Remote Sensing Lab 8: Spectral Signature Analysis and Resource Monitoring

   Background and Overview: In this lab I became accustomed to some procedures having to do with spectral signatures; simple and manual procedures were practiced, and also those using an index function were practiced (we used the NDVI and Ferrous Mineral index functions).

   Methods:
Part 1 (Spectral Signature Analysis): Opening the image supplied by my instructor of the Eau Claire Area, I clicked on the Drawing tab then the polygon button to draw a polygon over a large area of Lake Wissota, our area of spectral interest. I now clicked on the Raster tab, then on Supervised, and then Signature Editor in order to open the Signature Editor window. I now clicked on the Create New Signature From AOI button on the top tool bar of this window and renamed the created signature Standing Water. After this I clicked on the Draw Mean Plot Window icon in order to see the spectral signature drawn on a graph. I next did the same for all of the following types of terrain: standing water, moving water, forest, riparian vegetation, crops, urban grass, dry soil, moist soil, rock, asphalt highway, airport runway, and concrete parking lot. All of these spectral signatures can be seen below in the results section as signature mean plots showing the spread of reflectance across bands. I was also asked a few questions about these signature and the answers are listed here:

1. Water reflects most energy in the visible bands and especially in the blue (hence it looks blueish) and it absorbs the infrared and thermal bands quite well.
2. Vegetation displayed high NIR reflectance because it really just absorbs the visible band to make food and reflects higher bands to prevent damage. It looks green because it absorbs mostly much more red.
3. Dry and moist soils differ most in their reflection of the MIR band because of the MIR water absorption.
4. The following are comparisons of spectral signatures: Vegetation, urban grass and riparian vegetation are very similar because they are all live plant life! Airport runway and highway are also fairly similar but less similar. Moving water and crops are very different.
5. Bands three, four, five, and six seem like they have a lot of weird variations even among similar surfaces so I would choose these bands if I had to choose 4 bands to include in a sensor for differentiating these surfaces.

Part 2 (Resource Monitoring) Section 1 (Vegetation Health Monitoring): In this section I used the normalized difference vegetation index (NDVI) to create a new raster file based on its results of an image of the Eau Claire and Chippewa counties area. I brought the image into a viewer in ERDAS Imagine, then clicked the Raster tab, then Unsupervised, then NDVI. I made sure to input the specific image, and name the output image appropriately and save it in my folder, then selected the appropriate Landsat 7 Multispectral sensor and the NDVI index in the Indices window. I next clicked run and then opened the resulting image in ArcMap to display the raster in an appropriate 5 class and equal interval classification and symbology system and then created a cartographically pleasing map. I also when asked noted that the white areas are areas of a high index value, denoting areas of high vegetation health, and gray and black areas denoted either water or lower vegetation health. My map is included in my results.

Part 2 (Resource Monitoring) Section 2 (Soil Health Monitoring): In this section I used much the same procedure but instead of using the NDVI function and index I used the Ferrous Minerals index. I also created a map in the end in ArcMap with the same classification system and it too is included in my results. When asked I noted that the ferrous minerals are more prevalent in the south-west half of the image.

   

   Results:

Part 1 Signature Mean Plots:

Riparian Vegetation

Airport Runway

Asphalt Highway

Standing Water

Standing Water

Dry Soil

Vegetation

Urban Grass

Parking Lot

Crops

Moving Water

Part 2 Maps:

Ferrous Mineral Content in Eau Claire and Chippewa Counties

Vegetation Health (NDVI) in Eau Claire and Chippewa Counties

Sources: Imagery and instruction was provided by the instructor, Dr. Cyril Wilson.

Satellite image is from Earth Resources Observation and Science Center, United States Geological Survey.