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Estimating montane forest above-ground biomass in the upper reaches of the Heihe River Basin using Landsat-TM data

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In this work, the results of above-ground biomass (AGB) estimates from Landsat Thematic Mapper 5 (TM) images and field data from the fragmented landscape of the upper reaches of the Heihe River Basin (HRB), located in the Qilian Mountains of Gansu province in northwest China, are presented. Estimates of AGB are relevant for sustainable forest management, monitoring global change, and carbon accounting. This is particularly true for the Qilian Mountains, which are a water resource protection zone. We combined forest inventory data from 133 plots with TM images and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) V2 products (GDEM) in order to analyse the influence of the sun-canopy-sensor plus C (SCS+C) topographic correction on estimations of forest AGB using the stepwise multiple linear regression (SMLR) and k-nearest neighbour (k-NN) methods. For both methods, our results indicated that the SCS+C correction was necessary for getting more reliable forest AGB estimates within this complex terrain. Remotely sensed AGB estimates were validated against forest inventory data using the leave-one-out (LOO) method. An optimized k-NN method was designed by varying both mathematical formulation of the algorithm and remote-sensing data input, which resulted in 3000 different model configurations. Following topographic correction, performance of the optimized k-NN method was compared to that of the regression method. The optimized k-NN method (R 2 = 0.59, root mean square error (RMSE) = 24.92 tonnes ha–1) was found to perform much better than the regression method (R 2 = 0.42, RMSE = 29.74 tonnes ha–1) for forest AGB retrieval over this montane area. Our results indicated that the optimized k-NN method is capable of operational application to forest AGB estimates in regions where few inventory data are available.
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Document Type: Research Article

Affiliations: 1: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, PR China 2: Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, 7500 AA, The Netherlands 3: Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu, 730000, PR China 4: Plant Functional Biology and Climate Change Cluster, University of Technology, Sydney, NSW, 2007, Australia 5: Key Laboratory of Spatial Data Mining & Information Sharing of Ministry Education, Fuzhou University, Fuzhou, Fujian, 350002, PR China

Publication date: November 2, 2014

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