PARENT SESSION
Poster Session #15: Vegetation Analysis.
Tuesday, August 7, 2001. Presentation from 10:30 AM to 12:00 PM. Exhibition Hall
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Ground truthing remotely sensed vegetation parameters for arid land assessment and management.
Biedenbender, Sharon1, Marsett, Robert1, Qi, Jiaguo2, Watson, Mary1, Heilman, Philip1, 1 2
ABSTRACT- The Rangeland Analysis Utilizing Geospatial Science Project (RANGES) is researching Landsat 7 satellite imagery as a tool for quantitatively estimating vegetation parameters for arid land assessment and management. Satellite imagery offers more frequent and extensive coverage than ground vegetation surveys for areas too large to be regularly monitored by ground-based methods, and it can provide real-time information in a cost-effective way. Remote sensing technology has successfully given quantitative estimates of green vegetation cover, but soil background has made it difficult to isolate and quantify senescent vegetation. Senescent biomass and cover are critical on arid lands for forage, fire fuel loads, soil protection, and wildlife habitat. The RANGES Project is focusing its efforts on semidesert grassland, ponderosa pine, oak woodland, and semidesert shrub ecosystems in Arizona and New Mexico. Seasonal vegetation data collections are conducted to calibrate the satellite imagery. Vegetation parameters include standing green and senescent biomass and percent green and senescent canopy cover. The regression relationship between satellite image and ground estimates of percent green vegetation for four sites in 2000 was very good (R2=0.99). Likewise, the regression between satellite and ground estimates of total canopy cover was R2=0.91. The proportion of senescent vegetation cover can be derived from satellite and ground measurements of total cover and percent green. These finding indicate that remote sensing can provide quantitative estimates of vegetation parameters that will facilitate arid land assessment and management.
KEY WORDS: arid land vegetation, vegetation monitoring, remote sensing and vegetation, senescent vegetation