Spectral Signature Analysis & Resource Modeling

Goals:

The goal of this lab was measure levels of spectral reflectance from various surface and materials. We also evaluated natural resources using remote sensing bands. We focused on Eau Claire and Chippewa counties for this lab.

Methods:
Part 1: Spectral Signature Analysis
We used Erdas and ArcMap to process the data needed for this lab. Part 1 of this lab focused on measuring and plotting the spectral reflectances of 12 surface types. In order to identify different surface areas with more ease I reference the satellite image to google earth. We used the polygon tool in Erdas to draw an area within the desired surface. Then we used the signature editor tool to view the area's spectral signature mean plot. We collected all 12 surfaces and displayed their spectral reflectance signature mean plots together for further analysis (Figure 13). We analyzed vegetation and moisture content mostly.

Figure 1. Spectral Signature Mean Plot for airport runways

Figure 2. Spectral Signature Mean Plot for still water

Figure 3. Spectral Signature Mean Plot for asphalt highways

Figure 4. Spectral Signature Mean Plot for concrete

Figure 5. Spectral Signature Mean Plot for crops

Figure 6. Spectral Signature Mean Plot for deciduous forests

Figure 7. Spectral Signature Mean Plot for dry soil

Figure 8. Spectral Signature Mean Plot for evergreen forests

Figure 9. Spectral Signature Mean Plot for moist soil

Figure 10. Spectral Signature Mean Plot for riparian vegetation

Figure 11. Spectral Signature Mean Plot for rock

Figure 12. Spectral Signature Mean Plot for moving water

Figure 13. Spectral Signature Mean Plot for all 12 surfaces

Part 2: Resource Monitoring

Part 2 of this lab focused on resource monitoring of vegetation health by processing band ratios. We used Erdas to run a NDVI on an image of Eau Claire and Chippewa County to determine the abundance of vegetation in the area. Using the NDVI function in Erdas we produced a black, gray and white image. With 5 equal interval classifications ranging from black to white. Black was mostly water, no vegetation, very little vegetation, moderate vegetation and finally the lightest shade was labeled high vegetation. This was then imported this image into ArcMap and created a map of vegetation health (Figure 14).

We then implemented the ferrous mineral ratio on the image of Eau Claire County and Chippewa County to determine the spatial distribution of minerals in this area. This used the indices function under the unsupervised tab in the raster section of Erdas. This produced a black and white image similar to the first one used to identify vegetation, with lighter areas showing areas of ferrous minerals and darker areas showing areas with little to no minerals. This was once again categorized in 5 equal interval categories. Mostly vegetation, non exposed soil, low ferrous minerals, moderate ferrous minerals and high ferrous minerals. We then imported this image into ArcMap and produced a map of ferrous minerals in the Eau Claire and Chippewa county area (Figure 15).

Results:

Below are the results of my methods from part 2 focusing on vegetation and ferrous minerals in the Eau Claire and Chippewa county area.

Figure 14. Abundance of Vegetation NDVI

Figure 15. Spatial Distribution of Ferrous Minerals in the Eau Claire and Chippewa County Area

Sources:

Satellite image: Earth Resources Observation and Science Center, United States Geological 

Study area: Eau Claire County and Chippewa County in Wisconsin.