raster data analysis - grel@ist

Lecture 06Lecture 06

Raster Data Analysis

Rizwan Bulbul, PhD

Geospatial Research and Education LabDepartment of Space ScienceInstitute of Space Technology

[email protected]

mailto:[email protected]

Wednesday, April 13, 2016 Advanced GIS-2016-GREL@IST 2/49

Learning Objectives

● become familiar with basic single and multiple raster geoprocessing techniques.


Introduction

● Raster analysis ranges from simple to complex– Simplicity

● Data structure and implementation

– Flexibility● Wide application range

● Long history of raster analysis has resulted in diverse set of tools


Map Algebra

● Cell-by-cell combination of raster data layers● This entails the application of operations

– Unary

– Binary


Map Algebra

● Incompatible raster cell sizes cause ambiguities when raster layers are combined

● Which layer2 value should be used?● Median? Average?● Software specific treatment● Resampling may be needed


Map Algebra

● Raster operation types– Local operations

– Neighborhood operations

– Zonal operations

– Global operations



● Local operations– Cell-by-cell operations

– Single or multiple rasters

– Use a function that relates input to output or use classification table



● Local operations – with a single raster– Through a mathematical function transform each

input cell to corresponding output cell

[sloped ]=57.296 x arctan ([ slope p] /100)



● Local operations – with a single raster– Reclassification (recoding, transforming)

● Creates a new raster by classification using lookup tables, ranges of values , or a conditional test



● Local operations – with multiple rasters– Also referred map overlay

– Mathematical functions, and other measures based on cell values or their frequencies in the input rasters can also be derived and stored in output raster

● e.g. minimum, maximum, range, sum, mean , median etc (for numeric data)

● Majority, minority, no. of unique values or variety (for numeric and categorical data)



● Local operations – with multiple rasters

MeanMean Majority

Combine



● Local operations – with multiple rasters– Clip



● Local operations – Logical operations



● Local operations – Applications

– Universal soil loss equation (USLE)

A = RKLSCP– Band Rationing

NDVI = NIR-Red

NIR+Red


Raster Data Analysis - Types

● Local operations● Neighborhood operations● Zonal operations● Global operations



● Neighborhood operations– Works on a single raster

– Also called focal operation, uses a focal cell and set of its surrounding cells

– Common neighborhoods● Rectangles● Circles● Annuluses● Wedges



● Neighborhood operations– Depend on the concept of a “moving window”



● Neighborhood operations - Statistics– Same measures and statistics as with local

operations

– Block operation: a neighborhood operation that uses a rectangle (block) and assigns the calculated value to all block cells in the output



● Neighborhood operations - Statistics



● Neighborhood operations – Statistics– Majority example



● Neighborhood operations – Statistics– Mean example Moving window may be defined as kernel



● Neighborhood operations – Statistics– Mean example



● Neighborhood operations - Applications– Edge detection

– Noise removal

– Moving average – data smoothing



● Neighborhood operations – Applications– Edge detection


– Edge detection



● Neighborhood operations – Applications– Noise removal


– Noise removal



● Neighborhood operations – Applications– Mean

● Increases spatial covariance● High spatial covariance means values are autocorrelated● Spatial covariance increases with many moving window

functions



● Zonal Operations– Works with groups of cells of same values or like features,

called zones (e.g., land parcels, political/municipal units, water bodies, soil/vegetation types)

– Zone may be contiguous or noncontiguous

– May work with a single or two rasters

– Single raster – measures zonal statistics such as area, perimeter thickness and centroid

– Two rasters – one input raster and one zonal raster, zonal op produces an output raster that summarizes (area, min, max, mean , median etc) the cell values in the input raster for each zone in the zonal raster.



● Zonal Operations– Area



● Zonal Operations– Perimeter



● Zonal Operations– Thickness

Homework-Algorithm for zonal thickness and centroid-How you would compute thickness and centroid for vectors?



● Zonal Operations



● Global operations– Operations performed over

the entire extent of a raster

– Typical global operations include

determining basic statistical values

for the raster as a whole



● Physical distance measures– Physical vs cost distance/surfaces

– Continuous distance measures

– Allocation and direction

● Other Operations– Mosaic

– Raster data extraction

– Raster data generalization● Pyramiding● Aggregate● RegionGroup



● Physical distance measures (Cost Surfaces)

– Many problems require an analysis of travel costs

– A cost surface contains the minimum cost of reaching cells in a layer from one or more source cells

– Distance is commonly used as cost measure

Friction surface


Comparison

● Vector and Raster Data Analysis– GIS packages treat both data models separately

– Why?

– GIS projects differ in objectives and data sources

– Conversion is possible

– Choose type of analysis that is appropriate, efficient, and accurate

– Lets compare the two with buffering and overlay (the two most common operations in GIS)


Comparison

● Vector and Raster Data Analysis– Overlay

RASTER VECTOR

A local operation with multiple rasters Output feature geometry is the intersection of respective input geometries and output feature attributes are the sum of the attributes of the respective input features

No intersection computations – no complicated computations (Resampling is possible but still less complicated than computing intersections)

Must compute geometric intersections and insert points at the intersections. Intersection computation is quite an expensive operation

Local operation has access to mathematical functions to create the output

Only combines attributes from the input layers

Not suitable for analyzing data sets with large attributes. (May need a raster for each attribute !!!)

Most suitable for analyzing data sets that have a large number of attributes


Comparison

● Vector and Raster Data Analysis– Buffering

RASTER VECTOR

Uses cells in measuring physical distances Uses x- and y- coordinates in measuring distances from select features

Buffers are less accurate Bufferes are more accurate

Less flexible and limited options e.g. for defining multiple buffer zones, additional processing is needed on continuous distance measures

More flexible and offers more options e.g. multiple rings are possible


Further Reading

● Chapter 11, Introduction to GIS, Chang● Chapter 10, GIS fundamentals, Bolstad● Chapter 06, Introductory GIS, Jensen● Chapter 14, GI systems and Science, Longley● http://resources.arcgis.com/en/help/main/10.1/index.html#/How_Zonal_Geometry_works/009z000000ws00

0000/

● http://2012books.lardbucket.org/books/essentials-of-geographic-information-systems/section_12.html

raster data analysis - grel@ist

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