This project assessed multispectral imagery with the highest vegetation index values using an NDVI value threshold to understand vegetation area size that would be impacted by a potential change to the local water policy. This process required using remotely sensed data to map land cover types. I also learned about examining the quality of imagery resolution from differing satellite sensors and their consequences in feature detection.

My workflow is described below:

In ArcGIS Online

1. Filter through open source raster datasets in ArcGIS Living Atlas and the Landsat Explorer App.
2. Zoom to area of interest and adjust the renderer to display healthy vegetation with the Near Infrared band.
3. Define an area of study.
4. Set the renderer to Vegetation Index (NDVI).
5. Create a vegetation mask to allow desired features to stand out.
6. Create a study mask layer to measure proposed change.
7. Ensure the correct processing template is used.
8. Save masks and layers to organization.

In ArcGIS Pro

1. Import Satellite imagery.
2. Add vegetation mask and study layer to map from the Catalog.
3. Adjust rendering for the multipspectral imagery layer to Color Infrared.
4. If the visualization is too bright, use the Dynamic Range Adjustment button.
5. Create and NDVI layer by apply a Band Arithmetic raster function to the imagery layer.
6. Use a raster function to clip the NDVI layer to study area extent and create a new layer.
7. Create a new mask with the Greater Than raster function to display areas exceeding the NDVI value threshold.
8. Export the mask as a .tif file.
9. Remove mask from contents pane.
10. Generate a raster attribute table from the .tif file with the Build Raster Attribute Table geoprocessing tool.
11. Add mask back to contents pane.
12. Correct raster layer symbology changes from running the tool.
13. Adjust drawing order to display mask overlaps.

Other tools used:

• Swipe
• Examine raster properties

Data provided by ArcGIS Image for ArcGIS Online.