2001). Here, the

major challenge is obtaining a good balance between the level of detail and the rendering time

(Lim and Honjo 2003). Most of the approaches in that regard are oriented toward

improvement of rendering time while retaining the desired level of detail (Kumsap et al.

2005). A combination of land cover data from GIS, DEM and remote sensing imagery can


lead to better vegetation texture models where the canopy height is taken into consideration

(Muhar 2001).

Implicit surfaces

Implicit surfaces are surfaces of constant value, isosurfaces, created by moving the control

points of the curves such as Bezier and B-spline curves in three dimensions (Rockwood

1997). Implicit surfaces also called Bezier and B-spline surfaces, blobbly molecules,

metaballs or soft objects are used in modeling organic shapes, complex man-made shapes and

soft objects that are difficult to animate. These types of surfaces are highly useful for

modeling of movements induced by wind, e.g. movement of grass, shrubs or individual leaves

as well as movement of water in photorealistic representations.

Volumetric procedural models

In volumetric procedural modeling, also called hypertexturing, volume density functions and

fuzzy blobbies are used for modeling and animation of 3D objects and natural phenomena

such as fire, smoke, fog (Ebert 1997). Here, complex volumetric phenomena are described

with a few parameters such as a point location in space and time, and parameters that describe

the object being modeled. The output is the density and color of the object for that location in

space. These can increase the level of photorealism when applied in landscape modeling (e.g.

fog generation). Modeling forest fires, volcanic eruptions and atmospheric conditions could

be achieved by volumetric procedural modeling.

Particle systems

Particle-system objects are a large collection called cloud of very simple geometric particles

that change stochastically over time (Ebert 1997). Utilized to visualize volumetric natural

phenomena such as fire, water, clouds, snow and rain, this modeling technique has also found

extensive application in landscape modeling (Ervin and Hasbrouck 2001). Each particle has

attributes such as initial position, velocity, size, color, transparency, shape and lifetime (Ervin

and Hasbrouck 2001). A particle system is defined by a collection of geometric particles and

the algorithms that govern their creation, movement and death (Ebert 1997).


Particle models in landscape visualization are mostly used for modeling atmospheric effects,

avalanches and landslides. Although considered auxiliaries, since they are not the main focus

of decision making, visualization of atmospheric elements (e.g. clouds (Figure 44), fog, haze,

etc.), contributes directly to the overall reality of the scene (Appleton and Lovett 2003). While

sky is usually a background color or bitmap image, cloud-generation is based on the creation

of some random “noise” to induce variable densities and “clumping” (Ervin and Hasbrouck

2001). A similar technique is used for the creation of stars on the night sky or rain droplets.

Figure 44 Landscape without and with clouds

Particle system models are also used for modeling water in landscape visualizations. Water as

a flat plane has three important characteristics that determine its appearance in the landscape:

transparency, refractivity and reflectivity. The proportion of the three factors will determine

the properties of the water (Ervin 2001; Ervin and Hasbrouck 2001). State of the art software

offers a wide variety of tools for modeling water. For example, using water tools inside 3D

Studio Max it is possible to adjust the density and viscosity of water that will determine if the

objects on the water will float or sink, and adjust buoyancy and depth of the water surface.

Irregularities such as waves and ripples are of simple underlying mathematical structure

overlaid with random “noise” (Ervin and Hasbrouck 2001). Tools such as ripples and wave

creator (3D Studio Max) allow the user to control the amplitude of waves and ripples as well

as parameters such as phase and decay of the wave length among the others.


Attributes of a geometric object: texture and color

The first computer generated images were filled with uniform painted surfaces giving the

image a synthetic look (Peachery 2003). Since then it has been clear that to obtain more

realism it would be necessary to improve texturing of the surfaces. Thus, texturing became a

constant research objective in computer graphics (Peachery 2003). The biggest steps forward

occurred in the late 1970s with the introduction of bumped and 2D textures and color half-

tones while texturing of water, fog, human skin and animal furs are considered among the

more recent advancements (Ervin and Hasbrouck 2001). However, Schilling emphasized that

more research in texturing is needed (Schilling 1997).

Texture mapping denotes a technique where a 2D image is used to give color, texture and

other apparent surface characteristics to the 3D objects (Figure 45). Satellite imagery, aerial

photos or procedural textures (e.g. a texture map generated by mathematical function) could

be used as a 2D image and applied to the 3D object using a digital elevation model (DEM).

Texture mapping in geovisualization can involve various challenges, from texturing large

landscape surfaces (e.g. fields, forests, mountains) to texturing individual tree barks and

leaves, still or running water, buildings. etc. and all with an as high as possible extent of

realism. By combining DEM and panchromatic aerial imaging, Premoze et al. ((1999))

generated realistic alpine terrain (Premoze and Ashikhmin 2001). IKONOS (Hardin et al.

2005), LIDAR (Forlani et al. 2001) and other satellite data have also been utilized. Merging

various satellite data such as SPOT and LANDSAT to obtain various levels of detail prior to

draping them over a DEM is also common (Boehler et al. 2001). This latter idea of merging

data is known as data fusion in remote sensing.


Figure 45 Geospecific and computer generated textures. a) IKONOS (Resolution: 1m); b) QuickBird

(Resolution: 0.6m); c) LANDSAT (Resolution 15m); d) Procedural texture

In geospecifc texturing, a digital elevation model (DEM) of the terrain is used as a basis and

aerial photos, satellite images or procedural textures are applied as a texture. The DEM is an

array of elevation points created form contour lines and spot heights. While this method

ensures a highly realistic terrain model, there are several issues to resolve in order to obtain

such a high level of reality. Often it is necessary to remove shadows from the imagery, or

combine several satellite images of various resolutions in order to obtain solid terrain that

could be used for fly-through and/or zoom-in animation or in interactive virtual reality

applications (Scheepers 2001). Further improvements in obtaining photorealistic terrains are

obtained by modeling different lighting conditions, reflections and shadows. These, as well as

the addition of other main components such as vegetation or man-made objects are essential

for highly realistic landscapes.


COMPUTER ANIMATION

The only constant in nature is change. Visualizing changes around us has been a challenge for

centuries. Motion is one of the most common dynamic changes (Foley et al. 1997). There is

evidence that our ancestors 15,000 years ago used the texture of cave walls and the

illumination of open fire to create the illusion of motion in carved horses, bison, and deer in

the caves in Cap Blanc, France (Chalmers and Cater 2005). An intensive search for tools

depicting motion was evident in 19th century inventions such as photography (1827) and a

camera for capturing motion (1888) finally, culminated in the invention of motion picture by

Lumiere brothers (1895) (Kuperberg et al. 2002). The next revolutionary step in the search for

tools capable of capturing motion and change in general was powered by the development of

computer graphics.

Today, 3D computer animation is not only a tool for depicting change but also a technique

that offers a plethora of possibilities for scientific data presentation. The ultimate objective of

animation is the simulation of reality by artificial creation of objects that match their

appearance and attributes in real life (e.g. photorealistic animation of dynamic processes or

systems) (Kuperberg et al. 2002).4 Terrain fly-through applications for military, space

simulations, walk-through applications in architecture, archaeology, medicine and engineering

are among the fields reaping benefits from photorealistic animation (Mealing 1998). Given

the dynamic nature of geospatial data it is not surprising that animation has become one of the

most widely used tools in geomatics sciences. The ability to track changes, the fundamental

characteristics of complex geographic processes, is essential for an understanding of these

processes (Yattaw 1999). Regardless whether applied in cartography (Harrower 2002), for

landscape planning (Lange and Bishop 2001) or environmental assessment and protection

(Daniel 1992), the main objective of 3D animation in geosciences is a better understanding of

the complexity of geographic changes. A computer generated 3D animation can represent

both the “state of a geographic system at a given time (i.e. space-time structure) and the

behaviour of that system over time (i.e. trends)” (Harrower 2002). As an exploratory

geovisualization tool, animation allows end users to qualitatively assess large data volumes

and potentially discover space-time patterns that remain hidden in static representations

(Harrower 2002).

4 Here we are concerned with photorealism whereas there are clearly more generalized forms of animation, e.g., cartoons being one of these, and these generalized forms of animation are a focus of study in computer science.


Although animation has much to offer, its acceptance among geoscientists in the previous

decade was slow (Johnson 2002). The slow adoption was mainly due to two factors.

Foremost, the avoidance of animation is related to centuries of experience with other static

representation forms (e.g. maps) (Peterson 1994) and secondly, to the challenge of learning

the new skills necessary for the creation of animation (e.g. learning new programming

languages or software) (Harrower 2002). However, today it is accepted that animation is one

of the techniques that will be increasingly utilized alone or as a part of a virtual and

augmented reality presentations for the exploration of large, spatio-temporal data sets

(Kirschenbauer 2005).

A central research question in visualization in general revolves around the effectiveness of

animation as a communication tool (Tversky et al. 2002). In general, an animation must be

carefully designed in order to be effective. The animation has to be clear and slow enough for

the observer to perceive change, movement and timing as well as relationships between the

parts and the sequence of events (Tversky et al. 2002). Thus, in order to be effective

animation must be planned (Mealing 1998). The planning occurs in a process called

keyframing where the important animation events are identified and placed into frames called

key-frames inside animation software. For instance, in a straight movement of a 3D model

from a state A to a state B, the key events to be placed into key-frames are the state of the 3D

model at the points A and B. Careful selection of the key-frames is one of the most important

factors determining the animation's effectiveness. The more complex the 3D models and their

dynamics, the more key-frames are required for the storyboard (Mealing 1998). The transition

between the key-frames is defined by defining the movement of the camera alone, movement

of the whole 3D object or its parts, or both the camera and the object/parts (Fabio 2003) along

the motion curves in a process called betweeing or (tweeing).

The camera is the observer's view and its changing viewpoint is the main difference between

static and dynamic presentation. Two types of cameras are used to generate computer

animation: target and free cameras (Boardman 2005). The target camera has two components:

a camera and a target. The target camera is limited in its movement since it is always at the

same distance from the target and it is always pointed toward the target which is most often an

object (Figure 46a). The free camera reassembles the real camera: it can be freely positioned

in the working space (Figure 46b). The transition between two adjacent key-frames is


accomplished via interpolation within the software. Various types of splines described in the

previous sections are utilized for camera path interpolation; depending on the level of

smoothness in the movements desired by the programmer (Mealing 1998). The user also

predefines control over velocity and special constraints along the movement path. While

different media (e.g. movies, TV,..) in different countries (e.g. UK, USA.) have a fixed speed

for key-frames, it is usually a matter of experience and experimentation to establish an

appropriate speed for the key-frames for a particular computer animation (Mealing 1998).

One has to consider that 12 frames per second (fps) are considered sufficient but more than

often 18-24 fps are used. For example, film cameras operate using 24fps while video and

television use 30fps.

Although animation significantly improved the description of the complex geographic

systems and processes, its predefined nature limits interactivity with the end user (Tversky et

al. 2002). This limitation is not necessarily a negative connotation because the degree of

interactivity depends on the purpose of the geovisualization. The ability of the end user to

interact with the content is one of the ultimate objectives of the user-centered concept of

geovisualization (MacEachren and Edsall 1999). A completely new world of highly

interactive environments is emerging for geomatic specialists to use virtual and augmented

reality.

Figure 46 Two different camera types in a 3d scene: a) Target camera; b) Free camera with predefined

motion path


RENDERING

Rendering is the process of transforming a 3D scene into a 2D image. Here the physical

process that occurs in a camera when a picture is recorded on film is simulated by a computer

(House 1997). The renderer is an engine that drives the picture-making process. The main

steps in the rendering process are (House 1997):

1. Point of view – orienting the 3D scene to be seen from a particular point in space.

2. Projection – associating points in a 3D scene with a 2D image plane.

3. Visible surface determination – which surfaces in the 2D plane will be visible.

4. Sampling – fixing a set of sample points across a 2D plane and associating these with

visible points on the 3D scene.

5. Shading calculation – determining what color will be reflected/transmitted from these

sample points with respect to scene’s geometry, lighting, materials.

6. Image construction – from the shaded samples, determining and storing colors for

each pixel in the output image.

There are three main rendering methods: rendering polygon mesh objects, ray tracing and

radiosity.

Rendering polygon mesh objects

This algorithm performs two functions (Watt 1997):

1. It identifies the set of pixels that make the polygon that is subsequently changed

from a vertex list to a set of pixels in screen space by the process called rasterization. If not

performed accurately, this process can result in holes in the picture – the most common defect

seen in rendering software.

2. It identifies the light intensity associated with each pixel. Shading makes objects

more “volumetric” (Shirley 2005). Three common options for shading are: Flat, Gouraud and

Phong shading in order of increasing computational expense and increasing image quality.

Flat shading shades each polygon with the same intensity and it is usually utilized as a fast

preview tool. Gouraud and Phong shading are more advanced, efficient and they eliminate the

visibility of the polygon boundaries. The Gouraud shading method averages the light

intensities at the edge of each polygon and subsequently interpolates along each scan line

across the plane lying between these averages. The result is a smooth, eggshell-like gradation

(Mealing 1998). While an excellent tool for shading of diffuse components, Gouraud’s


shading has a problem with the specular component, i.e. if there is a highlight within a matte

polygon that does not extend to the vertices, Gauraud’s scheme will completely miss the

highlight. The highlights are actually reflections of the light and they move as the viewpoint

moves (Shirley 2005). To overcome the specular problem of Gouraud shading, Phong

developed a shading method. Details about the light geometry and accompanying

mathematical expressions behind Phong shadings are given by Shirley (Shirley 2005), but in

general, the Phong method calculates intensity of the light at a given point along the scan line

from its approximated normal.

Ray tracing

Despite being computationally intensive due to the fact that it operates pixel-by-pixel, ray

tracing is still the preferred method of rendering given its straightforward computation of

shadows and reflections (Shirley 2005) that results in high quality imagery. Mathematically a

ray is described by a point of origin, a propagation direction and an algorithm for ray-object

intersection (e.g. ray-sphere, ray-triangle or ray-polygon intersection) (Shirley 2005). As a

result of perfectly sharp shadows, the absence of fuzziness and perfect focus sometimes

makes the final images too “crisp” (Shirley 2005). To minimize this effect a distribution ray

tracing technique that allows soft shadows and fuzzy reflections is applied. Accounting for

light as an area rather than a point is the key for generation of soft shadows (Shirley 2005).

Minimal changes to shadowing code in the original algorithm of ray tracing, such as

representing the area light as an infinite number of point sources and choosing one at random

for each viewing ray instead of a discrete number of point sources, will result in soft shadows

(Shirley 2005). For so-called “soft focus”, the depth of field should be adjusted. Instead in a

point, the light should be “collected” into a non-zero size “lens” (Shirley 2005). The depth of

focus simulates the natural blurring of the foreground and background. An elaborate palette of

tools is available in commercial software (e.g. 3D Studio Max or Maya) for definition of ray

tracing parameters (3D Studio Max 2003). Another useful feature of ray tracing for the

generation of photorealistic scenes is anti-aliasing. Aliasing is a consequence of the fact that

the computer screen has a finite resolution while mathematical functions used to describe a

phenomenon are infinite – thus, the appearance of “jagged” effects especially along the edges

of objects. This effect can be minimized by using anti-aliasing where more than one ray per

pixel (usual for low level rendering techniques) is sent into the scene. Therefore, by averaging

the results of several rays, the edges appear to be smoother (Lintermann and Deussen 2004).


Radiosity

Defined as the amount of energy leaving a particular point on a surface, this rendering

technique is unique compared to all other rendering techniques due to the fact that it takes into

account the relationships among all objects present in a scene (Mealing 1998). Although more

advanced compared to other techniques, radiosity has a disadvantage in long rendering times.

Even before the rendering, the computer must calculate the “radiosity model” based on a

thermal engineering model for emission and reflection of radiation (Watt and Watt 1992).

These time-consuming calculations are the result of the nature of the radiosity model where

the scene surface is divided into a grid. Each grid segment is considered as a secondary light

source and its interaction with all other surrounding segments is calculated in an iterative

process (Mealing 1998). Despite the computational burden, radiosity provides for a significant

boost in realism. The advantages and disadvantages of ray tracing and radiosity algorithms are

summarized in Table 5 (3D Studio Max 2003).

Table 5 Comparison: Ray tracing and radiosity. Reproduced from (3D Studio Max 2003)

Algorithm Advantages Disadvantages

Ray-Tracing

Accurately renders direct illumination, shadows, specular reflections, and transparency effects.

Memory Efficient

Computationally expensive. The time required to produce an image is greatly affected by the number of light sources.

Process must be repeated for each view (view dependent).

Does not account for diffuse interreflections.

Radiosity

Calculates diffuse interreflections between surfaces.

Provides view-independent solutions for fast display of arbitrary views.

Offers immediate visual results.

3D mesh requires more memory than the original surfaces.

Surface sampling algorithm is more susceptible to imaging artifacts than ray-tracing.

Doesn’t account for specular reflections or transparency effects.

Light, shadows and surfaces of a natural scene are complex. Thus to achieve the high level of

realism required in a photorealistic visualization usually both methods (radiosity and ray

tracing) are combined (3D Studio Max 2003). Furthermore, it is often necessary to add


another advanced rendering technique such as multi-pass rendering (e.g. processing of a scene

several times using different rendering techniques and settings) to achieve the natural

appearance of the objects in a photorealistic scene.

One of the additional rendering techniques is bump mapping. Bump mapping as a rendering

effect increases the level of detail on a surface of an object (Figure 47). Effective bump maps

are greyscale images (3D Studio Max 2003). When an object is rendered with a bump-

mapped material, lighter (whiter) areas of the map appear to be raised, and darker areas

appear to be low. The result is a richer, more detailed surface representation obtained without

true geometric deformation (e.g. richer surface without loss in rendering speed). In

photorealistic landscape visualization, bump mapping of the color images onto a DEM as 3D

texturing techniques can be used to visualize vegetation surfaces (Muhar 2001). Management

of the level of detail (LOD) in highly realistic visualizations is based on the viewer distance

criterion: the closer the viewer, the higher the required LOD (Lluch et al. 2004; Remolar et al.

2003). This enables a high level of realism in walk-through applications where the user

always has the highest LOD at the closest distance (Kumsap et al. 2005).

Figure 47 Example of bump mapping. a) 3D model without bump mapping, b) Gray scale image as a bump map; c) 3D model with applied bump mapping


Since rendering of a natural scene due to its complexity is a challenge, the combination of the

above mentioned rendering methods is necessary to achieve high level of realism and detail.

In comparison, state of the art geomatics software (e.g. ArcGIS) currently uses low level

rendering systems such as OpenGL or DirectX (Microsoft) that provide only elementary

functions for shading and lighting. Thus, it is impossible to achieve a high level of realism

using current off-the-shelf geomatics software packages. Therefore, one of the most important

tasks at present in geovisualization is an effective integration of state of the art rendering

capabilities of commercial visualization software with state of the art geomatics software that

currently lacks this capability.

GUIDELINES FOR IMPROVED EFFECTIVENESS OF

GEOVISUALIZATION

Although complex, the human visual system is not unlimited in its capacity (Waddington

2001). The designers of geovisualization should be aware that visual displays can be too

complex or too confusing to comprehend, especially when burdened with clutter, distracting

colors, over-animated, etc. (Owen 1993). Therefore, there is considerable interest in how to

design a visualization model with an end-user in mind, to ease his/her perception, to overcome

confusion and the inherent biases of perceptions in order to make geovisualization a simple

and effective means of communication (Gershon 1994). A scheme from Clark and Lyons

(2004), originally developed for the evaluation of effectiveness of visuals in the learning

process, can be used here with slight modifications to summarize the factors influencing the

effectiveness of geovisualizations as a communication tool (Figure 48).


Figure 48 Factors influencing the effectiveness of visualization. Modified from Clark and Lyons (2004)

When using geovisualizations as communication tools one has to be aware of these three

interconnected groups of factors. Cultural, gender and age differences as well as prior

knowledge, experience and spatial ability of the information receiver determine the perception

and interpretation, and thus, the usefulness and effectiveness of geovisualizations (Tory and

Moeller 2004). Perception influences understanding and understanding influences decision

making.

Guidelines for an effective perception

Human perception is simultaneously a challenge and an opportunity for a visualization

developer (Mackinlay 2000). It is a challenge because an erroneous perception can lead

toward misinterpretation of visualization, which in turn can ultimately lead toward erroneous

decision making. It is an opportunity because a developer can use the existing and ever

expanding knowledge about human perception to develop more effective geovisualizations

(Mackinlay 2000). Processing some visual information is accomplished automatically with a

low level of effort and without conscious thought (Ebert 2005). This processing, referred to as

preattentive processing, is essential for processing large sets of information, and thus is the

essence of effective geovisualization (Ebert 2005). Table 6 combines various classifications

of preattentive features and their influence on perception (Gershon 1994; Healey and Enns

2002; Ware 2000).


According to Ware (2000) all preattentive features can be classified in four major categories.

A classification proposed by Healey and Enns (2002) based on the order of effectiveness of

the visual cues, is easily connected to the original classification by Ware (2000). Finally, one

can establish a link between the two classifications and the usage of geovisualization in design

and communication proposed by Gershon (Gershon 1994).

Color. Color is a first order preattentive feature that attracts human attention. Color scales

based on brightness are better perceived than those based on hue scales (Gershon 1994).

Therefore, a recommendation for non-photorealistic representations of data is: the more

important the data, the brighter the color (Gershon 1994). However, if the accuracy of the size

of the object is of importance, one has to bear in mind that the bright objects on a dark

background appear larger. When “realism” is important, the selection of colors is essential

and usually photographic sources are consulted. In terrain visualization, using only a plain

color from the software color palette will result in an artificial look. More realism is obtained

when color is combined with textures and ray tracing effects (e.g. transparency, reflection)

(Ervin and Hasbrouck 2001).

Table 6 Combined various classifications of preattentive features

Classification of preattentive features

(Ware 2000)

Order of effectiveness (Healey and Enns 2002)

Usage in design/communication

(Gershon 1994) Color

o Hue o Intensity o Brightness contrast

First order features to guide attention

To improve visibility of the displayed data

Motion o Flicker o Direction of motion

Second order features to guide attention

To make the process of observing visual display

faster and effortless Spatial position

o 2D position o Depth o Shading o Lightning

Second order features to guide attention

To make the process of observing visual display

faster and effortless

Form o Line orientation o Length, width o Size o Curvature o Spatial grouping o Number of items

Third order features to guide attention

To increase the faithfulness of visual representation


Motion. The usefulness of dynamic geovisualization is higher compared to static visualization

because of our effortless perception of motion. Healey and Enns (2002) summarized the effect

of motion in a sentence “human vision is made to capitalize on the fact that the world is in

general a quiet place.” Therefore, objects that move or change their position attract our

attention effortlessly. On the other hand, our perception of motion of even a single object on a

simplified path may not be accurate (Tversky et al. 2002). Fast motion of multiple objects on

complex trajectories therefore is even harder to perceive. In addition, regardless of how

smooth and continuous the motion is, some people may conceive it as composed of discrete

steps rather than being continuous. In some cases, this should be an indication that the motion

should be presented in discrete steps instead of forcing continuous animation. The perception

of movement is better with peripheral vision (Hearnshaw 1994). Therefore, this type of

movement should be favoured when generating fly-though or walk-through landscape

presentations. Tversky (2002) pointed out two general principles for effective dynamic

visualization:

1. Congruence Principle: “The structure and content of the external representation

should correspond to the desired structure and content of the internal representation.

For example, since routes are conceived as a series of turns, an effective external

visual representation of routes will be based on turns.”

2. Apprehension Principle: “The structure and content of the external representation

should be readily and accurately perceived and comprehended. For example, since

people represent angles and lengths in gross categories, finer distinctions in diagrams

will not be accurately comprehended. In the case of routes, exact angles of turns and

lengths or roads are not important.”

Movement could be used to make “hard-to-see objects” in geovisualization more visible

(Gershon 1994). The most useful utilization of the movement (animation) in geovisualization

is the representation of time related changes in the data (Hearnshaw 1994). For landscape

visualization, dynamics (i.e. movement through, and movement of) of terrain, water, and

vegetation are of interest (Ervin and Hasbrouck 2001). When designing movements through a

landscape, the positioning of the free and target cameras, their imaginary paths and type of


interpolation between the start and final position are the major factors to be considered since

the terrain model remains fixed. For modeling the movement of the landscape (e.g. modeling

of an earthquake) it is necessary to apply dynamic terrain modelling. Here, the appropriate

selection of the procedural or algorithm approach that includes time parameter is of the

essence (Ervin and Hasbrouck 2001). In photorealistic landscape visualization, dynamic

terrain modeling is important for zoom-in effects where the level of detail has to remain high

regardless of the scale the user selects. For example, a forest seen from a bird’s eye view has

to have a sufficient level of detail so that individual trees can be recognized. As the viewer

gets closer, more details about the tree structure should be visible, such as branches or leaves.

Spatial position. There are multiple cues for our spatial 3D orientation. Although constantly

and unconsciously used, our understanding of them is limited. Ambiguities associated with

the representation of a 3D scene in a 2D display can easily lead to misinterpretation of visuals

(Hearnshaw 1994). Thus, the research in this area is intensive. The most important

preattentive factors for our perception of spatial positions are the combination of pictorial

(e.g. shading, lighting, texture) and moving cues (e.g. motion parallax). Combined, all affect

our perception of spatial position and depth. Motion parallax denotes differences in relative

speed between parts of an object when either the object is moving or when the observer

moves his head. Parts of the object that are further away will appear to move slower while

closer parts will appear to move faster (Boyd 2000). Stereopsis (e.g. stereoscopic vision)

arises from the fact that human eyes are horizontally separated and each eye provides a unique

viewpoint of the world (Ijsselsteijn et al. 2005). Stereoscopic vision enhances our ability to

perceive differences in depth, specially close differences (Ijsselsteijn et al. 2005). A

combination of pictorial cues such as shading, lighting and textures can further improve our

ability to perceive depth and spatial positions of the objects in a landscape (Gershon 1994;

Interrante 2005; Lee et al. 2004).

Guidelines for improved realism

The degree of reality is defined as the amount of detail captured and reproduced in the model

(Shiode 2000). Realism is superseding symbolism in cartography and GIS sciences

(Sidiropoulos and Vasilakos 2006). A comparison between different levels of realism is given

in Table 7.


Table 7 Comparison between different presentations reproduced from (Angsuesser and Kumke 2001)

Criterion Geometric Presentation

Photorealistic Presentation

Nature (Original scene)

Origin artificial artificial natural Level of detail low high infinite Generalisation degree e.g. abstraction degree high low none

Individualisation degree low high only individuals Time dependence low high complete

Information perception little (selected) information in a

short time

much information over a long time

infinity of information in infinity of time

Although there is not reliable visual predictive-evaluation model (Steinitz 2001), there have

been numerous attempts and studies designed to evaluate the extent of realism especially in

photorealistic landscape visualizations (Lange 2001; Lyu and Farid 2005; Nakamae and

Tadamura 1995; Pietsch 2000). Dominant are human perception-based empirical studies

(Daniel and Meitner 2001) designed to evaluate “beauty”, “quality”, “preference”,

“memorability”, “imageability” etc. of photorealistic visualizations as the evaluation criteria

(Steinitz 2001). For example, Daniel and Meitner (2001) investigated the influence of four

different presentations (photorealistic, black and white sketch, greyscale and 4 bit color of the

same forest vista) on the perception of scenic beauty in forest vistas. The authors have

concluded that only photorealistic representation induced the correct perception of the beauty

of the scene. Participants of a study conducted by Appleton and Lovett (Appleton and Lovett

2003) indicated that the increased level of realism of the existing elements in a landscape will

positively influence evaluation of a photorealistic landscape planned for the future.

Despite the efforts of these studies, it is still not possible to strictly define the minimum level

of realism required to achieve the objectives of a visualization (Appleton and Lovett 2003;

Lange 2001). In general however, an increased level of detail, especially in the foreground,

positively affects the rating and degree of perceived realism in a landscape visualization

(Appleton and Lovett 2003; Lange 2001). However, if the resources are limited “the best

place to spend is on the addition of details to the ground surface and foreground vegetation”

(Appleton and Lovett 2003). Other recommendations include improving surface texture,

object material depth, radiosity and specularity as factors affecting the photo-realism of the

3D scene (Fleming 1999).


Guidelines on ethics and accountability

The power of geovisualization to influence perception and behaviour as well as decision

making is evident (Sheppard 2001; Sheppard 2005). With an increasing interest in the

utilization of visualization for enhancing public participation in planning, design, decision and

policy making (Al-Kodmany 1999; Sheppard 2005), the responsibility and accountability of

geovisualization professionals comes into focus (Sheppard 2005; Wallace and van den Heuvel

2005). The problems here are similar to those associated with ethics and accountability in

traditional cartography outlined by Monmonier in his book, “How to lie with maps”

(Monmonier 1991). The author emphasized that “maps are subjected to distortion due to

ignorance, greed, ideology or malice” (Monmonier 1991). There are two essential errors that

a geovisualization specialist can induce in viewers: seeing incorrectly or not seeing at all

(MacEachren 1995). There are a number of initiatives to introduce a code of ethics for

geovisualization professionals that will include not only the design of geovisualizations but

also presentation to viewers and documentation of viewer responses (Sheppard 2001). General

principles should include the following (Sheppard 2001):

1. Accuracy: visualization should simulate the actual or expected appearance of the

landscape.

2. Representativeness: visualization should represent typical or important

views/conditions of the landscape.

3. Visual clarity: the details, components, and overall content of the visualization should

be clearly communicated.

4. Interest: the visualization should engage and hold the interest of the audience.

5. Legitimacy: the visualization should be defensible and its level of accuracy

demonstrable.”

When designed correctly, landscape visualizations are powerful and persuasive tools that

should be used with full understanding of their ethical implications (e.g. influencing decision

making). Clutter-free geovisualizations, designed with human perception in mind (e.g. color,

motion and space) should be objective and accurate presentations of reality that do not

confuse but rather inspire the audience.


Due to the complexity of natural processes when creating a photorealistic landscape model

one has to take into consideration not only the utilization of various state of the art technology

but also principles of human perception and cognition. This end user-oriented approach is

necessary because the final visualization is usually employed as an information carrier and a

tool for discourse between different groups as a support in the decision making processes.

In conclusion, the selection of modeling, animation and rendering tools should support

accuracy and visual clarity of the presentation. Photorealistic landscape visualizations should

engage and hold the interest of the public, and aid in decision making while never losing

accuracy, visual clarity and legitimacy.

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APPENDIX 2

A Simple Study Evaluating the Potential of Visualizations as a Communication Tool in Geomatics

OBJECTIVES OF THE STUDY

The primary objective of this study was to investigate if dynamic visualization is an

alternative to 2D static textbook representations for conveying difficult geomatics concepts of

geomatics. However, 3D animation as a means of explaining the complex theory and

principles upon which the ruling paradigm of geomatics and derivative mapping technologies

are based, requires systematic evaluation. We have two sub-objectives in that regard:

1) To evaluate the effects of dynamic visualization as a communication tool in the

classroom, by asking the question: Do students in an introductory geomatics course

prefer animation over standard textbook illustrations?

2) To evaluate current software for 3D photorealistic visualization and their integration

with geomatics for educational purposes.

Experience shows that the most difficult concepts for students to understand involve those

involving several dimensions and changes in dimensions e.g., 3D to 2D or 4D. Such

fundamentals are also the most challenging concepts to communicate using traditional 2D

textbook static visualizations, numerics or textual descriptions. Difficulties in comprehension

of the aforementioned principles come from the individual differences in students’ spatial

level of thinking that can only be improved through adequate training (Ishikawa 2005). With

this in mind, we devised a series of dynamic visualizations to illustrate basic concepts in the

field of geomatics.

Appendix 2 Visualizations as a Communication Tool in Geomatics 128

BACKGROUND

In geomatics education, it is estimated that, due to the complexity of the topics, 99% of the

effort is allocated to description of the phenomena/problems with only 1% for explanations

and prediction (Wellar 1989). As such, it was recognized early on that visualization would

become one of the most important tools in geomatics education (Wellar 1989; Wellar 1995).

New trends such as web-based, distance learning and self-dedicated learning further

emphasize the need for visualizations as an important teaching tool in constructivist learning

environments (Dickey 2003).

While 2D static visualization (e.g. 2D pictures, graphs and photographs) is traditionally well

established, the utilization of dynamic visualization for educational purposes expanded after

the use of this technology on NASA’s website in the late 1990s. NASA’s dynamic

visualization of satellite images of the Earth was judged as revolutionary for earth science

education (Barstow 1997). NASA’s project leaders emphasised that dynamic visualization in

the classroom improved the communication of complex 3D concepts and eliminated the

cognitive and perceptual confusion usually associated with static 2D representations of those

same concepts (Barstow 1997). The stimulation of cognitive processes comes from the fact

that when presented as 3D dynamic visualizations, dynamic processes are presented in the

dimension of human experience (Sarjakoski 1998). Johnson (2002) thus correctly suggests

that dynamic visualization not only improves students’ understanding but also motivates

students for further learning. On the other hand, Harrower et al. (2000) reported the benefits

of animated and/or interactive visualizations for learning about spatiotemporal processes

depends on the knowledge level of the student; novices and those with an intermediate level

of understanding of the underlying subject benefited more than those with advanced

knowledge. However, the latter provided more valuable tips and suggestions for improvement

of the visualizations used. Libarkin (2002) summarized the benefits and limitations of various

visualization techniques in the classroom (Table 8).

Dynamic visualization or animation, defined as simulating motion pictures depicting the

movements of drawn or simulated objects with its three main parts (picture, motion, and

simulation) in combination with photorealistic visualization has a great potential to enhance

learning (Mayer and Moreno 2002).


Table 8 Advantages and disadvantages of visualizations. Adapted from (Libarkin 2002).

Teaching Learning

Advantage Disadvantage Advantage Disadvantage

Static Easy to design; low cost.

Limited instruction as to what is immediately visible.

Low cognitive load. Easer to evaluate important points.

Passive learning. Incorporation of active learning depends on students’ motivation

Dynamic Difficult verbal descriptions can be translated into visual images.

For novices, time consuming to develop.

Otherwise unobservable phenomena become visible.

Passive learning. Incorporation of active learning depends on students’ motivation.

Interactive Teacher’s role is observing learning rather than leading.

Time consuming to develop. Investment necessary.

Active engagement in real world phenomena. Student controls directions and ideally discovers.

May be difficult to extract important points from complex background.

Hunter and Lewandowski (2004) suggest that dynamic visualization should be used as an

additional tool in the geomatics classroom for better communication of complex dynamic

concepts. Barstow (1997) argues that this tool should take the central stage. Regardless of

where the tool fits within the nomothetic component, it is likely to continue to grow as

predicted by Wellar (1989) and Pailliotet and Mosenthal (2000). However, simply applying

this new technology does not guarantee educational benefits (Hegarty 2004). It is evident that

those educators who intend to prepare for the future cannot afford to oversee or underestimate

the enormous potential of dynamic visualization. To maximize the benefits of scientific

visualization, educators have to understand under what conditions to use the technology, as

well as to consider changing their perceptions and traditional educational practices.

When evaluating a new technology or tool in an educational environment, (Agnew 2001)

advocates the consideration of whether or not the tool will: a) promote greater understanding;

b) lead to skill development; c) foster active (deep) learning; d) motivate students; e) save

time (for students and teachers); f) save resources; and g) save finances. We are particularly

interested in the development of dynamic visualizations for the teaching of geomatics

concepts and capabilities. Because dynamic visualizations are directed, the educator

determines the content, focus and flow of the visual content presented. The major advantage

of this approach is that such visualizations can supplement verbal and static descriptions and

indeed illustrate processes that are otherwise unobservable. For example, the process of


coordinate transformation, the definition of longitude and latitude, and GPS positioning

among others can be effectively conveyed using animation. Through such visuals the student

is engaged in a passive learning mode and thus expends minimal cognitive effort for

understanding. We argue that 3-4D dynamic animations visualizations of geomatics concepts

do indeed meet all of the criteria of Agnew (2001) and therefore are an indispensable tool in

the geomatics educator's arsenal.

METHODS

The visualizations chosen for this test were all based on horizontal positioning concepts. In

the authors’ experience, these tend to be the most difficult concepts for undergraduate

students to comprehend. Therefore, these concepts were seen as one of the best ways to test

the effectiveness of 3D animation. The following animations were created and utilized:

1. Geographic latitude and longitude. Geographic longitude is the east or west angle on the

equatorial plane made between any point on a spherical representation of the earth and the

prime meridian. These are inherent 3D concepts. Our 3D, dynamic presentation started by

presenting a position on Earth model's surface. Subsequently, the animation showed the

opening of the earth from the surface to the core with the selected point being left on the

surface. The imaginary line connecting the point on the surface with the core was drawn to

indicate the angle for easier understanding.

2. Geodetic latitude: For a given location on an ellipsoidal model of the earth, the geodetic

latitude is the angle formed between a line normal (perpendicular) to the surface of the

ellipsoid model at the given location and the plane of the equator (Snyder 1987).

3. Graticule. As a network of latitude and longitude lines on a map or chart that relates points

on a map to their true location on the earth, the graticule was a logical step after the animation

of latitude and longitude. The animation presented the realistic Earth as seen from space being

covered with the network of latitude and longitude lines. At the beginning, the latitude and

longitude lines form a 2D network that winds around the Earth. At the end, it is obvious that

the network at the poles must be modified. This modification is a dynamic transformation


from cylindrical shape to the shape of the earth. At the end of the animation, the points at the

North and South Pole were zoomed in to illustrate convergence of longitude lines.

4. Geoid. The geoid is an equipotential surface of the Earth’s gravity field and can be

approximated by mean sea level. Because the gravity distribution of the Earth is not uniform

(due to irregularities in the density of Earth materials) the shape of the geoid is rather irregular

and therefore not used for mapping. Given that its undulating surface varies more than about a

hundred meters above or below a well-fitting ellipsoidal model of the earth, it was interesting

to present this model in a 3D mode and zoom out selected features. For dynamic visualization,

first the undulating surface was created as a 3D surface. To emphasise the scientific

visualization approach, the gravitational field used in the animation was downloaded from

NASA’s GRACE (Gravity Recovery and Climate Experiments) database (NASA 2003). The

GRACE project is a set of two satellites recording the complete gravitational field of Earth

every 30 days with a precision that is over 100 times higher than any existing measurement

(NASA 2003). The generated image is an equirectangular projection based on the WGS84

datum and it was color coded with colors ranging from deep red (maximal gravitational force)

to deep blue indicating minimal gravitational force. Given that the image was an

equirectangular projection (e.g. the pixels laid out in a regular longitude-latitude grid), texture

mapping and surface displacement were performed using 3D Studio Max. Zooming-in and

rotation features were also used to further emphasize the irregularity of the surface of the

geoid. Rotation of such a geoid was the first segment while comparison of its color coded,

undulating surface to the uniform, featureless earth model representation as an ellipsoid was

the last animation segment, while zooming in was used in between these two segments to

“dive in” to specific structures of interest and indicate those that indeed have different

altitudes in real life.

5. Ellipsoidal Earth is a model defined with a semi-major (long) and semi-minor (short) axis

that approximates the complex shape of the Earth. The ellipsoid is used in mapping because it

has a smooth surface upon which horizontal coordinates of longitude and latitude can be

determined. Many different ellipsoids are used because the earth’s surface is not perfectly

symmetrical and one semi-major and one semi-minor axis that fit some particular

geographical region do not necessarily fit another. A smooth surfaced, ellipsoidal structure

was used in this visualization to represent the earth. In the visualization, latitude and longitude


lines begin to stream from the North toward South Pole, filling in the surface of the ellipsoidal

earth. The angle of the ellipsoid in the space was the real angle of the Earth axis.

6. Map projections. Map projection is a mathematical transfer of the Earth’s graticule of

longitude and latitude lines onto a two dimensional surface (paper or computer). Such a

transformation can be challenging to present using a 2D representation only. At the beginning

of the dynamic representation, the earth with the network of longitude and latitude lines was

presented. The scientific component behind this animation is emphasized in the following

sequences. After the initial rotation of the 3D globe that truly represents the surface of the

entire earth without any distortion, the longitude and latitude lines on the globe (e.g. graticule)

were followed from the South to the North Pole during the animation to demonstrate

convergence of longitude lines toward the poles and their divergence towards the Equator.

This initial network of latitude and longitude lines encircling the globe is in the next sequence

transformed into a 3D cylinder wrapped around the globe tangent to the Equator. In the final

stage of the animation this 3D cylinder transforms into the 2D, planar network. The resulting

planar is the Plate Carreé projection, the oldest (according to some records first generated in

100 AD) and the most commonly used map projection due to its simplicity (Tufte 1990). The

transformation from 3D graticule to 2D planar grid was used to illustrate the distortion of the

shape and the area as an inevitable consequence of mathematical transformation. By

following the animation sequences, students will be able to visually examine a complex

mathematical transformation behind the concept of map projection and at the same time

understand the relative importance of distortion in area and shape.

7. Global Positioning Systems (GPS). To comprehend the movements of 24 satellites along

6 orbits and the determination of individual location on earth using a 2D presentation is a

challenge. Using a 3D dynamic visualization, the clarification is simple. Students have an

opportunity to observe individual orbits and satellites as they are added subsequently to the

screen. It is also possible to isolate a single orbit or a single satellite to illustrate how GPS

ensures correct navigation. This is an advantage over 2D static representation where such

realistic representations are only partially possible (e.g. relative distances from the Earth).

However, representation of the constellation in motion and movement of the satellites in 4D

(space + time) relevant to the Earth with 2D representation is not possible. To distinguish

scientific animation from other non-scientific, descriptive approaches depicting satellite

orbits, the distances from the Earth and the orbital velocities of the satellites were scaled


down. First, the Earth was scaled down, and keeping the proportions, the orbital distances

were calculated. To model orbital movements, the rotation periods of the satellites were kept

proportional to the rotation periods of the Earth. For example, it is known that highly elliptical

satellite systems such as GPS have a rotation period of 12 h. Thus, in the scaled down model,

one full rotation of the satellite around the Earth corresponds to two full rotations of the Earth

around its imaginary axis. In addition, the angles of the orbital planes were kept between 50

and 70o to simulate reality. In all animations depicting satellite orbits (e.g. geostationary

satellite orbits were also animated in this project), these rules were followed.

All animations (Figure 49) were made using a combination of 3D Studio MAX (v.7) for

modeling, animation and rendering and Adobe Premier Pro for postproduction (e.g. text and

title addition, special effects, file compression…). 3dsMax 7 is the state of the art software

that contains the essential high-productivity tools required for modeling, animation, rendering,

and design visualization.

All animations are created at NTSC video resolution with 720 pixels wide by 480 pixels high

at 29.97 frames per second. A windows media encoder has a high level of compression and

was used to reduce the file size without any perceptible reduction in video/image quality.

The effectiveness of 3d visualizations of basic geomatics principles was assessed by

undergraduate students enrolled in an introductory geomatics course. Here it must be

emphasized that no particular sampling strategy from the population (all undergraduate

students at the Department of Geography, University of Ottawa) was applied. The class was a

mixture of 20 second, 15 third, and 5 fourth year students within the Department of

Geography at the University of Ottawa, Canada. Out of 40 participants, 28 were female and

12 male. There was no division of the students into a treatment and a comparison group. All

students were exposed to both novel, dynamic 3D visualizations and the 2D textbook

representation of the same concepts. Although this can be considered true statistically based

experimental design for the study where a treatment and control group were compared, for the

first, simple evaluation of the effectiveness of visualizations it was considered applicable.


a) b) c) d) e) Figure 49 Key frames for animations: a) Latitude/Longitude, b) Graticule, c) Geoid, d) Map projection e) GPS

The classes were held in a multimedia classroom where each student had access to a state of

the art desktop computer. All visualizations were displayed using a high definition screen. For

comparative purposes, both the visualizations and the textbook were equally accessible.

Therefore, the students were able to re-run visualizations after the class as well as read the

textbook.


The questionnaire. After viewing the animations and textbook representations, an

anonymous short questionnaire presented at the end of this work was given to the students.

The questionnaire contained two major parts. In Part A, background information about

students such as gender, age, program of study, etc. was collected. Part B contained the

evaluation of the visualizations and complementary textbook presentations. Students were

asked to rate the presentations from the textbook and the visualizations according to their

personal:

1. Understanding of geographic and geodetic Latitude/Longitude

2. Understanding of Graticule

3. Understanding of Geoid

4. Understanding of Map projection

5. Motivation for learning

6. Presentation clarity

All visualizations and textbook explanations were rated on a scale from 1 to 5 with 1

indicating that a student finds a particular visualization/textbook explanation not useful at all

for understanding of the concept, to 5, indicating a visualization/textbook explanation as

being very useful for understanding of the concept. The students were also asked about their

motivation to study using visualization compared to the traditional textbook diagrams.

Finally, they were asked to judge the contribution of the presentation clarity on their

understanding of the concept. A section was included for open comments/suggestions.

RESULTS

In response to our central research question, students found visualizations more useful for

understanding a geomatics concept as compared to the 2D representation offered in the

textbook. Figure 50 shows the average response to all questions. For statistical comparisons,

all p-values were reported as two-tailed from paired t-tests assuming equal variance.

Regardless of gender and program enrolled, or previous experience with geographical

information science concepts, all students have found that the visualizations improved their

understanding compared to the textbook. The average mark on all questions for scientific


visualization was 4.39 with (95% CI = ± 0.243), while the average mark for textbook

diagrams was 2.86 (with 95% CI = ± 0.373) (p <1.37×10-7 (α=0.05))..

0

1

2

3

4

5

6

1 2 3 4 5 6 7Question

Mea

n w

ith 9

5% C

I

Text book

Dynamic Visualizat ion

Figure 50 Average response of all students on different questions in the questionnaire

Comparing individual visualizations, the biggest difference in understanding of the concept

was observed for the concept of the geoid. The average mark for the dynamic visualization of

the geoid concept was 4.25(with 95% CI = ± 0.259), while 2D presentation in the textbook

scored only 2.61 (with 95% CI = ± 0.363) (p < 7.91× 10-11 (α=0.05)), clearly indicating that

the students found dynamic visualization more useful than static diagrams. Considering the

mode of presentation on motivation for learning the concepts, the average mark for all

visualizations was 4.25 (with 95% CI = ± 0.269) while text book presentation scored only

2.94 (with 95% CI = ± 0..465) (p < 1.16 × 10-11 (α=0.05)).

It is well documented that male and female student have different approaches to learning

(Hartley 1998). Specifically, evidence suggests differences in spatial reasoning between males

and females (Bonanno and Kommers 2005; Colom et al. 2004; Spelke 2005). Spatial thinking

includes knowing about a) space (e.g. information perception, understanding distance,

orientation, direction, Lat/Long coordinates) (DeVarco 2005) b) representation (e.g.

dimensions and translation from one dimension to another, the effect of projections) and c)

reasoning (e.g. the ability to extrapolate and interpolate, calculate shortest distance, decision

making) (NRC 2006). Recently, spatial thinking was recognized as a new framework in the

geosciences and education (DeVarco 2005; NRC 2006) Therefore, it was of interest to

evaluate if there were any gender differences in perception and learning when scientific

concepts are communicated using dynamic visualization as compared to classical textbook


representation. However, we found no statistically significant difference between the

evaluations from male and female students. Further statistical details are given at the end of

the text. Both genders found the dynamic visualization more helpful for understanding of the

various GIS concepts compared to the textbook explanations. This is encouraging, since it

illustrates that scientific visualization is an effective media for the communication of the

complex concepts regardless of the gender of the information receiver. As the internalization

of these visual concepts may differ, however, that is left for future research.

Students’ Perceptions. From a qualitative viewpoint, student responses underline the

statistical inferences found we found. General comments included:

- “They (animations) keep me and probably everyone else extremely motivated.”

- “I think the animations keep my attention to the subject at hand.”

- “The animations are much more useful because it is a high concentration on an

important topic, versus the text placing equal importance on useless stuff.”

- “I found the graphical animations highly useful because they truly do represent what it is

we are to understand and therefore eliminates confusion. I think animations are in this

class particularly important because the concepts study regime full-view plus 3D

representation.”

- “The animations help a lot because it clarifies the theory and concepts.”

- “I find the animations make it easier to understand concepts and they’re better for

visually learning (compared to textbook).”

- “I think the visuals help keep people understand concepts as an easy method compared to

reading a page of writing.

SOFTWARE EVALUATION

There is an abundance of software that can be used for the development of scientific

visualizations. In addition, the use of multiple software packages is not uncommon.

Therefore, one of our objectives was to evaluate software used to create 3d dynamic

visualizations. Although there is a plethora of visualization software available, a combination

of 3D Studio Max and Adobe Premier5 were found to be state of the art for the development

5 3D Studio Max is a trademark of Discreet Media and Entertainment, Adobe Premiere is developed by Adobe Ssytems Inc. and both are available in North America for educational purchase and pricing from Torcomp Systems Inc – www.torcomp.com


of modern and well designed visualizations for teaching purposes. A typical working space

with different view points (e.g. top, left, right) and tools is presented in Fgure 51.

3D Studio Max offered a range of easy, accessible and identifiable tools for visualization of

various GIS concepts. For novice users, it is important to emphasize that the software has a

learning curve. However, a significant extent of realism can be achieved with a wide range of

pre-programmed textures, materials and lighting tools. Lighting in 3D Studio Max uses

photometric lights with a radiosity solution and ray tracing that results in improved image

quality. Photometric lights use light energy to simulate more natural lighting. While radiosity

realistically simulates interaction of the light with the environment by calculating

interreflection between the surfaces, ray-tracing provides better direct illuminations, shadows

and refractions. The combination of these techniques inside 3D Studio Max software results

in an exceptional quality and realism. In the visualizations presented in this study (e.g. the

basics of GPS) the use of lighting was even more pronounced since the central feature in the

visualization was the Earth illuminated in space.

Fgure 51 Typical working space in 3D Studio Max


For the generation of the geoid visualization, for example, the use of the texture and material

tools in 3D Studio Max was invaluable. Selection, change or compounding of materials in 3D

Studio Max is easily accomplished with the number of tools provided. Apparent level of detail

is also increased compared to other software.

The pre-programmed animation management system was extensively utilized in the study. It

was found to be similar to other tools: intuitive and well defined to help the user to achieve

the extent of the animation necessary to convey the message. 3D Studio Max animates not

only transformations (e.g. position, rotation, and scale) but also any accessible parameter (e.g.

angle, material parameters, such as the colour or transparency of an object, etc.). These give

the user a wide range of animation possibilities not available in other visualization packages.

A variety of rendering systems, e.g. native, mental ray or third party renders included in the

software, is an additional advantage of 3D Studio Max over other visualization software

packages and one of the reasons for its utilization in the presented study. 3D Studio Max

comes with its own network rendering software called Backburner. Network rendering uses

multiple computers connected over a network in order to decrease the amount of rendering

time, which is the most computationally intensive task.

While we utilized 3D Studio Max, software in the same class and with similar functionality

include Maya, LightWave (www.newteck.com), and Cinema 4D (www.maxon.net). The use

of any of these would accomplish the same tasks for animation as described above.

Additionally, for geovisualization and landscape visualization in particular, Vue Infinite

(www.e-onsoftware.com) is the least expensive solution with the shallowest learning curve

and best price. The Vue product line is particularly suited to landscape visualization.

In conclusion, it can be assumed that the future development of geomatics will include a

considerable amount of scientific visualization. This small study confirms that both students

and educators could benefit from such an approach to learning. However, a larger statistically

based study should be conducted prior to making any firm conclusions about the influence of

visualization on the communication of the complex geomatics concepts in the classroom.


QUESTIONNAIRE

GEG 2320 B Please fill in the following questions. All responses are anonymous.

Gender Female Male

Age 17-20 21-23 24-26 27-29 > 30

Year of Study 1 2 3 4 5 (Graduate) Other

Please grade on scale from 1-5, with 1 being not useful and 5 being very useful.

Please put your comments in the empty

box below.

Textbook Animations

Understanding Latitude/Longitude

Understanding Graticule

Understanding Geoid

Understanding Ellipsoidal Earth

Understanding Map projection

Motivation for

learning

Presentation

clarity

Program B.A. Environmental Studies BSc Geography BA Geography BSc Biology BSc Environmental Science BSc Earth Sciences

Other_________________________

Have you previously taken a geomatics course at university or college or high-school?

YES NO


ADDITIONAL INFORMATION

Table 9 Statistical Summary: All responses Question

1

Question

2

Question

3

Question

4

Question

5

Question

6

Question

7

Mean Response

Visualization/Textbook 4.45/3.25 4.58/2.97 4.25/2.61 4.33/2.97 4.25/3.06 4.25/2.24 4.16/2.94

Variance

Visualization/Textbook 0.72/0.90 0.46/1.25 0.65/0.98 0.64/0.74 0.71/1.31 0.71/1.31 0.24/1.15

t-stat -5.67 -7.52 -7.67 -6.92 -5.16 -8.62 -8.69

p-value (two tailed) 1.94E-7 1.38E-10 7.91E-11 1.72E-9 2.18E-6 1.16E-12 9.52E-13

For all questions: t-crit=1.99437, α=0.05

0

2

4

6

8

10

1 2 3 4 5 6 7Question

Mea

n (w

ith 9

5% c

onfid

ence

inte

rval

)

Female.TextbookFemale.VisualizationMale.TextbookMale.Visualization

Figure 52 Reponses by gender


Table 10 Summary statistics for male and female students: Visualizations vs. Textbook Mean

Visualization/Textbook Variance

Visualization/Textbook p-stat p-value

Female Students 4.33/2.74 0.05/0.12 -9.99 3.64E-7

Male Students 4.54/3.09 0.02/0.18 -8.72 1.54E-6

p-crit=2.17

0

2

4

6

8

10

1 2 3 4 5 6 7Question

Mea

n w

ith 9

5% C

I

TextbookDynamic.Visualization

Figure 53 Responses of the 2nd year geography students

Table 11 Summary statistics for differences in judging the usefulness textbook and the dynamic visualization by different peer groups Mean

Visualization/Textbook Variance

Visualization/Textbook p-stat p-value

2nd year Students 4.37/2.86 0.02/0.11 -10.63 1.84E-7

3rd year students 4.37/2.80 0.03/0.11 -10.75 1.63E-7

4th year students 4.34/2.83 0.03/0.10 -11.09 1.16E-7

p-crit=2.17


Evaluation for Dynamic Visualizations

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

1 2 3 4 5 6 7Question

Mea

n w

ith 9

5% C

I

2nd year responses3rd year responses4th year responses

Figure 54 Evaluation of dynamic visualizations by different year of study

Table 12 ANOVA: Evaluation of dynamic visualizations, differences among different years of study

ANOVA Source of Variation SS df MS F P-value F crit Between Groups 0.005426 2 0.002713 0.098425 0.906748 3.554557 Within Groups 0.496136 18 0.027563 Total 0.501562 20

Evaluation for Textbook

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

1 2 3 4 5 6 7Questions

Res

pons

es

2nd year responses3rd year responses4th year responses

Figure 55 Evaluation of textbook presentations by different year of study


Table 13 ANOVA: Evaluation of textbook, differences among different year of study

ANOVA Source of Variation SS df MS F P-value F crit Between Groups 0.012663 2 0.006331 0.056095 0.945614 3.554557 Within Groups 2.031672 18 0.112871 Total 2.044335 20

REFERENCES

Agnew, C. (2001). "Evaluating Changes in Learning and Teaching." Journal of Geography in Higher Education 25(3): 293 - 298.

Barstow, D. (1997). "Visualizing Earth." TERC 20(1). Bonanno, P. and P. A. M. Kommers (2005). "Gender differences and styles in the use of

digital games." Educational Psychology 25(1): 13-41. Colom, R., M. J. Contreras, I. Arend, O. G. Leal and J. Santacreu (2004). "Sex differences in

verbal reasoning are meditated by sex differences in spatial ability." The Pshychological Record 54: 356-372.

DeVarco, B. J. (2005). "Spatially aware, scale independent visualization on the sphere." Electronic Imaging, SPIE 5669: 208-214.

Dickey, M. (2003). "Teaching in 3D: Pedagogical Affordances and Constraints of 3D Virtual Worlds for Synchronous Distance Learning." Distance Education 24(1): 105-121.

Hartley, J. (1998). Learning and Studying: A Research Perspective, London: Routledge. Hegarty, M. (2004). "Dynamic visualizations and learning: Getting to the difficult questions."

Learning and Instruction 14(3): 343-351. Hunter, B. and A. Lewandowski (2004). How GIS Professionals Help School Teachers and

Students Use GIS. 2004 User Conference Proceedings. Ishikawa, T. K., Kim (2005). "Why Some Students Have Trouble with Maps and Other

Spatial Representations." Journal of Geoscience Education 53(2): 184-197. Johnson, N. (2002). "Animating geography: Multimedia and communication." Journal of

Geography in Higher Education 26(1): 13-18. Libarkin, J. C. (2002). "Research Methodologies in Science Education: Visualization and the

Geosciences." Journal of Geoscience Education 50(4): 449-455. Mayer, R. E. and R. Moreno (2002). "Aids to computer-based multimedia learning." Learning

and Instruction 12: 107-119. NASA. (2003). "Studying the Earth’s gravity from space: The gravity recovery and climate

experiment (GRACE)." from http://eospso.gsfc.nasa.gov/ftp_docs/GRACE_Fact_Sh_Final.pdf.

NRC (2006). Learning to Think Spatially. Washington, D.C., The National Academies Press. Pailliotet, A. W. and P. B. Mosenthal (2000). Reconceptualizing Literacy in the Age of

Media, Multimedia, and Hypermedia. Norwood, NJ., JAI/Ablex. Sarjakoski, T. (1998). "Networked GIS for public participation-emphasis on utilizing image

data." Computers, Environment and Urban Systems 22(4): 381-392.


Snyder, J. P. (1987). Map Projections--A Working Manual. Survey, U. S. G., U. S. Government Printing Office: 13.

Spelke, E. S. (2005). "Sex differences in intrinsic aptitude for mathematics and science." American Psychologist 60(9): 950-958.

Tufte, E. R. (1990). Envisioning information. Cheshire, Connecticut, Graphics Press. Wellar, B. (1989). Emerging trends in structuring and directing GIS research. Challenge for

the 1990s: Geographic Information Systems., Ottawa: Canadian Institute for Surveying and Mapping: 601-609.

Wellar, B. (1995). "Geomatics education and training, 1995-2000: Trends, issues, opportunities and challenges." Geomatica 49(3): 336-340.

Appendix 3 International ENVI Challenge 2005 Award 146

APPENDIX 3

International ENVI Challenge 2005 Award PHOTOREALISTIC VISUALIZATION USING ENVI

Zoran Reljic, MSc Student, Laboratory for Geomatics and GIS Science (LAGGISS), Department of Geography, University of Ottawa, CANADA

BACKGROUND

Today’s applied research milieu requires fast information exchange between multidisciplinary team members, the public and policy makers. Traditional ways of communicating scientific results via static maps or graphs can be greatly enhanced through alternative representations that better approximate the human experience. 3D photorealistic visualization is one of the growing fields within the geographic sciences that address the current challenge to science to rapidly disseminate information while at the same time facilitating the mental absorption of complex earth-based datasets. 3D geovisualization offers new dimensions in the earth observation data interpretation that assist in the easy communication to the various interest groups.

THE CHALLENGE The advantages of geovisualization are recognized by the Canadian government. In an effort to preserve and monitor the ecological integrity of protected areas such as Canada's National Parks, and increase public awareness of the subject, several government agencies have joined forces through the Government Related Initiatives Programme (GRIP) and with the Laboratory for Applied Geomatics and GIS Science (LAGGISS) at the University of Ottawa, Department of Geography. For community outreach and education, one goal of GRIP is to produce 3D photorealistic dynamic animations to communicate the natural beauty of our national parks and to highlight ecological integrity using remotely sensed data from a variety of imaging sensors. GRIP is funded by the Canadian Space Agency and led by Parks Canada in partnership with Natural Resourced Canada and the Canadian Center for Remote Sensing.

STATE-OF-THE-ART ENVI SOLUTIONS The 3D visualization methodology is broken down in a number of stages, including topographic data manipulation, earth observation data sourcing, data pre-processing, identification of key environmental features and finally previsualization and photorealistic visualization. In all steps, the ENVI tools are indispensable. Here, examples of four key tools utilized in the 3D photorealistic visualization of the landscape and ecological changes of Auyuittuq National Park are presented. Vector Elevation Contours to Raster DEM. Data preprocessing is a necessary step for photorealistic animation. Elevation data in gridded formats are not always available in remote regions at sufficiently detailed resolutions. A starting point in terrain visualization is the production of a digital elevation model (DEM). The DEM is further used for draping satellite


Figure 1 Converting contours to DEM imagery and deriving shaded relief, in addition to its importance in the orthorectification process. Using the Convert Contours to DEM, a DEM for the Auyuittuq National Park was easily produced from the Canadian National Topographic Data Base (NTDB) at the scale of 1:50,000. Here, the new enhanced method in ENVI 4.1 for opening vectors makes the converting process even faster.

Data Fusion. Data fusion combines lower resolution multispectral datasets with higher resolution panchromatic data to increase the spatial resolution of the multispectral imagery (this is also called pan-sharpening). Fusion is necessary for the development of the majority of the photorealistic animations that use satellite imagery as base textures used as surface matials for DEMs. A high-resolution Landsat ETM panchromatic image (spatial resolution 15m) was used to enhance the spatial resolution of a natural color image [blue (band 1), green (band 2), and red (band 3)] (spatial resolution 30 m) covering Auyuittuq National park in Nunavut, Canada. ENVI data fusion tools resulted in a pansharpened, high resolution color composite with a spatial resolution 15 m. In this case, the image sharpening techniques used a hue-saturation value (HSV) transform to automatically merge the lower-resolution color and higher resolution panchromatic images in ENVI.

Figure 2 a- panchromatic image (15m spatial resolution), b-color image (30m spatial resolution), c-fused image (15m spatial resolution).


3D SurfaceView. To visualize terrain elevation prior to adding the fused imagery (Figure 2) in the texturing step, the DEM was displayed in the ENVI 3D SurfaceView TM. Easy to use and navigate, the 3D Surface ViewTM allows display of a DEM as a color, a wireframe or draped satellite image (Figure 3) which also helps to familiarize the user and the terrain features. Furthermore, to explore and determine the points of interest for a flight path over the Auyuittuq terrain, a set of comprehensive tools were utilized. The vertical exaggeration, real-time rotation and zooming, the ENVI’s annotation tool for interactively drawing of flight path, are some of the basic and easy to use tools - just to name a few.

Figure 3 a-color, b-wireframe, c-Landsat image draped, d- IKONOS image draped Band Math. Additional image processing related to Auyuittuq national park involved image ratios and the calculation of the normalized difference snow index (NDSI). The NDSI is useful to distinguish snow and ice from similarly bright features like clouds or rocks. It is calculated using Landsat TM2 (green band) and Landsat TM5 (mid-infrared band) as:


NDSI = 2 52 5

TM TMTM TM

−+

The Band MathTM tool Figure 4 was used to quickly perform image arithmetic and apply it to the particular bands opened in ENVI.

Figure 4 The Band Math tool

aa) Landsat TM 1991-07-12 b) Landsat ETM 2000-08-13 (Path/Row: 17/13) (Path/Row: 17/13)

Figure 5 NSDI images in Auyuittuq National Park obtained by utilizing Band Math. The white areas represent ice and permanent snow cover. The largest white area is Penny Ice Cap, one of the largest ice-caps outside of Greenland and Antarctica. The Landsat image that was processed with ENVI is used in the project to describe the glacierized and permanently snow covered area of the park. In the far north, monitoring the parks integrity in response to natural climate change requires monitoring of the snowline and glacier cover and changes therein. Areas of concern are those such as Penny Ice Cap (Figure 5 & 6). The massive Penny Ice Cap Figure 6, an area of solid ice over 300m thick that covers around 5100 km2 is at the heart of Auyuittuq NP. This area is of special


interest because it is a remnant of the Laurentide Ice Sheet that once covered North America 21,000 years ago.

Figure 6 A map of Penny Ice Cap in Auyuittuq NP made with ENVI’s QuickMap

The pattern of glacier cover and the topography in Auyuittuq are of interest for communicating environmental integrity in the park and thus lend themselves to 3D visualization whereby viewers can be brought through the valley and shown the various glaciers that are being monitored for changes. There are two major glacier types: ice caps that occupy inland mountain areas and valley outlet glaciers. Penny ice cap is composed of interlocking outlet and cirque glaciers forming highland ice fields Figure 6.

Monitoring glacial retreat is very important to both the local ecosystem and to the global environment. Analyzing historical photographs and data, scientists from the University of Ottawa and Geological Survey of Canada have suggested that some of the glaciers in Auyuittuq are retreating up to 8 meters per year. In order to make a 3D visualization depicting the state of the glaciers trough time Landsat TM from 1991 and Landsat ETM from 2000 were utilized with the ENVI software. A comprehensive set of the tools that are


easily accessible made the job much easier and faster compared to the other similar software.

a) b)

Figure 7 3D view of the part of the Auyuittuq NP showing the Fork Beard Glacier in the summer of 1991 (a) and the summer of 2000(b)

Landsat TM image source: http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsp Landsat ETM image source: http://geobase.ca/


Figure 8 a–Nerutusoq Glacier (Ikonos), b–Summit lake(Ikonos), c–NerutusoqGlacier with vector snow line and rivers, d-Summit lake with vector snow line and Rivers, e–Thor peakand Fork Beard Glacier

(Landsat 321 Composite), f–Crater lake (Landsat 321 Composite)


Limitations Since all geographic information have limitations due to scale, resolution, and time of acquisition they have various precision to which they depict shape, distance or other geographic characteristics. While every precaution has been taken in the processing of the data to ensure consistent horizontal references (e.g., datum conversions) it is important to note that because of the scale of the data, the horizontal errors incurred could be greater than the level of detectability of the changes we are hoping to monitor. For example, the maximum accuracy of the NTDB digital topographic data at the 1:50 000 scale is approximately +/-10 m vertically and +/-10-12 meters horizontally due only to the map scale. As such, the vertical and horizontal errors within NTDB data largely preclude the use of topographic map derived snowlines and permanent ice-cover from serving as a basis of comparison (e.g., Figure 8c). Landsat images can be easily processed (geocoded, orthorectified and combined with DEM data), but on the other hand they have limitations of monitoring small glaciers (spatial resolution) and seasonal coverage (time resolution). However, if processes are not controlled over the different imagery available horizontal errors due to changes in nadir and other parameters accumulate and propagate thus making inferences on small changes in glaciers, for example, difficult to assess when such changes are in the range of a few meters a year.

Conclusion

Throughout this project, the utilization of ENVI’s comprehensive package tools have been exceptional. The number of tools and applications, easy navigation through the tool pallets as well as high quality graphical user interface are making this state of the art software an indispensable tool for enhancing photorealistic visualization and contributing directly to the preservation and monitoring of natural environments and ecological integrity within the Canadian National Parks system.

Appendix 4 Canadian Institute for Geomatics 2005 Conference Paper 154

APPENDIX 4

Canadian Institute for Geomatics 2005 Conference Paper

Integrating GIS and 3D Visualization for Dynamic Landscape Representation in Canada's National Parks Zoran Reljic*, M. Sawada*, Jean Poitevin**, Greg Saunders*** * Laboratory for Applied Geomatics and GIS Science (LAGGISS), Department of Geography, University of Ottawa, Ottawa, ON K1N 6N5. www.geomatics.uottawa.ca **Applied Research Coordinator, Ecological Integrity Branch, National Parks Directorate, Parks Canada Agency, 25 Eddy Street, 4th floor (25-4-S), Gatineau, Quebec K1A OM5. ***Ecosystem Data Technician, Resource Conservation, St. Lawrence Islands National Park, Parks Canada Agency, 2 County Rd. 5, RR #3, Mallorytown, ON K0E 1R0

ABSTRACT In today’s world of multidisciplinary approaches and intensive information exchange, communication of geographic predictions and observations are not strictly limited to the community of Earth scientists. More often it is necessary to communicate these results to the scientist from other, non-related fields, policy makers, share and stake holders and the general public. Given that, humans perceive and absorb visualised information more effectively than numbers alone, the integration of visualization with geographic information science is an emerging tool for geographers to communicate the results of complex geographic models or observations in a novel and visually attractive way. In particular, the 3D approach to visualization, when appropriate, approximates the dimension of human environmental experience and therefore facilitates the absorption of complex contextual information. Therefore, the presentation of different landscape development scenarios using photorealistic, 3D dynamic landscape visualization can facilitate easier and effective yet scientifically-based decision making. The results of ongoing study integrating geographic information systems, remote sensing and photorealistic 3D visualization are reported. This integration is done for the benefit of communicating the utility of earth observation data for the monitoring of ecological integrity within Canada’s National Parks. We illustrate the complexity and efficiency of integration of GIS and 3D visualization for dynamic landscape representation. Key words: 3D visualization, dynamic visualisation, landscape visualization, La Mauricie, Auyuittuq, animation INTRODUCTION


Feu

Brulage dirigé

Surface de camping aménagé

0 4 82 Kilometers

4

Campground FIRE 30 May 2003 Park Boundary

There is a growing interest in the implementation of geovisualization in contemporary geography. Utilization of visualization techniques can enhance the understanding of systems and processes, and even reveal patterns in data that would otherwise be hidden – even to the most experienced researchers (Monmonier, 1990). In addition, geovizualisation is an effective tool for scientists to communicate their research results to different interest groups with different levels of knowledge (e.g. policy makers, public, farmers). Contemporary efforts in the preservation of the environment and species on our planet are demanding a better understanding of the factors affecting the ecological integrity of protected regions like Canada's National Parks. Currently, however, the interpretation of data from parks science is usually in the form static maps, leaving the observer of such data to use his/her imagination with regards to the landscape scale and environmental context as well as observed changes over time. The rise of geovisualization is offering new tools for investigation of the patterns of ecological changes that are not available in static representations. Ecological Integrity is defined for parks by the Canada National Parks Act as:

…a condition that is determined to be characteristic of its natural region and likely to persist, including abiotic components and the composition and abundance of native species and biological communities, rates of change and supporting processes. (Parks Canada 2004)

As such, monitoring environmental changes within national parks through time can illustrate the degree to which ecological integrity is maintained and also indicate areas where ecological integrity may be affected by human use or other environmental changes. In particular, both archived and currently available earth observation datasets are fundamental for monitoring changes within and surrounding Canada's National Parks. This is the main goal of the Government Related Initiatives Project (GRIP) led by the Canadian Space Agency in conjunction with Parks Canada, the Canadian Centre for Remote Sensing and the University of Ottawa, Department of Geography. A major objective of GRIP is education and outreach, that is, finding effective ways to communicate the science component to the public and policy makers. Doing so will illustrate precisely how earth observation data can be useful for monitoring ecological integrity. Here we present such communications using Auyuittuq National Park (Figure 1) in Nunavut and La Mauricie NP in Quebec (Figure 2).

Figure 1 Auyuittuq National Park Figure 2 La Mauricie National Park

0 30 6015 Kilometers Park Boundary


VISUALIZATION Since the dawn of civilization humans have been trying to create a graphic representation of the world around them. From the hunting scenes on the walls of Altamira cave (14000 B.C.) to the modern 3D models of earthquakes, jet engine combustion, and DNA replication, one thing is clear: An image is worth a 1000 words and literally a dynamic animation is thousands of images. The human brain processes the visual information much more efficiently than textual or audio (MacEachren, 1992). Today, the main objective of visualization is to enhance human cognition of complex multi-dimensional data and large datasets.

Scientific visualization was defined by its early developers as “first and foremost an act of cognition, a human ability to develop mental representations that allow us to identify patterns and create or impose order“ (MacEachren, 1992) but also as a method of computing, a “tool for interpreting image data fed into the computer, and for generating images from complex multi-dimensional data“ (McCormic et al., 1987). Currently several approaches are used to visualize objects in geographic research. The most common are static, dynamic, interactive, immersive and animation.

Static visualization. A typical static visualization in geography includes maps, plans, photos, perspective drawings, photomontage, or physical models where an object is seen by a static observer. Static visualization refers to the process of visualizing the state information of objects. This involves defining the objects under study and a finite set of states of the objects, classifying objects by their states, extracting state information from the original data set, finding the appropriate way to present and explain the results (Lange, 2001). Dynamic visualization. Change is a fundamental characteristic of processes in nature and interactions among them (Goud, 2004). Thus, static representation cannot depict the true characteristics of such a dynamic system. Vegetation in nature grows, change with seasons and eventually die. Realistic representation of trees for example, even in static visualization is a challenge due to various levels of details that are necessary. Another issue is the response of plant communities to simple terrain variables, such as elevation, slope, and aspect. In some commercially available software it is possible to specify which plants are to be found in which ranges of elevation, slope, and aspect, and then corresponding images textures are used to generate a rendering (Ervin, 2001). Interactive visualization. In this approach, not only there is a dynamic linking between the graphical user interface with the underlying geospatial data but also with the end-user. The result is the change in virtual scene as a response to changes in data or end-user actions. Such environment Goodchild has named as “user-centric geographic cosmology” (Johannson, 2000). These interactive environments could be used as a tool to visualize and communicate the results of various scenarios to the interest groups who in turn can interact with these scenarios, relate to it and move around it in order to facilitate their decisions (Batty et al., 2000). Animated visualization. Animation is the creating a timed sequence or a series of graphic images or frames together to give the appearance of continuous movement. Surprisingly or not, the driving force for the development of animated landscapes was/is the video game industry. Here, the advancement in animated landscapes are already so advanced that game engines have become a basis for scientific landscape visualizations (Herwig and Paar, 2002). However, there is more to animation in geovisualization than landscape. Animations are “scale models in both space and time” and as such are potentially powerful tools for depicting


change in information (Monmonier, 1990). Moreover, an animation integrates the two major senses of sight and sound together to full effect. Immersive visualization. Immersion implies feeling of “being inside” the virtual environment on the side of the end-user (MacEahren et al., 1999). Here, the user manipulates virtual objects as in the real world as opposed to pointing, clicking or typing (Bajwa and Tim, 2002). Most of this feeling of “being in the virtual world” comes from stimulation of different senses in the real world (i.e. sound, visual, touch via feedback and smell). The degree of stimulation will influence the degree of immersion in the virtual environment. The major research interest is to find which display characteristics of the virtual world are inducing this sense of “being in” (MacEahren et al., 1999).

Figure 3 Methodology and process flow for scientific visualization

METHODOLOGY OF PHOTOREALISTIC VISUALIZATION In this project, the objective was to produce 3D photorealistic dynamic animations using earth observation datasets for Auyuittuq National Park and La Mauricie National Park that illustrate the utility of using remotely sensed data for monitoring ecological integrity. Several steps were used to create 3D dynamic animations. The general stages included searching for remotely sensed and topographic data availability, followed by data pre-processing with GIS and remote sensing software, development of textures and texture maps, OpenGL based story-boarding and pre-visualization and then final rendering (Figure 3). We utilize animation for the visualization of scientific and landscape principles. The process of animation follows from the processes used in script-writing and film-making, however, the script is equivalent to the scientific principle, the actors are the datasets/objects and the process of filming is done within the computer. The elements of scientific visualization are presented in Figure 3. The four main steps are development of scientific concept, modelling of the concept, animation of the concept and rendering. Scientific visualization is an iterative


process where the final photorealistic visualization is assessed for visual artefacts and other problems and the process is re-iterated until the scientist/director is satisfied.

a) b) c)

Figure 4 Example key frames for different visualizations, a) ice-berg near Pangnirtung Fjord; b) Snow

accumulation in Auyuittuq; c) Crater Lake in Auyuittuq with glacier.

One fundamentally necessary step is to relate the scientific concept to a previsualization storyboard (Figure 3). For example, the relation between a glacier and proglacial lake such as Crater Lake in Auyuittuq NP may be one such ecological/geomorphologic process that can be monitored using EO data (Figure 4). A storyboard is constructed that determines the motion and timing at key points of interest that illustrate the ecological relationship. This is a visual process by which key-frames are established and rendered like the examples in Figure 4. With these key frames and timing established, an OpenGL based previsualization or “previs” is created for direction purposes. Figure 4a and 4b are other examples of the establishment of key-frames.

However, before the data is visualized (Figure 3), considerable pre-processing in both GIS and image analysis software is required. LANDSAT and IKONOS datasets for Auyuittuq National Park as well as SPOT 5 for La Mauricie NP formed the basis of texture maps within our 3D photorealistic animation software. For both parks texture maps from the earth observation datasets were used for data fusion in order to combine lower resolution multi-


spectral datasets with higher resolution panchromatic data to increase the colour-based spatial sharpness. For example, our remotely sensed imagery was processed within PCI Geomatica 9 and ENVI 4.1 where image enhancements were made via various stretch algorithms. Effective texture development for 3D environments requires the use the latest image processing tools. Textures are either materials created within a 3D package or image maps or a combination of both that are applied to the surface of 3D objects. The materials composing a given texture can have different ambient, diffuse and specular properties and thus respond in different ways to light. For example, sedimentary rock like shale would have lower specularity than say a metamorphic rock like slate. Likewise leaves of different trees or species will have different reactions to ambient, diffuse and specular lighting (Figure 5).

a) b)

Figure 5: Textured vegetation, a) Visualization of a single tree using 3ds Max; b) textured ground

simulating grass and a forest canopy using Vue 5 Infinite* ecosystem generator.

For base textures we developed our image maps first within image processing software which allows for such processes as automatic detection/removal of hot spots, automatic radiometric color balancing between overlapping images and global optimization over the entire mosaic, and the automatic cut-line determination to minimize visibility of seams was crucial. Cut-lines and balancing are stored for review or adjustment. Automatic mosaicking in PCI reduced interaction and wait-time and thus streamlined the production workflow of texture pre-processing. The textural development process requires describing to the computer the visual and optical properties which differ among vegetation, water, soil, rock etc. These and other considerations were taken into account in the development of textures. In Auyuittuq, the pan-sharpened Landsat image was fused with an IKONOS image of the main valley to provide more detail in that area. Likewise in La Mauricie, the SPOT 5 10 m data were fused with the 5 m panchromatic channel and false colour images were created in addition to fusion of Landsat data for the same region. ArcGIS was used to clip the CDED Level 1 DEM to areas corresponding to our processed imagery before importation into the visualization software.


RESULTS AND DISCUSSION The first step was the utilization of a DEM and other models for definition of the terrain inside visualization software like 3D Studio Max*. To illustrate the potential of our approach to communicating ecological integrity, different regions of Auyuittuq National Park in Nunavut (Figures 6 and 7) and La Mauricie National Park (Figure 8) were visualized.

Figure 6 An example of a terrain generated and showing a view of Auyuittuq National Park. This terrain utilizes an IKONOS image as a base texture with snow distribution added procedurally based on slope and elevation.

Figure 7 A terrain visualization example of the Summit lake region of Auyuittuq National Park in Nunavut using IKONOS and pansharpened and fused Landsat datasets as a texture base.

* Vue 5 Infinite is landscape visualization software for photorealism from e-on Software, www.e-onsoftware.com. 3D Studio Max or 3ds Max is created and distributed by Discreet www.discreet.com and educational licensing is managed by Torcomp Systems www.torcomp.ca .


It can be seen that the extent or realism achieved with the software is acceptable for the intended objectives of the project. 3D Studio Max is capable of handling large elevation models such as mountains or portions of the continent. For La Mauricie, the SPOT 5 and Landsat datasets were used to assess the health of vegetation within the park. Vegetation and canopy density can be affected by a number of factors ranging from natural processes, disease and insect infestation to human use. Visualizing earth observation data in 3D can help communicate the utility of such data for monitoring and identification areas undergoing variations in ecological integrity. For example, using colour composites and the normalized differenced vegetation index (NDVI) in La Mauricie, we were able to clearly delineate areas with abnormal vegetation characteristics (Figure 8). CONCLUSIONS In general for both parks, the key environmental values and processes were visualized. The fact that the scientific visualization is gaining popularity among various governmental agencies such as Parks Canada has offered a unique opportunity to test the relevance of animation as a means of effective communication of earth observation science. Given the advances in the geovisualization and CPU power it is necessary to utilize these tools for better understanding of the systems and process in the nature. Visualization of the scientific data offers a new, dynamic dimension in the search for the patterns and relationships among the data. In addition, communication of the results to the scientist from other, non-related fields, policy makers, share and stake holder or simple, general public was simplified and presented in a visually appealing display that enables fast identification of the processes of interest in these unique ecological surroundings.


a)

c)

e)d)

b)

Figure 8 a) 2004 SPOT 5 false colour composite of campground near entry of parkway in La Mauricie National Park. The red area indicates smaller spectral response to vegetation structure/canopy; b) NDVI derived from SPOT 5 image where dark colours indicate less green vegetation; c) 1999 Landsat TM NDVI for same region illustrating darker colours around campground. Note that the SPOT 5 and Landsat derived NDVI are not radiometrically equivalent so the intensities are not directly comparable; This area of the park has had problems with spruce budworm infestations over the past years. d) Same as a) but illustrating an area razed in 2003; e) same as d) but for 1999 Landsat true colour.

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