Overview of This Tutorial

This tutorial presents a case history for use of hyperspectral techniques for vegetation analysis using 1997 AVIRIS data from Jasper Ridge, 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.

Objectives

1) To examine application of ENVI end-to-end hyperspectral processing methodology to a vegetation 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.

Files Used in This Tutorial

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 J97AVSUB subdirectory of the ENVIDATA directory on the ENVI TUTORIALS & DATA CD-ROM.

The files listed below are required to run this exercise. Miscellaneous files listed below provide additional information that may be useful. 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.

Required Files (in the Default Spectral Library Directory)

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

VEG_2GRN.SL	Jasper Ridge, Spectral Library

VEG_2GRN.HDR	ENVI Header for Above

Required Files (in the J97AVSUB Directory)

J97RDSUB.BIL	JPL AVIRIS Radiance, 300 x 250 x 224 bands

J97RDSUB.HDR	ENVI Header for Above

J97ATSUB.BIL	AVIRIS ATREM Corrected Data, 300 x 250 x 224 bands

J97ATSUB.HDR	ENVI Header for Above

JASP97WV.IMG	ATREM Water Vapor Image

JASP97WV.HDR	ENVI Header for Above

J97EFSUB.BIL	AVIRIS ATREM with EFFORT Correction Applied

J97EFSUB.HDR	ENVI Header for Above

J97EFSUB.DAT	Effort Gains and Offsets

J97MNF_1.IMG	VNIR MNF Transformed data, B 4 -102 less 33-34, 97-98

J97MNF_1.HDR	ENVI Header for Above

J97MNF_1.ASC	VNIR Eigenvalue plot data

J97MNF_2.IMG	SWIR MNF Transformed data B173 - 223, 50 Bands

J97MNF_2.HDR	ENVI Header for Above

J97MNF_2.ASC	SWIR Eigenvalue plot data

J97PPI_1.IMG	VNIR PPI image

J97PPI_1.HDR	ENVI Header for Above

J97PPI_1.ROI	ROI of VNIR PPI threshold

J97_SAV1.NDV	VNIR N-D Visualizer Save State

J97_EM1.ASC	VNIR ASCII File of Endmember Spectra - all EM

J97_EM1.ROI	VNIR ASCII File of Endmember Spectra - all EM

J97_EM1A.ASC	VNIR ROI File of Endmember Locations - selected EM

J97_EM1V.ASC	VNIR ASCII File of Endmember Spectra - selected vegetation EM

J97_SAM1.IMG	VNIR SAM Classes using J97_EM1A.ASC

J97_SAM1.HDR	ENVI Header for Above

J97_RUL1.IMG	VNIR SAM Rules

J97_RUL1.HDR	ENVI Header for Above

J97SAM1V.IMG	VNIR SAM Classes using J97_EM1V.ASC (vegetation only)

J97SAM1V.HDR	ENVI Header for Above

J97RUL1V.IMG	VNIR SAM Rules Classes using J97_EM1V.ASC (vegetation only)

J97RUL1V.HDR	ENVI Header for Above

J97_UNM1.IMG	VNIR Linear Spectral Unmixing using J97_EM1.ASC

J97_UNM1.HDR	ENVI Header for Above

J97PPI_2.IMG	SWIR PPI image

J97PPI_2.HDR	ENVI Header for Above

J7PPI_2.ROI	ROI of SWIR PPI threshold

J97_SAV2.NDV	SWIR N-D Visualizer Save State

J97_EM2.ASC	SWIR ASCII File of all Endmember Spectra

J97_EM2A.ASC	SWIR ASCII File of selected Endmember Spectra

J97_SAM2.IMG	SWIR SAM Classes using J97_EM2A.ASC

J97_SAM2.HDR	ENVI Header for Above

J97_RUL2.IMG	SWIR SAM Rules

J97_RUL2.HDR	ENVI Header for Above

J97_UNM2.IMG	SWIR Linear Spectral Unmixing Results

J97_UNM2.HDR	ENVI Header for Above

Other Miscellaneous Files (in the J97AVSUB Directory)

JASP97.AVH	JPL AVIRIS Header File

JASP97.WAV	1997 AVIRIS Wavelength file

JASP97.OCC	JPL On-Board-Calibrator file

JASP97.SPC	JPL Spectral Calibration File

JASP97.GN	JPL DN-to-Radiance Gain File

Tasks

1. Examine JPL AVIRIS radiance data and evaluate data characteristics and quality.
2. Evaluate Atmospherically Corrected (ATREM) AVIRIS data and compare to radiance data. Run and compare empirical correction methods.
3. Conduct Spatial/Spectral browsing to evaluate data, determine presence and nature of spectral variability, and to select wavelength range (s) for further analysis
4. Reduce data dimensionality using MNF transform
5. Select spectral endmember candidates using PPI
6. Evaluate linearity and select endmembers using n-D Visualizer
7. Map endmember distribution and abundance using ENVI mapping methods. Compare and contrast results
8. Reconcile image and field spectra/ground information

Jasper Ridge Background Materials

Remote Sensing Test Site Used By JPL and others since early 1980s

AVIRIS Standard Datasets for 1992-97

Detailed Maps and Ground Spectra Available.

Figure 1: Jasper Ridge Orthophoto

Figure 2: Jasper Ridge Trails and Shaded Relief, Stanford University

Figure 3: Jasper Ridge Vegetation Map, Stanford Univerisity

Figure 4: Jasper Ridge multitemporal AVIRIS Data

Figure 5: Jasper Ridge AVIRIS Rock and Soil Spectra

Figure 6: Jasper Ridge AVIRIS Vegetation Spectra

AVIRIS Processing Flow

The Figure below 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

• Examine JPL AVIRIS radiance data: Load 224-band AVIRIS spatial subset as grayscale images. Perform animation. Extract radiance signatures for areas of high variability. Examine radiance spectra for evidence of absorption features. Determine bad spectral bands. Load color composite images designed to enhance spectral contrast. Determine spectral subset(s) to use for materials mapping for vegetation and/or minerals.

Files:

J97RDSUB.BIL JPL Radiance

• Evaluate ATREM Correction applied to the JPL-provided AVIRIS spectral radiance to remove the bulk of the solar and atmospheric effects, transforming the data from radiance to apparent surface reflectance. Examine the data using spectral/spatial browsing and color composites to characterize spectral variability and determine residual errors. Review the water vapor image and relate to topography. Extract reflectance signatures for vegetation and geologic materials. Compare to spectral libraries.

Files:

J97ATSUB.BIL ATREM Apparent Reflectance

J97WVSUB.IMG	ATREM Water Vapor Image

USGS_VEG.SLI	Vegetation Spectral Library

USGS_MIN.SLI	Mineral Spectral Library

VEG_2GRN.SL	Jasper Ridge, Spectral Library

VEG_2GRN.HDR	ENVI Header for Above

• Evaluate EFFORT Correction applied to the ATREM data to remove residual errors. Review the gains and offsets used to make the correction using a plot window. Compare the EFFORT data to the ATREM apparent reflectance by examining the data using spectral/spatial browsing. Extract reflectance signatures for vegetation and geologic materials. Compare to spectral libraries.

Files:

J97EFSUB.BIL ATREM Apparent Reflectance with EFFORT Applied

J97EFSUB.DAT	EFFORT Gains and Offsets

• Apply MNF Transform to a spectral subset of the EFFORT data to find the data's inherent dimensionality. Review MNF eigenvalue plot(s) to determine break-in-slope and relate to spatial coherency in MNF eigenimages. Determine MNF cut-off between "signal" and "noise" for further analysis.

Files: Make your own MNF-Transformed dataset or review the results in the files below

J97_MNF1.ASC	VNIR Eigenvalue ASCII Data

JP7_MNF1.IMG	VNIR MNF Eigenimages on 94 bands as listed above

J97_MNF2.ASC	SWIR Eigenvalue ASCII Data

J97_MNF2.IMG	SWIR MNF Eigenimages on 50 bands as listed above

• 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.

Files: Generate your own PPI results and ROIs or review the results in the files below

J97PPI_1.IMG	VNIR PPI Image

J97PPI_1.ROI	VNIR PPI Threshold Results

J97PPI_2.IMG	SWIR PPI Image

J97PPI_2.ROI	SWIR PPI Threshold Results

• Perform n-Dimensional Visualization of the high PPI value pixels, using the high signal MNF data bands to cluster the purest pixels into image-derived endmembers. Rotate the MNF data interactively in 3-D, or spin in 3-or-more dimensions and "paint" pixels that occur on the "points" (extremities) of the scatterplot. Use Z-Profiles connected to the EFFORT apparent reflectance data and the n-D Visualizer to evaluate spectral classes. Use class collapsing to iteratively find all of the endmembers. Evaluate the linearity of vegetation mixing and endmembers. Save your n-D results to a save state file (.ndv). Export classes to ROIs and extract mean spectra. Compare mean spectra to spectral libraries. Use spectral/spatial browsing to compare image spectra to ROI means.

Files: Extract endmembers and make your own ROIs or review the results below

J97_MNF1.IMG	VNIR MNF Eigenimages on 94 bands as listed above

J97_MNF2.IMG	SWIR MNF Eigenimages on 50 bands as listed above

J97EFSUB.BIL ATREM Apparent Reflectance with EFFORT Applied

J97_SAV1.NDV	VNIR n-d visualizer saved state

J97_SAV2.NDV	SWIR n-d visualizer saved state

J97_EM1.ASC	VNIR Saved ASCII Endmember Spectra

J97_EM2.ASC	SWIR Saved ASCII Endmember Spectra

USGS_VEG.SLI	Vegetation Spectral Library

USGS_MIN.SLI	Mineral Spectral Library

VEG_2GRN.SL	Jasper Ridge, Spectral Library

VEG_2GRN.HDR	ENVI Header for Above

• Use ENVI's wide variety of mapping methods to map the spatial occurrence and abundance of materials at Jasper Ridge. As a minimum, try the Spectral Angle Mapper (SAM) and Unconstrained Linear Unmixing. Use SAM to determine spectral similarity to image endmember spectra Perform your own SAM Classification or review the results below. If time and space permit, try a SAM classification using one of the Spectral Libraries. Be sure to evaluate the Rule Images. Use the Unconstrained Linear Unmixing to determine material abundances. Be sure to examine the RMS error image and evaluate whether the physical constrains of non-negative and sum to unity (1) or less have been satisfied. Iterate if time and space permit. Compare abundance image results to the endmember spectra and spectral libraries using spatial/spectral browsing. If time and space permit, try running Mixture-Tuned Matched filtering and/or Spectral Feature Fitting.

Files:

J97_EM1.ASC	VNIR Saved ASCII Endmember Spectra

J97_EM2.ASC	SWIR Saved ASCII Endmember Spectra

J97EFSUB.BIL ATREM Apparent Reflectance with EFFORT Applied

J97_SAM1.IMG	VNIR SAM Classes

J97_RUL1.IMG	VNIR SAM Rules

J97_UNM1.IMG	VNIR Linear Spectral Unmixing Results

J97_SAM1.IMG	SWIR SAM Classes

J97_RUL1.IMG	SWIR SAM Rules

J97_UNM1.IMG	SWIR Linear Spectral Unmixing Results

USGS_VEG.SLI	Vegetation Spectral Library

USGS_MIN.SLI	Mineral Spectral Library

VEG_2GRN.SL	Jasper Ridge, Spectral Library

VEG_2GRN.HDR	ENVI Header for Above

Source: http://www.ltid.inpe.br