This tutorial presents a case history for use of hyperspectral techniques for vegetation analysis using 1994 AVIRIS data from Moffett Field, California, USA. It is designed to be a self-directed example using ENVI's complete end-to-end hyperspectral tools to produce image-derived endmember spectra and image maps. For more detail and step-by-step procedures on performing such a hyperspectral analysis, please execute tutorials 7-11 in this booklet prior to attempting this tutorial.
1) To examine application of ENVI end-to-end hyperspectral processing methodology to a near-shore marine case study
2) To give students hands-on experience in actually running the procedures rather than reviewing pre-calculated results (preprocessed results are provided for comparison)
3) To provide students with guidance to perform data exploration in a loosely structured framework
4) To compare analysis results with known ground information.
You must have the ENVI TUTORIALS & DATA CD-ROM mounted on your system to access the files used by this tutorial, or copy the files to your disk.
The files used in this tutorial are contained in the M94AVSUB subdirectory of the ENVIDATA directory on the ENVI TUTORIALS & DATA CD-ROM.
The files listed below are required to run this exercise. Selected data files have been converted to integer format by multiplying the reflectance values by 1000 because of disk space considerations. Values of 1000 in the files represent reflectance values of 1.0.
USGS_VEG.SLI USGS Vegetation Spectral Library
USGS_VEG.HDR ENVI Header for Above
USGS_MIN.SLI USGS Mineral Spectral Library
USGS_MIN.HDR ENVI Header for Above
M94AV.BIL AVIRIS ATREM Corrected Data, 500 x 350 x 56 bands
M94AV.HDR ENVI Header for Above
M94MNF.IMG VNIR MNF Transformed data
M94MNF.HDR ENVI Header for Above
M94MNF.ASC VNIR Eigenvalue plot data
M94PPI.IMG VNIR PPI image
M94PPI.HDR ENVI Header for Above
M94PPI.ROI ROI of VNIR PPI threshld
M94_EM.ASC VNIR ASCII File of Endmember Spectra - all EM
M94_EM.ROI VNIR ROI File of Endmember Spectra - all EM
M94_EMA.ASC VNIR ASCII File of Endmember Locations - selected EM
M94_SAM1.IMG VNIR SAM Classes using M94EM1A.ASC
M94_SAM1.HDR ENVI Header for Above
M94_RUL1.IMG VNIR SAM Rules
M94_RUL1.HDR ENVI Header for Above
M94_UNM1.IMG VNIR Unmixing image using M94EM1A.ASC
M94_UNM1.HDR ENVI Header for above
The salt ponds are highly colored and contain a dense biomass of algae and/or photosynthetic bacteria (Richardson et al., 1994). Accessory bacterial pigments cause distinct spectral signatures that can be detected using AVIRIS data. These include carotenoids, phycocyanin, and cholorphyll a and b. Application of the standardized AVIRIS analysis methods described below should lead to the extraction of endmembers from the data and spatial mapping of their distribution and abundance. There are obvious mixing non-linearities in the data, however, and care must be taken to recognize these.
Figure 1: Moffett Field AVIRIS True Color Composite Image.
The following diagram (Figure 2) illustrates an approach for analysis of hyperspectral data that is implemented with ENVI
The following outlines in general terms the implementation of this approach. The student is expected to follow the procedures below, referring to previous tutorials and the ENVI User's Guide for guidance in performing specific tasks where required. The purpose of this tutorial isn't to teach you how to run the ENVI tools, but how to apply the methodology and tools to a general hyperspectral remote sensing problem
Files:M94AV.BIL ATREM Apparent ReflectanceUSGS_VEG.SLI Vegetation Spectral LibraryUSGS_MIN.SLI Mineral Spectral Library
Figure 3: MNF Eigenvalue Plot
Files: Make your own MNF-Transformed dataset or review the results in the files belowM94AV.BIL ATREM Apparent ReflectanceM94MNF.ASC VNIR Eigenvalue ASCII DataM94MNF.IMG VNIR MNF Eigenimages
Figure 4: Top MNF Band 1, Bottom MNF Band 20.
Apply PPI Analysis to the MNF output to rank the pixels based on relative purity and spectral extremity. Use the FAST PPI option to perform calculations quickly in system memory, creating the PPI image. Display the PPI image, examine the histogram, and threshold to create a list of the purest pixels, spatially compressing the data.
Figure 5: PPI Image.
Files: Generate your own PPI results and ROIs or review the results in the files belowM94MNF.IMG VNIR MNF EigenimagesM94PPI.IMG VNIR PPI ImageM94PPI.ROI VNIR PPI Threshold Results
Files: Extract endmembers and make your own ROIs or review the results belowM94MNF.IMG VNIR MNF EigenimagesM94PPI.ROIM94AV.BIL ATREM Apparent ReflectanceM94_EM.ASC VNIR Saved ASCII Endmember Spectra (all)M94_EMA.ASC Selected VNIR Saved ASCII EndmembersUSGS_VEG.SLI Vegetation Spectral LibraryUSGS_MIN.SLI Mineral Spectral Library
Figure 6. Spectral Unmixing Results: Red Pigment (UL), Green Pigment (LL),
Vegetation 1(UR), Vegetation 2(LR)
Files:M94_EM.ASC VNIR Saved ASCII Endmember SpectraM94_EMA.ASC Selected Saved ASCII EndmembersM94AV.BIL ATREM Apparent ReflectanceM94_SAM1.IMG VNIR SAM ClassesM94_RUL1.IMG VNIR SAM RulesM94_UNM1.IMG VNIR Linear Spectral Unmixing ResultsUSGS_VEG.SLI Vegetation Spectral LibraryUSGS_MIN.SLI Mineral Spectral Library
Richardson, L.L., 1996, Remote Sensing of Algal Bloom Dynamics: BioScience, V. 46, No. 7, p. 492 - 501.
Richardson, L.L, Buison, D., Lui, C.J., and Ambrosia, V., 1994, The detection of algal photosynthetic accessory pibgments using Airborne Visible-Infrared imaging Spectrometer (AVIRIS) Spectral Data: Marine Technology Society Journal, V. 28, p. 10-21.