Objectives. Raster Data Discrete Classes. Spatial Information in Natural Resources FANR Review the raster data model

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1 Spatial Information in Natural Resources FANR 3800 Raster Analysis Objectives Review the raster data model Understand how raster analysis fundamentally differs from vector analysis Become familiar with basic tools of raster analysis Raster Data Discrete Classes Each cell has one and only one value. The values are coded to land cover, road types, etc. Example: Name Color Value slash pine 10 loblolly pine 20 water 70 hardwood 30 pasture 40 1

raster analysis Raster Data Discrete Classes Each cell has one and only one value.

2 Vector vs. Raster Revisited Vector format is good for storing datasets with many attributes For example stand map with species composition, age class, stand density, etc. Vector vs. Raster Revisited Value = the attribute (yr _ estab) Count = the number of pixels with that value (yr_estab) Multiple attributes require multiple grids Vector to Raster What if we wanted to do analysis using: Stand age ( yr_estab ) Stand type Stand volume per acre ( cuft ) 2

species composition, age class, stand density, etc.

3 Vector vs. Raster Revisited So, if raster format is not so good for datasets with multiple attributes, Why use rasters? 1. Raster format facilitates many spatial analyses 2. Raster format is much better at representing attributes that vary continuously across the landscape elevation distance to streams / roads precipitation Raster Data Analysis Single-Layer Analysis Distance, Direction, and Allocation Density Neighborhood Analysis Reclassification Multiple-Layer Analysis Map Calculator (Map algebra) Zonal Summaries Masking (raster Clipping ) Raster Data Analysis Setting up your analysis Working directory Analysis extent and snap grid Masking (raster Clipping ) Single-Layer Analysis Reclassification Distance, Direction, and Allocation Density Neighborhood Analysis Multiple-Layer Analysis Map Calculator (Map algebra) Zonal Summaries 3

Distance, Direction, and Allocation Density Neighborhood Analysis Reclassification Multiple-Layer Analysis Map Calculator (Map algebra) Zonal Summaries Masking (raster Clipping ) Raster Data Analysis

4 Setting grid analysis properties Analysis properties Working directory Masking Spatial extent of output Cell size Once set, analysis property values stay set until changed Analysis properties determine spatial properties for all newly created output grid layers Raster Data Analysis: Clipping : Raster Extent and Mask 1. Setting the extent of analysis 2. Masking (raster Clipping ) Raster Data Analysis: Clipping : Raster Extent and Mask 1) Extent set to Bfg_stands 2) Mask set to 100m buffer of roads 3) Raster Calculator: Output Grid = Input Grid The results: 4

Analysis: Clipping : Raster Extent and Mask 1. Setting the extent of analysis 2.

5 Single Layer analysis: Distance From any point on the landscape, how far is it to the nearest Stream? Road? Landing? Town? Sample Plot? Distance Analysis Example Vector or Raster Streams Layer Spatial Analyst: Distance: Straight Line Distance from source Single-Layer Analysis: Density Ex: Density of Roads What if you wanted to know what the density of roads are across the landscape (for every location in the landscape) Why might we want to know this? Wildlife applications? Forestry applications? Fisheries / Water quality applications? 5

Analysis: Density Ex: Density of Roads What if you wanted to know what the density of roads are across the landscape (for every

6 Single Layer Analysis: Density Ex: Density of Roads Roads Road Density Single-Layer Analysis: Neighborhoods Neighborhoods or Moving Windows What if you need to know something about the area around a point (raster cell), not just what s in that cell? Example: mean elevation vs. point elevation How Neighborhood Analysis Works Neighborhood is defined by a shape (circle, rectangle, etc.) and a size (3x3, 5x5, etc.) Functions computed for cell at the center of the neighborhood include sum, mean, standard deviation, minimum, maximum, majority, etc. Step 1 Step 2 Step 3 Results: SUM =

point elevation How Neighborhood Analysis Works Neighborhood is defined by a shape (circle, rectangle, etc.) and a size (3x3, 5x5, etc.

7 Neighborhood example 1 CUFT continuous attribute, mean in two different moving windows 3 x 3 cells 15 x 15 cells Single-Layer Analysis: Reclassification Convert One Classification Scheme 1 = Hardwood 2 = Natural Pine 3 = Planted Pine 4 = Clearcut 5 = Other To Another 1 = Mature Pine 2 = Other Forest 0 = Other Raster Data Analysis Single-Layer Analysis Distance, Direction, and Allocation Density Neighborhood Analysis Reclassification Multiple-Layer Analysis Masking (raster Clipping ) Raster Calculator (Raster or Map algebra) Zonal Summaries 7

Mature Pine 2 = Other Forest 0 = Other Raster Data Analysis Single-Layer Analysis Distance, Direction, and Allocation Density

8 Multi-layer Raster Analysis Raster calculator: Can be used to combine many grids using mathematical functions and Boolean logic Multi-layer Raster Analysis Zonal Analysis: Similar to Vector Overlay analysis, except zones are defined by all pixels with the same value Zone Raster: Value Raster: Multi-layer Raster Analysis Zonal Analysis: Similar to Vector Overlay analysis, except zones are defined by all pixels with the same value Zone Raster: Value Raster: Output t (Zonal Sum):

2 5 5 2 1 2 4 7 8 4 2 7 1 Multi-layer Raster Analysis Zonal Analysis: Similar to Vector Overlay analysis, except zones are defined by all pixels with the same value Zone

9 Multi-layer Raster Analysis Zonal Analysis Outputs: Multi-layer Raster Analysis Applications of Zonal Analysis: Zone = Stands, Values = Soil Productivity Zone = Stands, Values = Habitat Suitability Zone = Ownership, Values = Stand type Zone = Ownership, Values = Species Richness Raster Analysis Most wildlife habitat assessment is done using raster data and raster analysis functions Why? Data formats land cover, elevation, climate variables are often in raster format Efficiency faster to run using raster Spatial analysis lots of techniques and options 9

Richness Raster Analysis Most wildlife habitat assessment is done using raster data and raster analysis functions Why?

10 Raster Analysis: Example 1 Wildlife Habitat Analysis: Assemble the variables that are correlated with species presence or abundance Use field data (known) locations to sample each layer to develop statistical models Variable of interest measured for a limited number of isolated plots or individuals Other, possibly correlated variables are available as spatial datasets Develop a rule-based or mathematical model Sample Plot Variable = f(spatial Variables) Use the model to make predictions for plots not sampled Example: Tree species richness predicted as a function of Landsat TM vegetation indices, climate variables, and land ownership Sites used to sample environmental variables, exported to statistical software package Once the statistical models have been developed, the equations can be applied back in GIS using Map Algebra and the Raster Calculator 10

mathematical model Sample Plot Variable = f(spatial Variables) Use the model to make predictions for plots not sampled Example: Tree species richness predicted as a function of Landsat TM vegetation

11 Raster Analysis Example 2: GAP Analysis Objective Identify conservation Gaps at a national level Projects in all 50 states Cooperative effort b/w USGS, other federal agencies, state agencies, universities, non-profits GAP Analysis Land Cover Species Records Land Ownership Management Habitat Conservation Status GAP Analysis - How much habitat is there? - Where is the habitat? - How much of the habitat is protected from development, logging, etc? 11

Analysis Land Cover Species Records Land Ownership Management Habitat Conservation Status GAP Analysis - How

12 Habitat Model: -Created new grid from suitable habitats t including: -open water (fresh) -clearcuts -hardwood forests -loblolly/slash pine -cypress-gum swamp -freshwater marsh -evergreen forested wetland -Applied mask of suitable habitat >530 ha -Clipped by digitized range 12

pine -cypress-gum swamp -freshwater marsh -evergreen forested

Raster Operations. Local, Neighborhood, and Zonal Approaches. Rebecca McLain Geography 575 Fall 2009. Raster Operations Overview

Raster Operations. Local, Neighborhood, and Zonal Approaches. Rebecca McLain Geography 575 Fall 2009. Raster Operations Overview Raster Operations Local, Neighborhood, and Zonal Approaches Rebecca McLain Geography 575 Fall 2009 Raster Operations Overview Local: Operations performed on a cell by cell basis Neighborhood: Operations