Remote sensing estimation of the aboveground biomass of reed wetland in the Western Songnen Plain, China, based on MODIS data

Yanlin, 田艳林 Tian; Xianzhao, 刘贤赵 Liu; Dehua, 毛德华 Mao; Zongming, 王宗明 Wang; Yanfeng, 李延峰 Li; Changchun, 高长春 Gao

doi:10.5846/stxb201505110959

Cited by 3 publications

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“…26 For statistical models, a single or multiple VIs are traditionally adopted as the predictors for establishing a linear, exponential, logarithmic, or power model. 27,28 The development of machine learning and artificial intelligence has allowed for improved predictive accuracy. Such techniques can produce complex nonlinear mappings due to advanced learning strategies by utilizing the information contained in a set of reference samples.…”

Section: Introductionmentioning

confidence: 99%

Modeling wetland aboveground biomass in the Poyang Lake National Nature Reserve using machine learning algorithms and Landsat-8 imagery

Wang

2018

J. Appl. Rem. Sens.

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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, shortwave 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. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Introductionmentioning

confidence: 99%