Unit:  Overlay Analysis in Raster

Hopefully you've found some comfort with finding and manipulating geographic data.  Now it is time to put some of that technical confidence to use.  How might we use multiple criteria to assess the landscape?
For this unit, please focus on the following:
  • Learn how to identify different rasters:  aerial images, landcover, elevation, binary, ordinal rasters, etc.
  • Learn to use logical and inequality expressions.
Types of Rasters  |  Raster Overlay  |  Raster Classification  |  Conditional Rasters
Terrain Tools  |  Boolean Rasters  |  Hypsometric Tint

Workshop [c]:  Finding Yellow Birch

In this lab we'll explore how to manipulate boolean rasters to perform basic overlay operations, as well as explore terrain mapping.  After we get comfortable with digital elevation models (DEMs) and boolean rasters, we'll create "ordinal" rasters that score the landscape for how likely it is suitable habitat for yellow birch.  
Here is our workflow:
  • Download DEM and landcover rasters from VCGI, or download all data here.
  • Create subjective ordinal rasters that "score" habitats.
  • Combine scored rasters to create an overall suitability.
  • Make a map that shows the best places to look for Birch.
(1) Download DEM  |  (2) Score Rasters  |  (3) Combine Rasters  |  (4) Create Map

Workshop [d]:  Delineating Groundfish Zones

In this lab we'll extend our practice with raster analysis.  After we get comfortable with the bathymetric and vessel traffic data, we'll create "categorical" rasters that describe a myriad of conditions that might help us locate groundfish habitat.  
Here is our workflow:
  • Download bathymetric data, vessel traffic data, and create a proximity raster.  Or download all data here.
  • Create subjective nominal rasters that "categorize" habitats.
  • Combine rasters to create conditional values.
  • Make a map that shows the best places to look for groundfish.
(1) Download Bathymetry  |  (2) Vessel Traffic Data  |  (3) Proximity Raster  |  (4) Classified Density
(5) Ideal Proximity & Depth  |  (6) Conditional Raster  |  (7) Majority Filter