PE&RS March 1998

Cover Image

This mosaic of Landsat Thematic Mapper data and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data of a portion of the Bighorn Basin, northeast of Worland, Wyoming, USA, illustrates the synergy between large area coverage by satellite sensors and targeted airborne imaging spectrometer (or “hyperspectral”) data. The background image is a Landsat Thematic Mapper color composite of bands 3, 2, 1 (true color), saturation-enhanced to contrast with the AVIRIS overlay.  North is to the bottom of the page. The 3-D hyperspectral image cube demonstrates the high spectral resolution nature of the AVIRIS data, which has 224 bands covering the spectral range of 0.4 – 2.5 mm at 10 nm resolution.  The AVIRIS cube face is a true color composite utilizing bands 31, 19, and 10 (0.66 mm, 0.55 mm, and 0.45 mm). The cube sides represent color-coded reflectance spectra for the pixels along the edges of the image. The color scheme of black, blue, green, yellow, red, and white indicates increasing reflectance. These data are used together in a hierarchical strategy for geologic mapping and exploration utilizing Landsat to provide an overview and to locate targets for more detailed study, the hyperspectral data for mineral species and abundance mapping, and field mapping and field spectroscopy for verification. Commercialization of hyperspectral systems is underway, and these data will soon be widely available. Commercial aircraft systems are being flown now, and several satellite systems are planned for deployment by the year 2000.

Landsat data courtesy of EROS Data Center. The AVIRIS data were acquired 7 August 1996 by Jet Propulsion Laboratory and processed by Analytical Imaging and Geophysics, Boulder, Colorado using ENVI, the “Environment for Visualizing Images” distributed by Research Systems.  For additional information, contact Research Systems Inc., 303-786-9900, or www.rsinc.com.

Peer-Reviewed Articles (Click the linked titles to see the full abstract)

191 Challenging the Cloud-Contamination Problem in Flood Monitoring with NOAA/AVHRR Imagery
Yongwei Sheng, Yafang Su, and Qianguang Xiao

Water bodies can be identified not only in cloud-free areas, but also under semi-transparent clouds and in cloud shadows. 

199 Extension of Climate Parameters over the Land Surface by the Use of NOAA-AVHRR and Ancillary Data
Fabio Maselli, Ljiljana Petkov, and Giampiero Maracchi

Information about three major climatic parameters (mean annual temperature, and lengths of arid and cold seasons) is extracted from NOAA-AVHRR and ancillary data in order to extend the relevant estimates over the land surface in a complex Italian region (Tuscany). 

207 A Quantitative Comparison of Change-Detection Algorithms for Monitoring Eelgrass from Remotely Sensed Data
Robb D. Macleod and Russell G. Congalton

The image differencing change-detection technique performed significantly better than the post-classification and principal components techniques when employing Landsat TM data. 

217 Nonparametric Classifier for GIS Data Applied to Kangaroo Distribution Mapping
Andrew K. Skidmore

The algorithm is trained using GIS data layers as the independent variables, and predicts the spatial distribution of a dependent variable using a nonparametric technique.  

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