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A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia,China
Authors:Yichun Xie  Zongyao Sha  Mei Yu  Yongfei Bai  Lei Zhang
Institution:1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, PR China;2. Department of Geography and Geology, Eastern Michigan University, Ypsilanti, MI 48197, United States;3. International Software School, Wuhan University, Wuhan 430079, PR China;4. Institute for Tropical Ecosystem Studies, University of Puerto Rico-Rio Piedras, San Juan, PR 00931-1910, United States;5. Institute of Microbiology, Chinese Academy of Sciences, Beijing 100080, PR China
Abstract:Two models, artificial neural network (ANN) and multiple linear regression (MLR), were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China. The normalized difference vegetation index (NDVI) and topographic variables (elevation, aspect, and slope) were combined with atmospherically corrected reflectance from the Landsat ETM+ reflective bands as the candidate input variables for building both models. Seven variables (NDVI, aspect, and bands 1, 3, 4, 5 and 7) were selected by the ANN model (implemented in Statistica 6.0 neural network module), while six (elevation, NDVI, and bands 1, 3, 5 and 7) were picked to fit the MLR function after a stepwise analysis was executed between the candidate input variables and the above ground dry biomass. Both models achieved reasonable results with RMSEs ranging from 39.88% to 50.08%. The ANN model provided a more accurate estimation (RMSEr = 39.88% for the training set, and RMSEr = 42.36% for the testing set) than MLR (RMSEr = 49.51% for the training, and RMSEr = 53.20% for the testing). The final above ground dry biomass maps of the research area were produced based on the ANN and MLR models, generating the estimated mean values of 121 and 147 g/m2, respectively.
Keywords:Grassland aboveground dry biomass  Inner Mongolia  Landsat ETM+  Artificial neural network (ANN)  Multiple linear regression (MLR)
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