Remote Sensing Lab 8

Remote Sensing Lab 8

Goals and Background:

This lab will introduce the user to measuring and examining spectral signatures of various features. Collection, analysis, and display methods will be covered for all spectral signatures discussed. In addition, vegetation health analysis is covered as well as soil analysis, with the focus of ferrous materials (iron content).

Methods 

Part 1

We will start by collecting, displaying, and analyzing spectral signatures of 12 different features. These features include:
Standing Water
Running Water
Deciduous Forest
Evergreen Forest
Riparian Vegetation
Crops
Dry Soil
Moist Soil
Rock
Asphalt Highway
Airport Runway
Concrete Surface

Collection begins by first drawing a polygon over the feature with care not to include any unrelated pixels. Then, the Signature Editor is accessed (found under Supervised in the Raster Tab) and a new signature is added with the "Add signature from AOI" option using the already drawn polygon.

figure 1: The Signature Editor window, with the first signature of standing water added.

We will repeat the process for the other 11 features as well. To display a signature, we can select the "Plot Signature Mean" option, which will show the mean reflectance for each band (wavelength) for the spectral signature. This tool can also plot multiple signatures at a time, allowing for the user to analyze differences for similar features.

figure 2: The signature mean plot for standing water. 

Part 2

Section 1

Spectral characteristics can also be used for large scale analysis as well. Using a simple band ratio, we can examine vegetation health and abundance in the AOI of Eau Claire and Chippewa counties. Called the normalized difference vegetation index (NDVI), it makes use of the following equation:

NDVI = (NIR - RED) / (NIR + RED)

To perform this analysis, we will use the Indices tool found under Unsupervised in the Raster tab, with the NDVI option selected for the index. In addition, we specify Landsat 7 Multispectral for the sensor, to match our input image.

figure 3: The Indices tool with the NDVI index selected. 

Section 2

We can also perform a similar band ratio to determine the abundance of ferrous material (iron content) the soil has. We will make use of the following equation:

Ferrous material: MIR / NIR

We will again use the Indices tool, but this time select Ferrous Materials as the index. We will also again specify Landsat 7, as we are using the same input image as before.

figure 4: The Indices tool with Ferrous Materials selected as the index.

Results

figure 5: The spectral signatures for the various features. Similar features such as the Asphalt Highway and Airport Runway have very similar signatures throughout all 6 bands. Interesting to note, however, is where they diverge. For example, while the previously mentioned Highway and Runway are very similar, a Concrete Surface (a bridge) supposedly made from similar material greatly diverges from them in the 4th, 5th, and 6th bands. This could be due to a variety of factors, such as some of the reflectance of the water under the bridge being captured in some of the bridge's pixels

figure 6: A map showing abundance of vegetation in Eau Claire and Chippewa counties. Vegetation abundance unsurprisingly seems to follow agricultural patterns; while cropland has a high amount of vegetation, urban landscapes have very little or even lack vegetation.

figure 7: A map showing ferrous material abundance in Eau Claire and Chippewa counties. Interesting to note is the mostly western spatial distribution for high ferrous material content. This is most likely related to underlying geological deposits. 

Sources

Earth Resources Observation and Science (EROS) Center. (n.d.). Retrieved May 14, 2019, from https://www.usgs.gov/centers/eros