Title :
Herbaceous biomass retrieval in habitats of complex composition: a model merging SAR images with unmixed landsat TM data
Author :
Svoray, Tal ; Shoshany, Maxim
Author_Institution :
Dept. of Geogr. & Environ. Dev., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel
fDate :
7/1/2003 12:00:00 AM
Abstract :
A remote sensing methodology for herbaceous areal above-ground biomass (AAB) estimation in a heterogeneous Mediterranean environment is presented. The methodology is based on an adaptation of the semiempirical water-cloud backscatter model to complex vegetation canopies combined with shrubs, dwarf shrubs, and herbaceous plants. The model included usage of the green leaf biomass volumetric density as a canopy descriptor and of cover fractions derived from unmixing Landsat Thematic Mapper image data for the three vegetation formations. The inclusion of the unmixed cover fractions improves modeling synthetic aperture radar backscatter, as it allows separation between the different radiation interaction mechanisms. The method was first assessed with reference to the reproduction of the backscatter from the vegetation formations. In the next phase, the accuracy of AAB retrievals from the backscatter data was evaluated. Results of testing the methodology in a region of climatic gradient in central Israel have shown a good correspondence between observed and predicted AAB values (R2=0.82). This indicates that the methodology developed may lay a basis for mapping important and more advanced ecological information such as primary production and contribute to better understanding of processes in Mediterranean and semiarid regions.
Keywords :
geophysical signal processing; geophysical techniques; image processing; radar imaging; remote sensing; remote sensing by radar; sensor fusion; synthetic aperture radar; 350 to 2500 nm; IR; Israel; Landsat TM; Mediterranean; SAR; backscatter; canopy; complex canopies; complex habitat; geophysical measurement technique; green leaf biomass; herbaceous biomass; image processing; infrared; merging; model; multispectral remote sensing; optical imaging; radar image; radar remote sensing; radar scattering; sensor fusion; shrubs; synthetic aperture radar; vegetation mapping; visible; volumetric density; Backscatter; Biomass; Image retrieval; Information retrieval; Merging; Production; Remote sensing; Satellites; Testing; Vegetation mapping;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2003.813351