Lab 9: Hyperspectral Remote Sensing

Introduction

The purpose of this lab is to familiarize ourselves with hyperspectral remote sensing. To do this we used ENVI a advanced software that allows for hyperspectral data to be analysed.  Hyperspectal remote sensing aids in identifying land surface more accurately than other traditional types of remotely sensed data. By narrowing the spectral bands than other methods, analysts can better differentiate between land surface materials over tradional remote sensing techniques. Specifically, this lab is designed to introduce us to spectrometry, hyperspectral images and different spectral processing techniques, Fast Line-of-sight Atmospheric Analysis of Hypercubes FLAASH to atmospherically correct hyperspectral image, and determine the state of different vegetation types.

Methods

To begin the lab were asked to extract spectral charactaristics from regions of interest (ROI) from hyperspectral imagery using ENVI. The pre-determoned ROIs were brought into the image aloong with the statistical and spectral plots for each of the ROIs. Each of the ROIs were collected over regions that contained specific minerals. The spectral profiles for this minerals were brought into plots and stacked.  

The next section of this lab envolved using FLAASH to atmospherically correct images. The University did not have the proper lincense to complete the processing of the images so we were given the correct image that was previously corrected by FLAASH. To analyse the corrected image, the original image and the corrected image were brought into ENVI and compared side-by-side. 

The third and final section of this lab was completing vegetation analysis of hyperspectral imagery. This was done by using images that had been previously corrected using FLAASH. This was done by using the Vegetation Index Calculator. Within this tool there are 27 different indices. For this lab we used 3: the Agricultural Stress Tool, the Fire Fuel Tool, and the Forest Health Tool.  The Agricultural Stress Tool measures greenness, canopy water cover, canopy nitrogen, light use efficiency, and leaf pigments. The Fire Fuel Tool measures greenness, canopy water content, and dry or senescent carbon. Lastly, the Forest Health Tool measures greenness, leaf pigments, canopy water content, and light use efficiency.

Results

Figure 1. Image on the left is uncorrected image and the image on the right is FLAASH corrected image. The ROI is that of vegetation and their respective spectral profiles can be seen in the image. 

Figure 2. Agricultural Stress output image

Figure 3. Fire Fuel output image

Figure 4. Forest Health output image

Figure 5. Minimum Noise Fraction tool used in NDVI, used to reduce noise in the image

Conclusion

Hyperspectral Remote Sensing has the ability to produce highly accurate images relative to traditional remote sensing techniques. By using narrower spectral channels hyperspectral remote sensed images have the ability to better delineate different surface charartistics. These charatistics include both chemical and physical properties of image objects.  For this lab we were able to process and analyse images regarding vegetation and minerals.