Spectral Signature Analysis & Resource Modeling

Introduction

The main point of this lab was to have us create images that showed spectral reflectance and then to have us interpret and understand those images.

Methods

To first get started I created a Lab 8 folder within my personal folder to stay organized. Part 1 was spectral signature analysis. We worked with a Landsat ETM+ image that covered Eau Claire, Wisconsin. We measured and plotted the spectral reflectance of 12 materials and surfaces from the image including:

• Standing Water
• Moving Water
• Vegetation
• Riparian Vegetation
• Crops
• Urban Grass
• Dry Soil (uncultivated)
• Moist Soil (uncultivated)
• Rock
• Asphalt Highway
• Airport Runway
• Concrete Surface (Parking Lot)

To do this we used an image called Eau_Claire_2000.img and digitized the various surfaces that I listed above. This proved to be rather difficult because the image became very pixelated as you zoomed in. I actually had to use the help of google maps to help me identify some features. To "digitize" the surfaces we simply opened Erdas Imagine with the Eau Claire image and clicked on the home tab, then the Drawing tab and then polygon. We used the polygon tool to digitize each individual surface. Then to show the mean plot of each surface I clicked the raster tab, then the Supervised button and then signature editor. For each digitized surface you would click "Create new Signatures from AOI" and then rename the class by whatever feature you just digitized. Then to show the graph of that surfaces reflectance I would click "Display Mean Plot Window." This same process was continued for all 12 surfaces. Below is an image of the mean plot window of each surface.

Figure 1: Signature Mean Plot of all 12 surfaces

Each individual surface has its own way to reflect electromagnetic energy. For example all the plots for vegetation tend to be very similar, having a low reflectance in the red band and a very high reflectance in the the middle infrared band. The reason for this is because the plants need red light to support photosynthesis for creating energy for the plant. There is high reflectance in the middle infrared because if absorbed this would cause much damage to the plant's cells. 

Part 2 of the lab was resource monitoring such as vegetation health monitoring and soil health monitoring. To set up a Normalized difference vegetation index (NDVI) we first added the ec_cpw_2000.img image. I then clicked the raster tab and then unsupervised and then NDVI. I set up the parameters and named the output image ec_cpw_2000ndvi.img. The process was run and below is the output I obtained.

Figure 2: Image showing difference of healthy vegetation to unhealthy vegetation

The darker parts of the image (dark gray and black) should be the healthier vegetation due to the large absorption from the water content of healthy vegetation. The light areas should then be the unhealthy and dry vegetation that reflects much more light than the healthy vegetation.

The process for the Soil Health Monitoring was much of the same process as explained above. Using the image ec_cpw_2000.img I clicked the tab Raster, then unsupervised then indices. Setting up the parameters to "Ferrous Minerals" I then set the output image to be named ec_cpw_2000fm.img. After running the process the image below is the output I obtained.

Figure 3: Image of Ferrous minerals in Eau Claire area

The ferrous minerals seem to more dramatically change as you leave the city of Eau Claire and head outwards to more open areas.