Lab 5: Spectral Signature Analysis & Resource Monitoring

Shelby Short

Goal and Background:

 The main goal of this lab was for us as students to look at multiple Earth Surface and Near Surface features on Earth to better understand their spectral reflectance measurements and then use that data to make remote sensing inferences. These near surface and surface features were captured with satellite imaging which allowed us to collect spatial band data that was then graphed, and analyzed(Wilson, 2020). 

Methods and Analysis:

                The first part of the lab we looked at analyzing spectral images from a LandsatETM+ image of the Eau Claire Area. We measured and plotted the spectral reflectance of 12 materials and surfaces from the image. To do this we created a polygon on the image we were analyzing (Figure 1). Then you use the Signature editor tool(Figure 2) to create an Area of Interest that can then be plotted(Figure3). By plotting the spectral reflectance you can look to see what bands have the highest reflectance v.v. the highest absorption. For this example of standing water, Band 1, the blue band had the highest reflectance and band 4, the Near Infrared band had the lowest reflectance. In total, we did this for 12 different features which created a plot with various differet spectral signatures(Figure 4).  

Figure 1. Landsat ETM+ Image of Lake Wissota, Wisconsin in ERDAS with a polygon draw over to analyze the spectral reflectance of a standing body of water.  

 Figure 2. Signature Editor Tool used to create the Signature Mean Plot. 

    Figure 3. Signature Mean Plot showing the reflectance on the y axis and the Band number on the x axis. 

 

 

 

 Figure 4. Signature Mean Plot of 12 different surface features relative to one another. 

    The second part of the lab pertained to resource monitoring. To do this we performed simple band ratio to monitor the health of vegetation and soils. In order to execute this sort of analysis, the first tool used is NDVI tool under the raster unsupervised tools(Figure 5) to create a new raster image(Figure 6).  

Figure 5. NDVI tool used for monitoring the health of vegetation and soils. 

Figure 6. Image of Eau Claire area showing areas with a bright tone to be highly vegetated and areas with a dark or black tone to have little to no vegetation. 

    Another field of resource monitoring is that of the soil and its health. to determine the health of soil through remote sensing, one can compare the Middle Infrared/Near Infrared of ferrous minerals using the Indices tool(Figure 7). We can put this image in ArcMap and analyze it further(Figure 8).  

Figure 7. Indices tool used to create a raster image for analyzing soil health. 

Figure 8. Map created from the indices tool in ERDAS. This map has 5 classifications that show where there are a lot of ferrous minerals compared to areas of low minerals which is further compared to vegetated areas or areas of no soil exposure. 

Results and Knowledge Gained:

At the end of this lab, I was able to collect and analyze spectral signature curves for various Earth surface and near surface features for any multispectral remotely sensed image and also monitor the health of vegetation and soils(Wilson, 2020). 

Data source:

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

Survey.

Wilson, C. (2020) Spectral signature analysis & resource monitoring, LAB 5, GEOG 338: Remote sensing of the Environment (pp. 1-16).