GIS Programming: Working with Rasters

-

Rasters are one of the two primary data models used in GIS. While vectors use points and line segments to identify locations on Earth, rasters define space using a matrix of equally sized cells. Two modules included within the ArcPy package allow access to many geoprocessing tools designed to work with raster data and imagery, which is a specific type of raster data. These modules are the Spatial Analyst (arcpy.sa) and Image Analyst (arcpy.ia) modules. 

Our task for this week's lab was to write a script that creates an output raster layer identifying areas that fall within a given set of parameters regarding slope, aspect, and land cover class. More specifically, the output layer had to identify areas with the following characteristics: 

  • Forest land cover (classifications 41, 42, and 43)
  • Slope between 5°and 20°
  • Aspect between 150°and 270°
Below is a flowchart which outlines the various steps of the script.


One challenge I had while writing the script was trying to assign both the upper and lower limits of the slope and aspect calculations to a single variable (i.e., goodSlope and goodAspect) using the logical and operator in the following lines of code:

goodSlope = (slope >= 5) and (slope <= 20)
goodAspect = (aspect >= 150) and (aspect <= 270)

 
This resulted in the following error: 

ValueError: The truth value of a raster is ambiguous. Invalid use of raster with Boolean operator or function. Check the use of parentheses where applicable.


To resolve this issue, I ended up having to assign the maximum and minimum slope and elevation to two separate variables as explained in the lab instructions, resulting in four total variables as follows: 

goodSlopeLow = slope >= 5
goodSlopeHigh = slope <= 20
goodAspectLow = aspect >= 150
goodAspectHigh = aspect <= 270


Below is a screenshot of the output raster result from my successful script. The areas meeting the slope, aspect, and land cover class criteria are shown in red. 




The following is a screenshot of the results of my script shown in the IPython Console within Spyder.





Comments

Post a Comment

Popular posts from this blog

Isarithmic Mapping: Annual Precipitation in Washington State

-
Image
The second most common type of thematic map behind the choropleth map is the isarithmic map. While choropleth maps are great for mapping phenomena existing within hard boundary lines, also known as enumeration units, isarithmic maps are perfect for mapping continuous data such as temperature, precipitation, or elevation. In isarithmic mapping, contour lines are frequently used to connect locations with equal values. Hypsometric tints can then be added to color the areas between contour lines with different shades so that map readers can easily distinguish between areas with high and low values.  In this lab, our task was to create an isarithmic map showing average annual rainfall in Washington State. This rainfall data was interpolated using the PRISM (Parameter-elevation Regressions on Independent Slopes Model) method developed by Oregon State University. The map below was created using ArcGIS Pro. Areas with low precipitation (less than 10 in/yr) are shown in red, and areas with the
Read more

Spatial Enhancement, Multispectral Data, and Band Indices

-
Image
There are several techniques that can be employed within ERDAS Imagine to identify specific features using multispectral imagery. These features can then be displayed using different color band combinations to make them easier to distinguish visually. In general, the four steps used to identify features in ERDAS are: Examine the histogram for shapes and patterns in the data. Visually examine the image as grayscale for light or dark shapes and patterns. Visually examine the image as multispectral, changing the band combinations to make certain features stand out. Use the Inquire Cursor to find the exact brightness value of a particular area. In this lab, we were asked to identify three features within a multispectral image using these methods of interpretation based a given set of criteria. We were then asked to select an appropriate color band combination to display these features in a way that makes them clearly stand out from their surroundings.  The map below displays the first feat
Read more

Development Suitability Analysis

-
Image
For one of the scenarios in this week's lab, I was playing the role of a GIS Analyst for a property developer. My task was to create a map showing suitable land for development based on the following criteria:  Land Cover Soils Slopes Streams Roads  My general process for this analysis was to first convert all of these layers into rasters if they weren't already. For the rivers and roads layers, I used the Euclidean Distance tool to calculate the shortest distance from each cell to the nearest river or road. I then reclassified each of these layers based on a suitability scale of 1 - 5. Lastly, I combined the results of each of these five criteria using the Weighted Overlay tool. Below are my results.  The first map is an equally weighted average where each of the five criteria have an influence of 20% (or 1/5th). The second map shows an alternatively weighted output where each of the five criteria were weighed as follows:  Land cover (20%)  Soils (20%) Slope (40%) Distance to
Read more