The conservation of Jordan's Mediterranean forest requires the use of remote sensing. Among the most important parameters needed are the crown-cover percentage (C) and above-ground biomass (A). This study aims to: (1) identify the best predictor(s) of C using Landsat Enhanced Thematic Mapper (ETM) bands and the derived transformed normalized difference vegetation index (TNDVI); (2) determine if C is a good predictor of A, volume (V), Shannon diversity index (S) and basal area (B); and (3) generate maps of all these parameters. A Landsat ETM image, aerial photographs and ground surveys are used to model C using multiple regression. C is then modelled to A, V, S and B using linear regression. The relationship between C and Landsat ETM bands (1 and 7) plus the TNDVI is significantly high (coefficient of determination R2 = 0.8) and is used to produce the C map. The generated C map is used to predict A (R2 = 0.56), V (R2 = 0.58), S (R2 = 0.50) and B (R2 = 0.43). Cross validation for the predicted C map (cross-validation error = 5.3%) and for the predicted forest-parameter maps (cross-validation error = 13.7%-19.9%) shows acceptable error levels. Results indicate that Jordan's east Mediterranean forest parameters can be mapped and monitored for biomass accumulation and carbon dioxide (CO2) flux using Landsat ETM images.
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Document Type: Research Article
Department of Natural Resources & Environment, Faculty of Agriculture, Jordan University of Science and Technology, P.O. Box 3030, Irbid, Jordan
Arthur Temple College of Forestry and Agriculture, Stephen F. Austin State University, P.O. Box 6109, SFA Station, Nacogdoches, TX, USA
Publication date: March 1, 2011
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