Landsat Pathfinder data sets for landscape change analysis

• Published in: IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium Vol. 1; pp. 547 - 550 vol.1
• Main Authors: Dwyer, J.L., Sayler, K.L., Zylstra, G.J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 1996
• Subjects: Area measurement; Assembly; Brightness; Costs; NASA; Remote sensing; Satellites; Signal resolution; Spatial resolution; Vegetation mapping
• ISBN: 9780780330689; 0780330684
• DOI: 10.1109/IGARSS.1996.516399

The North American Landscape Characterization (NALC) project is a component of the National Aeronautics and Space Administration (NASA) Landsat Pathfinder Program which supports scientific research on issues related to global environmental change. The increasing volume of multiresolution satellite data presents researchers with unique opportunities to conduct large-area investigations. More than 20 years of historical Landsat data provide regular observations of consistent measurements at relatively high spatial resolution at increasingly reduced cost to researchers. The NALC project involves the assembly of Landsat multispectral scanner (MSS) data "triplicates" covering the conterminous U.S. and Mexico. The NBLC triplicates an comprised of co-registered Landsat MSS data acquired in 1973, 1986, and 1992 (plus or minus one year) and a co-registered digital elevation model (DEM). The 1980s and 1990s data sets also include a normalized difference vegetation index band. The NALC triplicates are being used to study human-induced land transformations and landscape ecology, to monitor the effects of land management practices on resource condition, to investigate the role of land cover change in biogeochemical cycles, and to study feedbacks between climate and land cover change. Algorithms have been developed for processing Landsat MSS and TM data to identify and characterize landscape change. The change analysis procedure involves: (1) transformation of the imagery to scene-based measures of brightness, greenness, and wetness (TM data only), (2) pairwise differencing of the brightness, greenness, and wetness measures to compute change vectors for each image pixel, (3) encoding the change vectors using hue, saturation, and value for visualization, and (4) formulating a signal-to-noise model by which to isolate areas of "significant" change. These algorithms have been applied to various NALC data sets, and the results have been provided to collaborators for review and comment. The research topics that have been investigated include regional climatic influence on forest condition and water supplies In the Sierra Nevada from 1985 to 1992, land management practices in natural grasslands in Nebraska, the phenologic response of vegetation to seasonal climatic conditions in semiarid regions, and expansion of urban and suburban land use.