Lab 8: Spectral signature analysis & resource monitoring

Goals and Background 

The goal of this lab is to gain experience on the measurement and interpretation of spectral signatures. This lab consists of collecting spectral signatures from remotely senses images. They will be graphed and there will be an analysis on them to verify whether they pass the spectral separability test. Also, health of vegetation and soils will be monitored using simple band ratio techniques.

Methods

Part 1: Spectral signature analysis

The satellite image was provided from the year 2000 of the Eau Claire and Chippewa Falls area in Wisconsin. 12 materials and surfaces from the image will be measured and plotted for the spectral reflectance.

1. Standing Water
2. Moving water
3. Deciduous forest
4. Evergreen forest
5. Riparian vegetation
6. Crops
7. Dry soil (uncultivated)
8. Moist soil (uncultivated)
9. Rock
10. Asphalt highway
11. Airport runway
12. Concrete surface

To obtain the spectral signatures in Erdas, the Polygon tool is utilized under the Drawing tab (Figure 1). After drawing an outline of the area of interest (AOI) to collect the spectral signature from, Signature Editor opened from the Supervised menu in the Raster tab of Erdas.  With the Signature Editor open, the Create New Signature is used from AOI and Display Mean Plot Window to add the signature from the polygon to the window and display the graph of the spectral signature.

Figure 1. Spectral images being identified using the drawing tool in Erdas. 

In the Signature Editor, Signature Name (label) was changed for each of the spectral signatures. Analyzing the spectral signatures is same time in the editor window and then selecting Multiple Signature Mode on the plot window allows you to view more than one signature (Figure 2).

Figure 2. Identified images with multiple signatures plotted on the graph. 

Part 2: Resource Monitoring

Section 1: Vegetation health monitoring 

In this section of the lab, simple band ratio is performed by implementing the normalized
difference vegetation index (NDVI) using the satellite image was provided from the year 2000 of the Eau Claire and Chippewa Falls area.

This is completed by  Raster-Unsupervised-NDVI in Erdas. In the Indices interface, the Sensor reads ‘Landsat 7 Multispectral’ in the Indices window and NDVI is highlighted. The following formula is used in order to get vegetation presence:

5 classes were used to show the abundance of vegetation presence in Eau Claire and Chippewa counties.

• Mostly water 
• No vegetation 
• Very little vegetation 
• Moderate vegetation 
• High vegetation 

Section 2: Soil health monitoring 

In this section of the lab, simple band ratio is performed by implementing the ferrous mineral ratio using the satellite image was provided from the year 2000 of the Eau Claire and Chippewa Falls area.

This is completed by Raster-Unsupervised-Indices in Erdas. In the Indices interface, the Sensor reads ‘Landsat 7 Multispectral’ in the Indices window and under Select Function, choose FERROUS MINERALS . The following formula is used in order to get ferrous mineral ratio:

5 classes were used to show the spatial distribution of ferrous minerals in Eau Claire and Chippewa counties.

• Mostly vegetation 
• Non exposed soil 
• Low ferrous minerals 
• Moderate ferrous minerals 
• High ferrous minerals

Results

Part 1: Spectral signature analysis

Analyzing the graph of reflectance from you are able to see the variation between standing water and moving water.  The variation displayed between the two water surfaces is explained through Specular and Diffuse reflection. The movement and ripples in the moving water give it diffuse reflection which send the reflections in all directions and reduces the intensity.  Where as the standing water is smooth has a more specular reflection, which sends the reflectance back with a higher intensity.  Multiple variations of this type of analysis can be done from the spectral information gathered in this manner.

Figure 3. Signature Editor with all of the spectral images identified. 

Figure 4. Signature mean plot with all 12 images plotted. 

Part 2: Resource Monitoring

Section 1: Vegetation health monitoring 

Figure 5. Vegetation Presence in Eau Claire and Chippewa County ranked with 5 classes. 

Section 2: Soil health monitoring 

Figure 6. Distribution of Ferrous Minerals in Eau Claire and Chippewa County based on 5 classes. 

Sources

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