GEOG 438 Advanced Remote Sensing: Lab 9 - Hyperspectral Remote Sensing

Goal

The goal of this lab exercise was to introduce us to the processing of hyperspectral remotely sensed images. The ways in which this lab introduced us to hyperspectral remote sensing was through some various basics of hyperspectral and spectroscopy, using FLAASH for the purposes of atmospheric corrections, and various tools to determine vegetation health.

Methods

Part 1 - Introduction to Hyperspectral Remote Sensing

The first portion of this lab was devoted to viewing and basic analysis of hyperspectral images in the ENVI software. In ENVI, we began by loading in select bands from the image and importing ROI's (Regions of Interest) and plotting them to view there spectral signatures. Once this was done, other spectral signatures were imported from a spectral library, in this case the JPL spectral library. In addition to viewing spectral signatures of ROI's and reference signatures, this portion of the lab also demonstrated animating the image data. Using the animation tool in ENVI, we animated all the individual bands in a slideshow that can be used to view if there are any bad bands or images with errors.

Part 2 - Atmospheric Correction Using FLAASH

The second portion of this lab exercise was devoted to using FLAASH to conduct atmospheric correction on the hyperspectral imagery. As UWEC does not have the license for FLAASH, we just walked through the steps of using FLAASH rather than actually executing it. The first step to conduct FLAASH correction was to open the FLAASH window and input the desired image and then select the "Read array of scale factors (1 per band) from ASCII file" radio button to import a text file to import the desired parameters. Other default parameters were accepted and the FLAASH tool was "run." Next, we viewed the "corrected" image and viewed which bands FLAASH flagged as being bad from the bad bands list.

Part 3 - Vegetation Analysis

Part 3 of this lab was all about various forms of vegetation that can be accomplished using hyperspectral imagery. The first vegetation index we used was the Vegetation Index Calculator to calculate an NDVI image. Some other indices that were tested at this stage of the lab were things like water indices, red edge indices, and Lignin indices. Once this was done, some vegetation analysis tool were run. The first of these tools was the Agricultural Stress Tool to create a image showing areas of vegetation under stress. After this tool, the next tool was the Fire Fuel Tool which measures areas of vegetation that are more or less susceptible to fires. In using this tool, we also gained experience using the Mask Band tool to mask out portions of the image that we did not wasn't to analyze, such as the urban areas in the image. The final tool run in this portion of the lab was the Forest Heath tool which measures overall vegetation health based on parameters like water content, greenness, etc.

Part 4 - Hyperspectral Transformation

The final portion of this lab was devoted to using a MNF (Minimum Noise Fraction) transform tool to determine image dimensionality, reduce computational requirements, and remove noise data from the image. To do this, we used the MNF Rotation > Forward MNF > Estimate Noise Statistics from Data tool and input the provided noise statistics. Once this was done, we viewed the Eigenvalues plot of the MNF transformed image.