Lab 8: Spectral signature analysis & resource monitoring

Goal

The purpose of this lab is to work with measurement and interpretation techniques for spectral reflectance signatures of various materials in satellite images and to perform basic resource monitoring using remote sensing band ratio techniques. During this exercise, I collected spectral signatures from remotely sensed images, graphed them, and performed analysis on them in order to test spectral separability. This is an important step in image classification. I also monitored the health of vegetation and soils using basic band ratio techniques.

The main goal of this lab was to equip me with the necessary skills to collect and analyze spectral signature curves, specifically in order to monitor the health of vegetation and soils.

Upon completion of this last introductory remote sensing lab, the idea is that I am now equipped with the necessary skills for an entry level remote sensing job, and am also equipped to work on an independent project to complete this class.

Objectives

1.       Spectral signature analysis

2.       Resource monitoring

·         Vegetation health monitoring

·         Soil health monitoring

·         Prepare a map to show mineral distribution

Part 1: Spectral signature analysis

In this part of the lab, you are going to use a Landsat ETM+ image (taken in 2000) that covers the Eau Claire area and other regions in WI, and MN to collect and analyze spectral signatures of the following earth features:

1. Standing Water

2. Moving water

3. Vegetation

4. Riparian vegetation.

5. Crops

6. Urban Grass

7. Dry soil (uncultivated)

8. Moist soil (uncultivated)

9. Rock

10. Asphalt highway

11. Airport runway

12. Concrete surface (Parking lot)

The Field Spectrometer Pro instrument takes reflectance measurements in the visible, Near-Infrared and Mid-Infrared regions of electromagnetic spectrum (0.4 – 2.5 μm).

The first spectral signature collected was from Lake Wissota, located in the north-northeast part of Eau Claire. Using the polygon tool (under Home>Drawing), I selected an AOI inside Lake Wissota (Fig. 1). I then opened the Signature Editor (Raster>Supervised>Signature Editor) and graphed the spectral curve of this standing water signature (Fig. 2). The water curve is characterized by high absorption at near infrared wavelengths range and beyond, which is why the signature curve shows such low reflectance at 0.4 and above.

Figure 1. The selected area of Lake Wissota near Eau Claire, Wisconsin provided the spectral signature for the Standing Water category.

Figure 2. From the signature mean plot of standing water, I gathered that the band with the highest reflectance was 0.1 and the band with the lowest reflectance was 0.4 micrometers, though 0.6 was also comparably low.

I continued to select areas from the photo to represent the other 11 categories, using Google Earth imagery as ancillary data. The Signature Editor window (Fig.3) displays the other categories and their details. These were compiled into the Spectral Signature graph (Fig.4).

Figure 3. The Signature Editor window containing the twelve categories. 

Figure 4. Comparing the spectral signatures of the different materials gives us a rounded understanding of which bands (layers) provide the largest distinction between reflection levels of the different materials. 

Part 2: Resource monitoring

Section 1: Vegetation health monitoring

In this section of the lab, I performed a band ratio on the ec_cpw_2000.img image by implementing the normalized difference vegetation index (NDVI).

Using the Raster>Unsupervised>NDVI tool to open the Indices interface, I used the ‘Landsat 7 Multispectral’ sensor to generate an NDVI image. Unfortunately, the ERDAS software had a malfunction before I could take a screenshot of my NDVI image. 

Section 2: Soil health monitoring

For this final section, I used simple band ratio by invoking the ferrous mineral

ratio on the ec_cpw_2000.img image (Fig.5) to monitor the spatial distribution of iron contents in soils within Eau Claire and Chippewa counties (Fig. 6).

Figure 5. Eau Claire and Chippewa Counties in false color. This was the image used in both parts of Lab 8.

Figure 6. The final product was this image depicting Ferrous Mineral deposits in white. Notice that the distribution of these minerals are on the west side of the Chippewa River.

Sources

Data for this lab exercise was provided to the students of Geography 338: Remote Sensing of the Environment by Dr. Cyril Wilson, Professor of Geography at University of Wisconsin Eau Claire.

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