Hyperspectral remote sensing of aboveground biomass on a river meander bend using multivariate adaptive regression splines and stochastic gradient boosting
Hyperspectral remote sensing of aboveground biomass on a river meander bend using multivariate adaptive regression splines and stochastic gradient boosting
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DOI：
10.1080/2150704x.2014.915070
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发表时间：
2014-05-04
影响因子：
2.3
通讯作者：
Randall, Jarom
中科院分区：
文献类型：
--
作者：
Filippi, Anthony M.;Gueneralp, Inci;Randall, Jarom
特别是，迫切需要通过遥感对洪泛平原森林地上生物量（AGB）进行研究，以提高对这些地区碳循环的认识。AGB估计是特别具有挑战性的洪泛区森林，其特点是高空间变异性AGB造成的生态地貌动力学过程。在这项研究中，我们进行远程AGB检索的落叶河岸林河曲弯曲的基础上的高光谱/高维光谱波段和其他输入变量。我们比较了多元自适应回归样条（MARS），随机梯度提升（SGB）和基于立方的AGB估计。结果表明，MARS和SGB衍生的估计是显着更准确的比基于立方的AGB。最准确的MARS和SGB估计的决定系数R-2分别为0.97和0.95，而具有最低误差的Cubist估计的R-2为0.85。然而，MARS和SGB AGB没有显著差异。这些建模方法适用于各种规模和环境。
Research on aboveground biomass (AGB) retrieval via remote sensing in floodplain forests, in particular, is urgently needed for improved understanding of carbon cycling in such areas. AGB estimation is particularly challenging in floodplain forests, which are characterized by high spatial variability in AGB resulting from biogeomorphodynamic processes. In this study, we perform remote AGB retrieval for a deciduous riparian forest on a river meander bend based on hyperspectral/high-dimensional Hyperion bands and other input variables. We compare multivariate adaptive regression splines (MARS)-, stochastic gradient boosting (SGB)- and Cubist-based AGB estimates. Results show that MARS- and SGB-derived estimates are significantly more accurate than Cubist-based AGB. The most accurate MARS and SGB estimates have a coefficient of determination, R-2, of 0.97 and 0.95, respectively, whereas the Cubist estimate with the lowest error has an R-2 of 0.85. MARS and SGB AGB are not significantly different, however. These modelling approaches are applicable across scales and environments.