文献类型：期刊文献

英文题名：Estimating Aboveground Biomass of Wetland Plant Communities from Hyperspectral Data Based on Fractional-Order Derivatives and Machine Learning

作者：Li, Huazhe[1,2,3,4] Tang, Xiying[1,2,3,4] Cui, Lijuan[1,2,3,4] Zhai, Xiajie[1,2,3,4] Wang, Junjie[5,6] Zhao, Xinsheng[1,2,3,4] Li, Jing[1,2,3,4] Lei, Yinru[1,2,3,4] Wang, Jinzhi[1,2,3,4] Wang, Rumiao[1,2,3,4] Li, Wei[1,2,3,4]

第一作者：Li, Huazhe;李瀚之

通信作者：Li, W[1];Li, W[2];Li, W[3];Li, W[4]

机构：[1]Chinese Acad Forestry, Inst Wetland Res, Beijing 100091, Peoples R China;[2]Beijing Key Lab Wetland Serv & Restorat, Beijing 100091, Peoples R China;[3]Chinese Acad Forestry, Inst Ecol Conservat & Restorat, Beijing 100091, Peoples R China;[4]Beijing Hanshiqiao Natl Wetland Ecosyst Res Stn, Beijing 101399, Peoples R China;[5]Shenzhen Univ, Coll Life Sci & Oceanog, Shenzhen 518060, Peoples R China;[6]Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, MNR Key Lab Geoenvironm Monitoring Great Bay Area, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China

年份：2024

卷号：16

期号：16

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20243516966083);Scopus(收录号：2-s2.0-85202445398);WOS:【SCI-EXPANDED(收录号:WOS:001305220400001)】；

基金：This research was funded by China's Special Fund for Basic Scientific Research Business of Central Public Research Institutes (CAFYBB2021MC006 and CAFYBB2021ZB003).

语种：英文

外文关键词：aboveground biomass; hyperspectral data; fractional-order derivative; machine learning; Shapley Additive Explanations

摘要：Wetlands, as a crucial component of terrestrial ecosystems, play a significant role in global ecological services. Aboveground biomass (AGB) is a key indicator of the productivity and carbon sequestration potential of wetland ecosystems. The current research methods for remote-sensing estimation of biomass either rely on traditional vegetation indices or merely perform integer-order differential transformations on the spectra, failing to fully leverage the information complexity of hyperspectral data. To identify an effective method for estimating AGB of mixed-wetland-plant communities, we conducted field surveys of AGB from three typical wetlands within the Crested Ibis National Nature Reserve in Hanzhong, Shaanxi, and concurrently acquired canopy hyperspectral data with a portable spectrometer. The spectral features were transformed by applying fractional-order differentiation (0.0 to 2.0) to extract optimal feature combinations. AGB prediction models were built using three machine learning models, XGBoost, Random Forest (RF), and CatBoost, and the accuracy of each model was evaluated. The combination of fractional-order differentiation, vegetation indices, and feature importance effectively yielded the optimal feature combinations, and integrating vegetation indices with feature bands enhanced the predictive accuracy of the models. Among the three machine-learning models, the RF model achieved superior accuracy using the 0.8-order differential transformation of vegetation indices and feature bands (R2 = 0.673, RMSE = 23.196, RPD = 1.736). The optimal RF model was visually interpreted using Shapley Additive Explanations, which revealed that the contribution of each feature varied across individual sample predictions. Our study provides methodological and technical support for remote-sensing monitoring of wetland AGB.