This phase of the project utilized a number of the Spatial Analyst tools. The biggest challenges came with the processing phases. Initially the image files (photos) were evaluated using the Isocluster tool, followed by the software using the Maximum Likelihood evaluator. This was to classify the images into 50 classes. Initially this process failed many times. I was able to get it to process using 25 classes. Later the instructor provided a 50 class raster using the mosaic tool. I then changed my strategy from using the 7 images I had converted to using the provided mosaic file. The next steps involved identifying the pixel values to determine the types of land classes. The goal being to classify into 3 classes, either Trees, Grass or Impermeable surfaces. I then used the Reclass tool to classify the 50 classes into three classes. This process ultimately failed over 15 times, however through a consultation with my GIS internship supervisor, I found some work-arounds using the Extract by Attributes. After this Extract by Attributes the software had no issues reclassifying the raster to 3 classes. Another part of this whole process involved using the Extract by Mask tool to make the project relevant to just the neighborhoods of the study area. Finally the tables were manipulated using the field calculator to compute percentages of trees, carbon storage and carbon sequestration, followed by constructing the graphs to add to the maps.
Despite some processing frustrations, I learned a number of things about the Spatial Analyst tool set and classifying and reclassifying rasters
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