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Geocollaborative Soil Boundary Mapping in anExperiential GIS EnvironmentTrevor M. Harris & Paddington HodzaPublished online: 14 Mar 2013.

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Introduction

Soil maps are an integral part of contemporary digital information infrastructures. The use and application

of soil maps has expanded well beyond their traditional role in agriculture and now underpin many domains in engineering, environmental resource management, urban planning, hazard mitigation and others. Soil map production, however, remains a time consuming process and is a challenge to providing timely, up-to-date, and reliable soil information. This paper proposes the use of an innovative Experiential GIS (EGIS) environment that draws on immersive three dimensional (3D) graphical displays coupled to a GIS to allow soil scientists to develop critical soil-landscape models and delineate soil boundary

Geocollaborative Soil Boundary Mapping in an Experiential GIS Environment

Trevor M. Harris and Paddington HodzaABSTRACT: Soil maps are an important and integral component of national geospatial data infra-structures. The creation of these maps involves a geovisualization exercise whereby soil scientists develop cognitive models that correlate observable landscape features to soil occurrence. This is traditionally an analog process, cognitively demanding, time consuming, and invariably non-collaborative. A new geovirtual soil mapping technique is proposed in this paper in the form of an innovative Experiential GIS (EGIS) environment. This immersive environment enables soil maps to be constructed through experiencing and interacting with spatial data through immersion in 3D geovirtual scenes. The system outlined integrates GIS, immersive geovisualization, and robust geodatabase capabilities. Four soil scientists with extensive soil mapping experience ranging from 5 to over 20 years are concurrently immersed in the same 3D geovirtual landscape which more closely mirrors the way in which we view the world around us. The soil scientists are immersed within the 3D scene where they are essen-tially freed from the laws of physics, and may roam anywhere across the landscape as if in a virtual helicopter. The landscape is draped with any combination of orthoimagery and GIS-derived data allowing soil scientists to interpret, digitally delineate, and attribute soil boundaries. Exploiting the EGIS technology while maintaining the centrality of the soil scientist in soil interpretation and soil map production, promises considerable resource efficiencies than those achieved in traditional soil survey. The paper lays out the nature of this potential paradigm shift in soil mapping. The results of using this technology to construct soil geographic knowledge are also discussed in terms of soil map detail, cost efficiencies, time effectiveness, system usability, geocollaborative soil mapping advantages, and the reduced cognitive workload on practicing soil scientists.

KEYWORDS: experiential GIS, soil boundary mapping, immersive geovisualization, geocollaboration

Cartography and Geographic Information Science, Vol. 38, No. 1, 2011, pp. 20-35

maps in a more efficient, time-saving, and collaborative mapping environment.

Currently, a number of methods, ranging from traditional soil survey to digital soil mapping, are used to identify, delineate and describe soils. Regardless of the chosen method, accurate soil mapping requires field observation, laboratory measurement and the all-important soil scientist’s knowledge. Traditional digital soil mapping focuses on creating reliable and replicable soil maps using numerical models to infer the spatio-temporal distribution of soil classes and soil properties (Hempel et al. 2008; Weber et al. 2008). These predictive models are often based on conventional statistics such as regression (Gessler et al. 1995; McKenzie and Ryan 1999; Bell et al. 2000), geostatistics including kriging (Odeh et al. 1994; 1995) and artificial intelligence inference engines (Skidmore et al. 1991; 1996; Zhu et al. 1997; 2001).

Although predictive soil modeling can enhance the speed of soil mapping, traditional soil mapping based on the stereoscopic interpretation of aerial photographic pairs remains the predominant

Trevor M. Harris, Department of Geology and Geography, West Virginia University, WV, 26506-6300, USA. Paddington Hodza, Department of Geography and Environmental Studies, University of Colorado at Colorado Springs, CO, 80918, USA, E-mail: <phodza@uccs.edu>.

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approach to soil mapping (Scull et al. 2003). This approach is primarily a geovisualization exercise in which soil scientists use maps, imagery, and spatial data to develop cognitive soil-landscape models. The most common cognitive modeling process involves the stereoscopic interpretation of two-dimensional (2D) analog stereo photos augmented by field investigation. This process is labor intensive, time consuming, and largely non-collaborative. Only one soil scientist at a time can effectively use a stereoscope to conceptualize the soil-landscape model. Although the soil scientist perceives a 3D terrain model, the model has to be manipulated in the mind inducing a significant cognitive load. Soil mapping in this mode is thus highly individualistic and relies heavily on the cognate and interpretive skills of the soil scientist. Surprisingly, only one soil map at any given scale is given to the user community despite several possible soil perspectives for an area. Generating multiple soil maps that reflect interpretive differences between soil scientists is prohibitively expensive and would contribute to an unacceptable soil map refresh cycle of close to a century (Soil Survey Division Staff 2005).

This study seeks to advance the popular human-centered approach to interpretive soil mapping through a digital, collaborative, immersive and explicitly 3D methodology. The approach recognizes the persistent centrality of the soil scientist as human interpreter in soil mapping by enhancing the scientist’s cognitive ability to visually interpret and analyze digital graphical displays. The study elevates geovisualization as an integral and valuable part of soil mapping and draws upon an innovative Experiential GIS (EGIS) that reduces the gap between the computer’s logical model and the user’s cognitive semantic world.

The EGIS environment is a loosely-coupled system comprising geovisualization, GIS, virtual reality (VR), and robust geodatabase, supported by a stereoscopic 3D Cave Automatic Virtual Environment (CAVE). Within this immersive system, meaning and sense are constructed through experiencing and interpreting 3D geovirtual scenes draped with remotely-sensed imagery and GIS-derived data. A user immersed in the EGIS environment is no longer exogenous to geospatial data but immersed in a geovirtual world where the user experiences

a psychophysical feeling of ‘being there’ and ‘being inside’ a digital recreation of a real-world environment with real-time sensory and motor feedback (Harris and Baker 2007; Hodza 2009). It is contended here that this immersive sensation provides for a greater in-depth understanding of the terrain and soil-landscapes that results in highly accurate soil maps.

To test the effectiveness of the proposed system, four soil scientists were concurrently immersed in the EGIS environment to collaboratively create a soil map of a specified area. The geocollaboratory essentially coalesces multiple soil scientists’ viewpoints into the soil map. While the EGIS environment could equally be applied to soil mapping in any part of the world, and indeed may be especially advantageous in areas where access to land is highly problematic, this article focuses on the United States (US) where the authors have experience of the soil mapping process. This is not to suggest that the methodology outlined here would not be appropriate or extensible to other worldwide soil mapping projects and indeed would likely generate even greater resource efficiencies than detailed here. To this end the paper first discusses the manner in which soil maps are currently produced and the challenges involved in meeting national soil map needs. Since the EGIS draws on several geospatial technologies, these technological themes are discussed as they define and impact the nature of an immersive 3D mapping system. Drawing on these themes the components and architecture of the EGIS are then described and the psychophysical nature of experiential immersion is discussed. Following testing of the system by soil scientists the effectiveness of the system and the quality of the soil map produced in the system are assessed based on the metrics of map comparison, a binary logic confusion matrix, and qualitative responses from the soil scientists.

Traditional Soil Survey Practice

Traditional soil survey is grounded in the para-digm of the soil-landscape relationship prescribed in Jenny’s (1941) model of soil formation in which the soil-landscape relationship defines a unique physical terrain unit with specific location, areal

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extent, and soil and landscape attributes (Schaetzl and Anderson 2005). Soil is created through the interplay of several factors including climate, organ-isms, relief, and parent material all operating within a temporal domain (Jenny 1941). Soil is mapped in three major stages comprising (1) field observation, (2) the creation of mental models that relate soil variation and observable environmental features, and (3) inferring soil classes and soil properties from these models (Cook et al. 1996).

In the first stage, soil scientists traverse a survey area observing the variation in soil forming fac-tors and soil profiles at selectively dug pits. The soil pits generally expose less than 0.001% of the subsurface soils in an area (Burrough et al. 1997; Hudson 1992; Scull et al. 2003). This proportion is arguably too small to accurately delineate and describe the survey area soils and thus soil pits serve as an aid in the soil mapping process. Difficult terrain, restricted access to private land, and the high cost of field survey also make it infeasible to visit every location in a survey area. Thus field observation is augmented by analog stereo photo interpretation and the analysis of soil forming factor datasets. These two exercises inform the second stage of soil-landscape mental modeling.

In spite of an unknown level of uncertainty, soil-landscape mental models are used in the third soil survey stage to infer the geospatial patterns of soil distribution (Cook et al. 1996; Scull et al. 2003). The resulting soil boundaries are inferred from and are delineated on 2D aerial photos and become the cartographic representation of the modeling

process. Depending on the soil survey type, the soil boundaries tend to be treated as preliminary soil map units that require field verification or as final products. The extent of this field check-ing is determined by the soil survey ‘order’ that ranges from 1 to 5 (Table 1) (Soil Survey Division Staff 1993). Order 1 is the most detailed and verifies all soil boundaries through field traverses and transects while Orders 4 and 5 field check only select boundaries. Most soil maps define well delineated soil map units (Burrough 2007). The implied accuracy arising from such line-work has been criticized for obscuring fuzzy soil boundar-ies and the spatial autocorrelation that lies within and between soil classes (Burrough et al. 1997; Rossiter 2001). For this reason, knowledge-based geospatial models are increasingly used to predict continuous soil properties using fuzzy modeling, geostatistical analysis, expert systems and digital terrain analysis (Moore et al. 1993; Skidmore 1996; McBratney et al. 1997; 2003; McKenzie et al. 2000; Zhu et al. 2001; Scull et al. 2003; Shi et al. 2004; Lagacherie et al. 2007). While fuzzy soil boundaries more closely mirror soil scientists’ perception of soil variability and reality, fuzzy soil maps have yet to become popular with the general user community.

Despite being the dominant method for soil mapping the use of stereo photos to manipulate terrain and soil-landscape relationships in the mind is physically and mentally draining. The soil sci-entist constantly hand-configures, stereoscopically interprets, and hand-draws soil boundaries on the

Order Level of data needed

Minimum-size delin-ation* (hectares)

Appropriate scales for field mapping and publications

1 Very intensive (i.e., experimental plots or indi-vidual building sites.) 1 or less 1:15,840 or larger

2 Intensive (e.g. general agriculture, urban plan-ning.) 0.6 to 4 1:12,000 to 1:31,680

3 Extensive (i.e., range or community planning.) 1.6 to 16 1:20,000 to 1:63,360

4Extensive (e.g., general soil information for broad statements concerning land-use potential and general land management.)

16 to 252 1:63,360 to 1:250,000

5 Very extensive (e.g., regional planning, selections of areas for more intensive study.) 252 to 4,000 1:250,000 to 1:1,000,000

or smaller

Table 1: Orders of soil surveys (adapted from Soil Survey Division Staff (1993: p48).* This minimum size delineation could be larger in practice.

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photos in a concurrent manner. Since only one scientist is involved in this interpretive exercise, only one perspective of the survey area soils is incorporated into the map. The non-rectified aerial photos also lead to feature mismatch prob-lems when the soil boundaries are extracted and integrated with other geospatial data (Bell et al. 1999). Often the soil boundaries must be ‘stitched’ together to create seamless soil maps with the likelihood of edge-matching errors. Maintaining soil boundary consistency across mapped areas is difficult because of differing expertise, the extent of familiarity with the survey area, and the vary-ing knowledge and interpretive judgments arising from multiple contributing soil scientists (Dobos et al. 2001; Bathgate and Duram 2003). Despite efforts to systematize this process and seek objec-tivity through rigorous training (Hudson 1992), traditional soil mapping falls between the twin stools of being both a science and an art (Hewitt 1993). This view does not, however, minimize the “scientific value” of the soil knowledge created via this approach (Cook et al. 1996).

Given the magnitude of national soil mapping initiatives, methods that contribute to digital map-ping efficiencies such as GIS, improved geospatial data, and superior interpretive capabilities can greatly impact the quality and productivity of soil mapping projects. This paper contends that EGIS provides significant advantages to the soil mapping process that contributes to high quality soil maps, production efficiencies, and a lessened burden on soil scientists to interpret basic landscape and soil forming features.

Geovisualization, GIS and Geocollaboration

Traditional soil surveys draw heavily on the visual senses. In the digital world, geovisualization augments these cognitive abilities through powerful graphical displays. Visualization assists in creating and interpreting mental models that underpin knowing (Kraak 2003; Cartwright et al. 2004; MacEachren et al. 2004). The field of geovisualization is still forming and draws upon concepts from cartography, cognitive science, usability engineering, scientific visualization, computer science and VR (MacEachren et al. 1999a; MacEachren 2001; Dykes et al. 2005; Edsall and Sydney 2005). Geovisualization

transforms geospatial graphical displays into two-way communication interfaces where the user is no longer a passive observer but an active participant controlling the presentation of data in real-time (Fairbairn and Parsely 1997; Crampton 2002). The ability to control the appearance of geospatial scenes and the user’s perspective offers greater insight into data than is provided by fixed views and static displays (Peuquet and Kraak 2002). Emerging from this framework is the potential use of immersive technologies to place the user in a geovirtual world that enhances human interaction and understanding of complex geospatial data. (Edwards 1992; Biocca 1997; Dykes et al. 1999; MacEachren et al. 1999b; Qui and Hubble 2002; Ramasundaram et al. 2005).

A geovirtual environment is a 3D enabled digital system that offers a life-like perceptual experience that engages all the user’s senses in the interpretive process. (Neves and Camara 1999; van Dam et al. 2000; Fisher and Unwin 2002). Immersive geovisualization facilitates a more intuitive approach to visual data exploration and analysis (Hodza 2009). Current immersive visualization tools offer limited geospatial analysis functions (Huang et al. 2001; Longley et al. 2005). These functions are the domain of GIS whose primary form of geovisualization is 2D maps and 2.5D wireframe displays. The pseudo 3D displays provided by GIS such as digital terrain models (DTMs) have become very popular because they more closely mirror the real-world and because they are more effective in solving complex landscape problems than 2D displays (van Driel 1989).

Little work has been undertaken to date to link immersive geovisualization and GIS although VR and GIS have previously been coupled (Koller et al. 1995; Huang et al. 2001). Since the first VR-GIS application depicting Georgia Tech Campus (Faust 1995), projects involving GIS and VR have involved urban planning, environmental impact studies, landscape visualization, landcover change analysis, data exploration, data mining, and field investigation (Cook et al. 1998; Hurst 1998; Brown 1999; Dykes et al. 1999; Huang and Lin 1999; Verbree et al. 1999; Batty et al. 2001; Appleton et al. 2002; Haklay 2002; Qui and Hubble 2002; Wachowicz 2002; Edsall and Larson 2006). While many of these applications

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offer limited immersive capabilities, they have generally received positive user feedback especially concerning navigational control and the real-time ability to generate multiple views of geospatial phenomena.

VR and geovisualization tools have been explored in soil science primarily to display and interact with 3D soil-landscapes (Grunwald et al. 2007). These desktop applications are limited in user immersion and presence. Mendonca Santos et al. (2000) used finite-element modeling and GIS to display the geospatial distribution of soil horizons in 3D. Grunwald and Barak (2003) created web-based Virtual Reality Modeling Language (VRML) 3D soil-landscapes to show the geospatial distribution of bulk density and the variation of soil horizons with topography and offer an intuitive way to understand subsurface soil. The 3D models proved effective in laboratory exercises designed for students to explore environmental processes such as water movement through soil (Ramasundaram et al. 2005). To date, we are unaware of any interpretive soil mapping project that has exploited these digital technologies to identify, delineate and describe soil properties.

More recently geocollaboration has emerged as a research area in which geospatial technologies are viewed not only as important ‘tools’ for problem solving and decision-making but also as ‘communication interfaces’ connecting collaborating users (MacEachren and Brewer 2004; Schafer et al. 2005). Most geospatial tools are fashioned for single workstations and problems emerge when these tools are used for geocollaboration (MacEachren and Brewer 2004; Chen et al. 2008). In same-place and same-time group work only one user can occupy a desktop computer and interact in an ergonomically-sound and effective manner with the system via conventional keyboard and mouse. Although large multi-touch displays such as those developed by Perceptive Pixel (http://perceptivepixel.com) can support concurrent multi-user interaction, the displays do not facilitate the experiential engagement with data as espoused in this study. Nonetheless, geocollaboration applications continue to grow and include information dissemination, urban planning, participatory mapping and policy formulation, environmental management and decision-

making, and disaster management (Jankowski and Nyerges 2001; Li and Coleman 2002; Cai et al. 2005; Chang and Li 2005; Balram and Dragicevic 2009; Carton and Thissen 2009; Jankowski 2009; Simao et al. 2009).

Experiential GIS

The heart of the EGIS environment lies in the technological fusion of GIS and immersive geovisualization. Such a system can support many application uses but is exploited here to aid soil mapping. Here we offer only a brief descrip-tion of the system’s architecture though a more detailed discussion of the design architecture can be found in Hodza (2009). The EGIS environment comprises three loosely-coupled and reusable mod-ules comprising GIS, geodatabase, and immersive geovisualization components. The modules are implemented on three hardware platforms in a three-tier client-server architecture. Through these modules, the EGIS environment is able to sup-port (1) user immersion, (2) extensive user-system interaction, (3) the creation, editing, storing and management of geospatial data, (4) 3D stereo dis-play, (5) geospatial and geovisual analysis and (6) geospatial knowledge creation. A user immersed in the EGIS environment can interact with, and experience, the 3D geovirtual scenes as if they were real. The user is no longer exogenous to the map but is an active participant immersed within the map. Data are created and edited with a geospatial editor within the GIS module that is implemented on a Tablet PC and through which the digital pen interacts with the display and the system. Although one geospatial editor was used here, the EGIS environment can support multiple concurrent user editors.

A CAVE called FLEXTM manufactured by Fakespace, Inc. (www.fakespace.com) provided the immersive visualization module. The FLEXTM

is a room-like structure in which four computer projections are seamlessly displayed onto three walls and the floor to create a full-body immersion environment with real-time sensorimotor experience (Figure 1). This active stereo system is controlled by a PC cluster comprising a master computer and four slave computers that manage the four seamed displays and implement user navigation and interaction with the display. A user wearing a head tracking device and using a hand-held wand can easily navigate the displayed scene. Multiple users wearing active stereo glasses may be immersed concurrently in the 10ftx8ft FLEXTM.

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The immersive experience creates a psycho-physical conviction that detaches the user from the real-world of technology and renders the user present in a virtual environment that closely mir-rors reality. The sense of immersion and presence in a scene is created by deceiving the user’s senses with computer-generated psychological cues such as depth perception, peripheral eye coverage and force feedback. This experiential immersion greatly enhances a user’s analytical capability and per-formance in problem-solving.

Wired and wireless networks connect the EGIS modules and the geodatabase module is imple-mented on a rack-optimized server. The CAVE assumes the role of a ‘thin’ client while the GIS module performs most of the geocomputational work acting as a ‘thick’ client. Either the Tablet PC or FLEXTM module can become a server when these components directly interact with each other.

The core functionality of the EGIS environment draws on the powerful geovisual and geospatial functions of commercial-off-the-shelf (COTS) soft-ware. Specifically, the ArcGIS suite of GIS software modules including ArcMap and 3D Analyst, and SAS statistical and mathematical analysis software were used in this study. In addition, SQL Server

and ArcSDE were used to pro-vide robust geodatabase system functionality.

GeovirtualSoil BoundaryMapping

Based on research undertaken by the authors it is suggested that an EGIS environment offers a sig-nificant opportunity and resource to support soil mapping. While this system could be applied to update and improve existing soil maps, this study focuses on the more difficult task of identifying soils in an unmapped area. In this ‘once-over’ soil mapping enterprise, one female and three male soil scientists with an average age of 39 years and with extensive soil mapping experiences ranging from 5 to 20 years, were jointly involved in this geovirtual soil

mapping exercise.Two days before the mapping exercise began,

the soil scientists received training on the use of the EGIS. Not surprisingly, the four scientists had no prior exposure to a CAVE or immersive geovisualization system although two had previously used ArcGIS. In addition to a basic introduction to the EGIS technology, the training program included comprehensive hands-on practice in (1) navigating the geovirtual environment through flight simulation supported by head-tracking and hand-held wand devices, (2) altering display scale and manipulating viewing parameters such as angle, contrast, position, and zoom to better discern subtle landscape features, (3) drawing and editing soil boundaries using a digital pen and (4) digitally attributing soil map units.

The study area

A study area was selected for mapping of which the soil scientists had no prior working experience though they were generally knowledgeable of the soil geography of the region. This area is located in Tanner within Gilmer County in West Virginia and extends for about one square mile (Figure 2). The region has complex topography and soils and was chosen to provide a realistic soil mapping challenge to the soil scientists. High resolution digital orthoimagery and a digital elevation model

Figure 1: Soil scientists geovisualizing different soil types in an EGIS environment. Digitized soil bound-aries are shown in yellow (Source: Hodza (2009))

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(DEM) are available for this area along with a pre-existing Soil Survey Geographic (SSURGO) database soil map published in 2004. To preclude bias in the mapping process, the 2004 soil map was not made available to the participants but was used to provide an evaluative metric for the geovirtual soil map generated by the soil scientists.

The hydrological drainage of Tanner consists of the Little Kanawha River that flows through an

irregular plain of low to moderate relief (Figure 2). Elevation varies greatly in this area ranging from 700m in the valley of the Little Kanawha River to 1300m in the hills located to the south west. The topography is characterized by hills with moderate to steep slopes that in some places reach gradients of over 35% (Figure 3a). The geology consists of shale and sandstone rocks dominated by the Dunkard, Monongahela and Conemaugh

Figure 2: (left) Location of Tanner study area within Gilmer County in West Virginia; (right) Orthophoto (Source: West Virginia Statewide Addressing and Mapping Board (2003); Scale: 1:4800)

Figure 3: (a) Slope map, and (b) Surficial geol-ogy drapped over orthoimagery

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Groups deposited between 280 and 320 million years ago (Figure 3b) (Pate 2004). The shale is not extensive and most of the area is comprised of sandstone though some coal deposits are to be found in the area.

Data and methodology

A number of steps were involved in the geo-virtual mapping process involving (1) developing a comprehensive soil mapping geodatabase, (2) configuring the soil boundary drawing environ-ment, (3) conducting preliminary landscape analysis, (4) exploring, interpreting and analyzing the soil-landscape model within the 3D geovirtual envi-ronment, (5) digitizing, editing and finalizing soil boundaries using the Tablet PC, (6) evaluating the soil map product, and (7) assessing the soil scientist experience of soil mapping in the EGIS environment. Part of the latter evaluation involved observing soil scientists as they carried out soil mapping tasks, and completing a questionnaire and exit interview at the conclusion of the map-ping exercise.

When mapping a new area, soil scientists examine existing soil information for nearby regions and identify the soil nomenclature likely to be found in the area. A standard nomenclature such as the USDA Soil Taxonomy or FAO-UNESCO system assists in creating consistent seamless soil maps across extensive areas. The soil scientists were given access to a published soil map and its legend for

a one mile square area that is approximately one mile from the Tanner study site along with the soil series and soil morphology of Gilmer County. A digital geodatabase of the study site was also created and populated with data including 3m DEM, 60cm orthoimagery, 30m landuse-landcover, and surface geology - all of which are normally used in the development of soil-landscape models.

The geospatial editor user interface was custom-ized for frequently used feature digitizing and attri-bution tools to facilitate ease of use. Appropriate snapping tolerances for digitizing soil boundaries were also set and feature topology rules such as ‘polygons must not overlap’ were defined to assist in error detection.

Wearing stereo glasses and with the head-tracking device in place, the soil scientists had complete navigational control over the study area landscape and could position themselves at any location and elevation and obtain any perspective viewpoint at will. The soil scientists were as if in a virtual helicopter that could navigate to anywhere in the geovirtual environment with unrestricted views of terrain features and with the added assistance of seeing GIS-derived data draped on the 3D terrain surface. Orthoimagery that included natural-color and color-infrared, along with DEM derived slope and hypsography could be overlain onto the ter-rain model at will. To increase interpretive ability and to better perceive subtle landscape features, the soil scientists exaggerated relief by altering the vertical terrain scale.

Figure 4: Soil scien-tists discussing soil boundary delineation in the EGIS environ-ment. The scientist on the right is wear-ing the head tracking device.

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The experience of immersion in the 3D geovir-tual environment is not easy to describe in print form but is similar in some ways to experiencing a combination of entertainment systems such as those found in 3D film theatres and large-format special venue film theatres produced by IMAX Corporation and the SOARIN’TM exhibit in Walt Disney World’s EPCOT theme park. In the EGIS, visual acuity is greatly heightened and more closely reflects the way in which we experience the real-world, albeit freed from the natural laws of physics and with the freedom to roam anywhere.

As the soil scientists navigated the geovirtual environment via dynamic fly-through simulations, they reported a significantly improved ability to observe, interpret and analyze the patterns, varia-tions and relationships in soil forming factors that previously had to be inferred from 2D stereo pairs of analog aerial photos (Figure 4). As a geocol-laboratory, the scientists developed a collective soil-landscape mental model which provided the basis for delineating soil boundaries. As these soil boundaries were identified, so the line-work was drawn on the Tablet PC and immediately displayed in 3D in the CAVE for further discussion, critique, validation and subsequent realignment if deemed appropriate. Although one soil scientist digitized most of the soil boundaries, all the soil scientists

were involved in analyzing and interpreting the displayed landscape scenes.

After drawing the soil boundaries and checking for errors, the soil scientists tagged the resulting soil map units with taxonomic properties including symbology, acreage, parent material, and slope gradient. The created geovirtual soil ‘pre-map’ was stored in the geodatabase. As in traditional soil mapping, this map can be verified in the field to confirm or modify soil interpretations and a final soil map then made for official publication.

Results

The EGIS soil map product

Figure 5(a) shows the EGIS soil map. The map consists of twenty soil polygons classified into nine soil map units or soil classes (Figure 5a and Table 2). These map units are further broken down into three soil complexes (GpF3, GuD3 and GuE3), five soil consociations (Cg, Ha, MoB, VaD and VsE) and one miscellaneous land type (W). Each map unit and its soil series are described in detail by Pate (2004) and at http://soils.usda.gov/technical/classification/osd/index.html.

Tables 2 and 3 itemize the attributes of the dif-ferent soil map units. The areal extents of these map units range from 4 acres (i.e. MoB) to about

Figure 5: (a) EGIS soil map (Source: Hodza (2010)); (b) Official soil map (Source: Pate (2004))

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244 acres (i.e. GpF3) (Table 2). Seven of these map units are also found in the neighboring area for which a pre-existing soil map was given to the soil scientists at the onset of the geovirtual soil mapping. The two soils that were delineated only in the study area are MoB and VaD.

EGIS soil map quality

Because of the extensive discussions that took place during the mapping process, the considered opinion of the soil scientists was that this map was sufficiently accurate to qualify as a very creditable Order 2 or Order 3 soil survey product publish-able at a scale of 1:24000. The quality of the geovirtual soil map was also assessed by the soil scientists to have been at least comparable to the official SSURGO soil map (Figure 5(b). Neither the official map nor the EGIS map underwent independent ground truth and this impedes any attempt to estimate the absolute accuracies of these maps. For this reason, the relative accuracy of the EGIS generated soil map is assessed based on its difference from the official soil map (Hodza 2010). Three measures of map quality evaluation were used to estimate the value of the EGIS map-ping system that involved visual methods, a binary logic confusion matrix, and a questionnaire and interview with the soil scientists. The inclusion of qualitative comments from the soil scientists was considered important because while the official soil map was published in 2004, the map actually depicts soil patterns as they existed in the study area in 1995 when major soil mapping fieldwork was completed. Furthermore, this map is the product of an individual soil scientist employing traditional soil survey methods with all the associated caveats previously outlined above (Pate 2004).

A side-by-side map comparison of the geovirtual and official soil maps (Figures 5a and 5b) shows

that the official map consists of 24 more soil poly-gons than the EGIS map. Of the twelve soil map units in the official map, one complex (GuE3) and two consociations (Sb and MoC) were not identi-fied through geovirtual soil mapping. Although MoC and GuE3 soil map units which occur on slopes of 8-15% and 15-25% respectively were not mapped here, the soil scientists did identify the same Monongahela silt loam and Gilpin-Upshur soils at slopes of 3-8% (i.e. MoB) and 25-35% (i.e. GuE3).

The visual method was complemented by a con-fusion matrix which adopts a strict binary right/wrong view to soil map quality. When used to assess absolute map quality the confusion matrix traditionally provides three measures of accuracy for the producer’s map, the user’s map, and for overall map accuracies (Story and Congalton 1986). In comparing the official and EGIS maps the extent to which the official and the EGIS maps match for

SMU SMU Name Parent Material Acreage Slope (%)

Coverage (%)

Cg Chagrin loam Alluvium 111.023 0-3 14.1

GpF3 Gilpin-Peabody complex Residuum 513.283 >35 65.2

GuD3 Gilpin-Upshur complex Residuum 18.283 15-25 2.3

GuE3 Gilpin-Upshur complex Residuum 34.289 25-35 4.4

Ha Hackers silt loam Alluvium 5.663 0-3 0.7

MoB Monongahela silt loam Old Alluvium 3.571 3-8 0.5

VaD Vandalia silt loam Colluvium 73.194 15-25 9.3

VsE Vandalia silt loam Colluvium 11.722 25-35 1.5

W Water Water 16.373 2.1

Table 2: Study area soil map units

Series Taxonomic Classification

Chagrin Fine-loamy, mixed, active, mesic Dystric Fluventic Eutrudepts

Gilpin Fine-loamy, mixed, active, mesic Typic Hapludults

Hackers Fine-silty, mixed, superactive, mesic Typic Hapludalfs

Monongahela Fine-loamy, mixed, semiactive, mesic Typic Fragiudults

Peabody Fine, mixed, active, mesic Ultic Hapludalfs

Upshur Fine, mixed, superactive, mesic Typic Hapludalfs

Vandalia Fine, mixed, active, mesic Typic Hapludalfs

Table 3: Taxonomic classifications of study area soil series.

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each soil class is defined by the overall map agree-ment statistic. To evaluate the confusion matrix, many scholars use a kappa coefficient that offers insight into the level of map agreement occurring by accident (Cohen 1960; Ma and Redmond 1995).

Soil class data at 30 randomly generated refer-ence sites were used to generate the confusion matrix. The confusion matrix revealed a relatively low level of map agreement of about 33% with a standard deviation of 9%. The extent of map agreement was reduced to about 29% by the kappa coefficient due to a randomized map agreement of about 4%. Noteworthy here is that there were three soil map units, Ha, MoB and GuE3, that had a 0% level of producer’s and user’s agreement in respect to the official and EGIS maps.

In contrast to the statistical comparisons, the soil scientists expressed considerable confidence in their ability to both use the EGIS environment to identify, delineate and describe the soils and in the quality of the resulting map product. Despite some initial difficulty in getting located and ori-ented in the geovirtual environment, the scientists were quick to master the navigational tools of the system and to explore the soil geography of the study area. The scientists appreciated the ‘go anywhere’ capability of the system and particu-larly the ability to gain access to places they could never have imagined achieving in the field due to trespass laws and physical barriers.

The EGIS environment was certainly viewed as an ergonomically superior soil-mapping inter-face than the traditional stereoscope. The EGIS environment eliminated much of the physical fatigue experienced when soil scientists bend for long periods of time to view the stereoscope. One scientist did experience some nausea, which can occasionally occur in geovirtual environments and is often exacerbated by one person wearing the head-tracking device. This is not dissimilar to being a passenger in a car that is navigating twist-ing roads. The system provided multiple viewing angles, locations and elevated views of 3D soil landforms in contrast to the ‘fixed view’ of the stereoscope. The group considered that viewing 3D landforms from multiple perspectives was con-siderably more intuitive and valuable than the fixed nadir view of the stereoscope and greatly facilitated identifying general and subtle terrain features. Furthermore, the speed of soil mapping was considered to be much faster and efficient than traditional methods and cut the time to map an area almost in half. The ability to relocate to other mapping projects anywhere in the country is another powerful aspect of the system.

Overall, the soil scientists enjoyed the EGIS envi-ronment and in particular the ability for several of them to experience concurrent immersion in geovirtual scenes as well as the navigation control over the system. Interestingly, they experienced varying levels of psychophysical presence, with one scientist praising the system for creating the feeling of “being out there” in the field and another indicating that “I was able to visualize myself in the field but I did not feel like I was in the field” (Hodza 2009). Nonetheless, the soil scientists appreciated the ability to view and collaboratively interpret the same soil landforms simultaneously, which they considered a major advantage of the system and contributed to a superior soil map product.

Conclusion

The ability to be immersed within a landscape scene and to develop the important soil-landscape model for soil boundary delineation was seen by the soil scientists as a considerable advancement over more traditional stereoscopic pair interpretation. The collaboration that the CAVE enabled for sev-eral soil scientists to simultaneously be immersed in the scene and yet share the same space is a powerful one in maximizing the expertise of soil mapping experts. The interpretations of each scientist were critically discussed to create a col-lective soil-landscape mental model that supported subsequent soil boundary delineation. Embedding multiple viewpoints into the soil map in real time was a very positive aspect of the system according to the soil scientists and led to their high level of confidence in the accuracy of the map product. Importantly, the 3D modeling demanded in tra-ditional soil mapping was offloaded to the EGIS environment in the form of explicit 3D rendered displays that greatly reduced the cognitive process-ing load on individual members (Hutchins 1995; Rogers 2006; Zhang and Patel 2006).

Significantly, despite having only limited experi-ence using the EGIS environment, the scientists mapped the study area in what they considered to be less than half the time (seven hours) it would have taken them using traditional soil survey methods (sixteen hours). A number of factors contributed to this efficiency including the ability of the EGIS environment to perform (1) rapid data integra-tion; (2) faster data manipulation and analysis; (3) more intuitive environment for soil geography interpretation and boundary mapping, and (4) faster error detection and correction through pre-defined topological rules and digital editing tools.

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ACKNOWLEDGEMENTSThe authors thank the Natural Resources Conservation Service for their interest and pro-vision of expert soil scientists who participated and contributed so much to this study

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