ESRM 250/CFR 520 Autumn 2009 Phil Hurvitz

Raster Analysis 2 ESRM 250/CFR 520Autumn 2009 Phil Hurvitz 1 of 42

Overview • Importing data from generic raster files • Creating surfaces from point samples • Mapping contours • Calculating summary attributes for polygon features using a raster layer (“Zonal Statistics”) • Cross tabulating areas • "Querying" across multiple raster layers • Calculating neighborhood statistics • Calculating distance surfaces and buffers • Determining proximity • Converting raster and vector data sources 2 of 42

Overview • Importing data from generic raster files • Creating surfaces from point samples • Mapping contours • Calculating summary attributes for polygon features using a raster layer (“Zonal statistics”) • Cross tabulating areas • "Querying" across multiple raster layers • Calculating neighborhood statistics • Calculating distance surfaces and buffers • Determining proximity • Converting raster and vector data sources 3 of 42

* common format;free for download Importing data from generic raster files • ArcGIS can import rasters from many different generic raster data formats • ASCII raster file format • binary raster file format • USGS Digital Elevation Model (DEM) raster file format* • US DMA DTED raster file format 4 of 42

USGS source UW source Importing data from generic raster files • USGS DEMs are available online (free) 5 of 42

Importing data from generic raster files • USGS DEMs are available online (free) 6 of 42

Creating surfaces from point samples • Generation of a complete surface from incomplete point samples • Interpolation between and beyond individual sample points • For estimating values at locations where actual measurements were not made 8 of 42

Creating surfaces from point samples • Better estimation of surface value at locations near measured sample points • Several different interpolation methods are available • Assumption of gradual change of value across landscape • “GIGO:” Garbage In, Garbage Out • Advanced Kriging & geostatistics methods are also available in ArcGIS (but beyond the scope of this course) 8 of 42

continuous surface discrete sample points Creating surfaces from point samples • Points are interpolated to a surface 9 of 42

spline (minimized curvature) inverse distance weighting (local influence is strong) Creating surfaces from point samples • Two basic methods (spline and IDW) 10 of 42

Mapping contours • Finds adjacent cells of the same value • Converts linear arrangement of raster cells to vector lines • Creation of individual contours as simple graphics, or • Creation of feature dataset of contours for entire raster layer • User control of base contour and contour interval • Why is this tool valuable? Few digitized contour line data sets exist for remote areas, but DEMs frequently do exist 12 of 42

new layer Mapping contours • Group of contours created as shapefile 13 of 42

Summarizing zones • Defines zones of cells based on a group of integer cells or polygons with similar value • Creates statistical summary of each zone • Summary table is created • Summary chart (optional) 15 of 42

Summarizing zones • “Zone” is a group of cells (or polygons) that have the same attribute value 16 of 42

select polygon field to define zones of cells select raster layer containing variable to summarize specify output select statistic to graph (optional) Summarizing zones • Summary table definition 17 of 42

Summarizing zones • Table and chart are created statistics from input raster based on polygonal zones 18 of 42

Cross tabulating areas • Creates a “zonal intersection” of integer raster layers (similar to vector intersection) • Output is a table • 1st input layer creates records (1 record for each unique value) • 2nd input layer creates fields (1 field for each unique value) • Table values are map unit area measurements of combinations of zones • Valuable technique for change detection 20 of 42

Cross tabulating areas • An example: ownership & forest type potential vegetation type 42

Cross tabulating areas • Each ownership & vegetation class is quantified (remember all graphs come from tables) 42

rows columns Cross tabulating areas • Cross-tabulation setup 21 of 42

area measurements in map units (e.g., 2933100 square feet) Cross tabulating areas • Output table record layer (stands) row layer (soils) 22 of 42

Cross tabulating areas • Combination of Kapowsin soil and mixed-redcedar = 2933100 ft2 = 67.33 ac 23 of 42

"Querying" across multiple raster layers (“Map Query”) • Raster Calculator is easy to use and gives rapid results • Results may be as good as vector overlay depending on cell size & relative precision • Multiple rasters can be simultaneously queried(whereas only 2 vector layers can be compared in vector overlay) • Output represents cells that meet and do not meet query criteria 25 of 42

GUI query builder interface result is a new temporary raster "Querying" across multiple raster layers • Building Map Queries 26 of 42

"Querying" across multiple raster layers • Find cells where: • distance to streams < 300 ft and • elevation > 1500 ft and • timber volume > 60 mbf/ac 26 of 42

"Querying" across multiple raster layers • Cells that meet all three criteria are identified (value = 1) 26 of 42

Calculating neighborhood statistics • “Focal” statistical functions • Moving “focus” (also known as “kernel”) window calculates statistics for all cells within the focus • Output value is written to central cell (also known as “focal cell”) in the output raster • Statistical functions: Minimum Maximum Mean Median Sum Range Standard Deviation Majority Minority Variety 28 of 42

locations of greatest variationin elevation Calculating neighborhood statistics • Focal Standard Deviation 29 of 42

Calculating neighborhood statistics: high pass filter • High-pass filter (a focal process) uses these coefficientson the kernel 30 of 42

Calculating neighborhood statistics: high pass filter • High-pass filter finds edges edges are higher in absolute value for the output grid 31 of 42

Calculating distance surfaces and buffers • Similar to buffering with vector data but with greater informational content • Creates a continuous distance surfacerather than a discrete bounded polygonal area • (A vector buffer results in “inside/outside” whereas the distance surface gives measured distances) • Distance measured from input layer featuresor raster cells 33 of 42

continuous distance value surface Calculating distance surfaces and buffers • Distance from vector features 34 of 42

Calculating distance surfaces and buffers • Limitation by maximum distance • Like a vector buffer but also with measured distance for each output cell 35 of 42

Assigning proximity • Defining territories based on proximity • Can be applied in analysis of competition

output cells have the value of the closest input feature output value is selected from input layer table Assigning proximity • “what territories are closest to a set of features?” aka “Thiessen,” “Voronoi” 37 of 42

Converting raster and vector data sources • Raster  vector conversions are possible • Always a loss or generalization of shape • Support for point, line, polygon  raster in ArcGIS • Avoid converting rasters that do not have large contiguous zones (e.g., DEMs) 39 of 42

polygon shapefile select conversion field,output name & folder Converting raster and vector data sources: raster to polygon • Convert raster zones to polygon feature data set 40 of 42

GRIDCODEfield stores vector attribute Converting raster and vector data sources: raster to polygon • Convert raster zones to polygon shapefile 41 of 42

Value field Converting raster and vector data sources: raster to polygon • Convert vector lines to raster zones 42 of 42

ESRM 250/CFR 520 Autumn 2009 Phil Hurvitz