Spectral Reflectance

INTRODUCTION

The following lab demonstrates the ability to measure and interpret spectral reflectance signatures of Earth surface features and materials and to perform basic monitoring of Earth resources using remote sensing band ratio techniques.  

METHODOLOGY

Spectral Signature Analysis

All spectral images were collected from a Landsat ETM+ aerial image of Eau Claire, WI. Using ERDAS Imagine, all spectral images were first digitized, organized by the signature editor, and analyzed using the signature mean plot. The first surface feature digitized was Lake Wissota to give a general overview of the process. The list of additional features collected were:

• Standing water
• Moving water
• Riparian vegetation
• Crops
• Urban grass
• Dry soil
• Moist soil
• Rock
• Ashalt highway
• Airport runway
• Concrete surface

Resource Monitoring

To monitor vegetation health, the normalized difference vegetation index (NDVI) was used which subtracts the red band from NIR divided by the sum of the red band and NIR. The output image was then opened in ArcMap where the gradient was switched to an equal interval 5-class classisification system map. 

The same process was repeated to monitor soil health, except using the ferrous minerals equation (MIR/NIR) instead of the NDVI. 

RESULTS

Spectral Signature Analysis

Figure 1 shows the signature analysis process in ERDAS Imagine. The signature editor on the left displays all areas digitized while the mean plot on the right allows for analysis of the digitized feature.

Figure 1: Lake Wissota digitized with ERDAS signature editor and signature mean plot

Figure 2 shows the signature mean plot for moving water collected by Putnam Rock on the Chippewa River. Water features absorb most energy and have low spectral reflectance as the wavelength increases. The reason for higher reflectance in the blue band is because of the shorter wavelength and the resulting scattering. 

Figure 2: Signature mean plot for moving water in Eau Claire

Figure 3 shows the signature mean plot for a forested area in Eau Claire. Again the blue band has a higher reflectance in the visible light spectrum because of scattering. The reflectance peaks at the NIR band because the vegetation has absorbed all of the energy needed for photosynthesis in the visible spectrum and begins reflecting instead of absorbing.

Figure 3: Signature mean plot for forest area in Eau Claire

Figure 4 shows the signature mean plot for both dry and moist soils in Eau Claire. The largest difference in spectral reflection occur in the visible light spectrum. This is because of the moisture content in the moist soil. At the NIR wavelength, however, the water no longer has the same effect and when analyzing soil health, it's essential to collect data in the first three bands to provide easy identification.

Figure 4: Signature mean plot for dry and moist soils in Eau Claire

Figure 5 shows the signature mean plot for all collected features while Figure 6 shows these features listed in the editor.

Figure 5: Signature mean plot for all features collected in Eau Claire

Figure 6: Signature editor displaying all features collected in Eau Claire

Crops, dry soil, and airport all show similar trends of dipping reflectance on band 4, increasing to band 5 and then decreasing again on band 6.

Forest, urban grass, and moist soil all show similar trends of slowly decreasing from band 1 to band 3, followed by an increase on band 4 and a decreasing reflectance to band 6. All of these have moisture in them that reflects energy similar.

Concrete surface, standing water, and moving water all show a similar trend of slowly decreasing reflectance from band 1 to band 6. This could be because they all have very little reflectance throughout all wavelengths.

Asphalt and rock are very similar in that the reflectance is maintained from band 1 to band 4 and then peaks a little at band 5. This could be because

Concrete has a unique spectral signature. This is because of its unique composition of material.

Resource Monitoring

As shown by Figure 7, the spectral reflectance of the NDVI was re-classified in ArcMap to produce a map showing vegetation health in Eau Claire. This is a cheap and efficient way of vegetation monitoring and can be used for a vast number of purposes. The white in the image indicates an abundance of forest, while the black indicates areas that vegetation doesn't exist. 

Figure 7: Map showing vegetation health in Eau Claire

Figure 8 then shows the same thing but instead for soil monitoring. The white in the image indicates where most ferrous minerals are located and indicate man-made structures that consist of concrete, asphalt, and other materials.

Figure 8: Map showing soil health in Eau Claire

SOURCES

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