Lab 8: Spectral Signature Analysis and Resource Monitoring

Background

The objective of this lab is to gain experience on the measurement and interpretation of spectral reflectance of various Earth surfaces and near surface materials using satellite images.

Methods

Collecting images, Graphing images and analyzing images to verify whether they pass the spectral separability test discussed in lectures.

Part 1: Spectral signature analysis

The first step is to open up the Eau_claire_2000.img to measure and plot the spectral reflectrance of 12 materials and surfaces from the image. For this image the analysis will include :

1.Standing Water

2.Moving water

3.Forest  

4.Riparian vegetation. 

5.Crops

6.Urban Grass

7.Dry soil (uncultivated) 

8.Moist soil (uncultivated)

9.Rock

10.Asphalt highway

11.Airport runway

12.Concrete surface (bridge, parking lot, or any type of concrete surface)

To take a spectral signature click on the Home tab and then drawing.  The click on the polygon tool, and digitize an area within, in this case Lake Wissota, to plot the spectral signature.  Now click raster and click on Supervised followed by signature editor.

Now that the signature editor is open create a new signature from the AOI and change the default name to what makes sense, for this example 'Standing Water' was used.

Next, click on the Display Mean Plot Window to display the graph.

Finally, Collect Spectral Signatures for 2-12 in the same way, and add all of the signatures to the same plot.

Getting a final graph that looks like this:

Figure 1:  This is a graph showing the spectral analysis of the 12 objects defined above. The y-axis shows the reflectance and the x-axis is the wavelength.

Part 2:

In the second part of this lab the goal is to preform simple band ratio by implementing the normalized difference vegetation index (NDVI) on a selected image.

NDVI =(NIR - Red)/(NIR+Red)

To start click on Raster- Unsupervised - NDVI

This opens the Indices interface.  For this example Landsat 7 Multipsectral was used for the sensor.

Now under select function make sure NDVI is highlighted, and run the Program

Finally open it into Arcmap and create a map with a classification scheme to understand the colors.

Figure 2: Vegetation Health Monitoring Map: Black - Low Vegetation, White - High Vegetation

Section 2:

Soil Health Monitoring

Ferrous Minneral = MIR/NIR

Open an image to study, and click on Raster-Unsupervised - Indices.

The indices interface opens.  Fill out the data according to the parameters needed and under the selection function choose ferrous minerals

Figure 3: Ferrous Mineral Monitoring Black: bad exposure, White: High Ferrous Minerals

Conclusion

Using spectral analysis can tell a lot about an area without actually have to visit the area.  If looking for places that show high ferrous mineral content creating a map like the one in Figure 3 can be helpful to cut down on time of having to find great exposed areas. Also looking at Vegetation quality can be helpful for farmers, therefore using signature analysis can tell a lot about an area from just a computer screen.