Lab 8: Spectral Signature Analysis

Introduction

In this lab, students will learn how to take, graph, and analyze spectral signatures from satellite imagery. This will build upon students priory knowledge of spectral reflectance of Earth's surface features. Twelve spectral signatures will be collected from a Landsat ETM+ image that covers a portion of western Wisconsin and Eastern Minnesota. Students will need to located each of these surface features: standing water, moving water, vegetation (forest), riparian vegetation, crops, urban grass, dry soil (uncultivated), moist soil (uncultivated), rock, asphalt highway, airport runway, and a concrete surface.

Methods

ERDAS IMAGINE 2013 will be used to capture and analyze the spectral signatures.

Figure 1: With the proper image opened in ERDAS, Lake Wissota was picked as the location for standing water. The Drawing tab was selected, as seen above, and polygon, near the left end of the toolbar, was chosen. A small polygon was drawn and selected, making it the active area of interest.

Figure 2: Next, the Signature Editor was opened by navigating to the Raster tab > Supervised > Signature Editor, as seen above.

Figure 3: With the polygon still selected, the Create New Signature(s) from AOI icon (looks like a bent arrow next to the plus sign) was clicked. This created a new entry into the black editor window. The name was changed to Standing Water. The same process continued for each feature until all twelve had been taken.

Figure 4: The signature mean plot for standing water. These graphs are generated by clicking the Display Mean Plot Window icon (looks like a zig-zag line) in the Signature Editor window. By holding shift and clicking to select multiple signatures and clicking the Switch Between Single and Multiple Signature Mode Icon (looks like 3 zig-zag lines) in the Signature Mean Plot window, any number of signatures can be plotted on the same graph.


Results

Figure 5: All twelve signatures plotted together to help visualize trends. It became apparent that three trends seemed to dominate the graph. These trends were broadly categorized as water, vegetation, and land.

Figure 6: Water features. Standing water has a higher reflectance across all spectral channels. This is most likely due to higher sediment or algae content in the standing water when compared to moving water.

Figure 7: Vegetation features. Crops and urban grass have higher reflectance in the visible red band and mid-IR channel when compared to forest vegetation and riparian vegetation. This suggests that crops and urban grasses are under more stress or are more unhealthy than the other types of vegetation.

Figure 8: Land features. Rock has the highest reflectance across all spectral channels. Dry soil has substantially higher reflectance in the mid-IR channel than moist soil. As seen in Figure 6, water has low reflectance in the mid-IR channel because it absorbs most of this radiation. As such, the greater the water content in the soil, the lower its reflectance will be, especially in the mid-IR channel.




Data Sources

UWEC Department of Geography and Anthropology