Quantitative estimation of wetland aboveground biomass (AGB) is an essential aspect in evaluating the health and conservation of this valuable ecosystem. We combine AGB field measurements and remote sensing data to establish a suitable model for estimating wetland AGB in the Poyang Lake National Nature Reserve (PLNNR), which is included in the Ramsar Convention’s List of Wetlands of International Importance. All field sampling points cover four dominant vegetation communities (Carex cinerascen, Phalaris arundinacea, Artemisia selengensis, and Miscanthus sacchariflorus) in the PLNNR. Wetland AGB is retrieved from the Landsat-8 OLI image. To improve the accuracy of wetland AGB estimation, we compare the performances of three machine learning algorithms, namely, random forest (RF), back-propagation neural network (BPNN), and support vector regression (SVR), with linear regression (LR) in estimating the AGB in the PLNNR. Results are as follows: (1) the RF model with a root-mean-square error of 0.25 kg m − 2 performs better than BPNN (0.29 kg m − 2), SVR (0.27 kg m − 2), and LR (0.31 kg m − 2) in our testing dataset, and AGB density in the PLNNR is between 0 and 1.973 kg m − 2. (2) The four most important features for AGB modeling are near-infrared, short-wave infrared 1 band, enhanced vegetation index, and red band. Our study presents an effective and operational RF model that estimates wetland AGB from Landsat data, providing a scientific basis for floodplain wetland carbon accounting and possible future studies, such as the linkage between wetland AGB and the great water level fluctuations.
With the development of population and economy, the problems of deficit in water resources and degradation in water environment are increasingly serious in the Taihu watershed of China. The information on spatial and temporal availabilities of water will be helpful for the optimum utilization of water resources. In this study, we apply precipitation (P) from the Tropical Rainfall Measuring Mission (TRMM) products, evapotranspiration (ET) derived from MODIS data, and ground-observed runoff. Then annual water budgets in the Taihu watershed from 2005 to 2007 and the variation of water budget components in spatial and temporal (monthly and annually) scales were evaluated. The results indicated that ET was the most notable component of water consumption in the watershed. The annual mean ratio of the ET to the precipitation was 0.73 to 0.89 in the watershed and 1.1 to 1.3 in Lake Taihu area from 2005 to 2007. The analysis of water balance in the watershed indicated that the amount of water input and output were approximately equal for the watershed and the lake areas with imbalance percentages of 1.4% to 4.4% and 0.1% to 4.0%, respectively.
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