文献类型：期刊文献

英文题名：Hyperspectral Analysis and Regression Modeling of SPAD Measurements in Leaves of Three Mangrove Species

作者：Li, Huazhe[1,2,3] Cui, Lijuan[1,2,3] Dou, Zhiguo[1,2,3] Wang, Junjie[4,5,6,7] Zhai, Xiajie[1,2,3] Li, Jing[1,2,3] Zhao, Xinsheng[1,2,3] Lei, Yinru[1,2,3] Wang, Jinzhi[1,2,3] Li, Wei[1,2,3]

第一作者：Li, Huazhe;李瀚之

通信作者：Li, W[1];Li, W[2];Li, W[3]

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]Shenzhen Univ, Coll Life Sci & Oceanog, Shenzhen 518060, Peoples R China;[5]Shenzhen Univ, MNR Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518060, Peoples R China;[6]Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China;[7]Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China

年份：2023

卷号：14

期号：8

外文期刊名：FORESTS

收录：;EI(收录号：20233514644013);Scopus(收录号：2-s2.0-85168809692);WOS:【SCI-EXPANDED(收录号:WOS:001055944500001)】；

基金：This research was funded by The Special Fund of Chinese Central Government for Basic Scientific Research Operations in Commonweal Research Institutes (CAFYBB2021MC006).

语种：英文

外文关键词：hyperspectral data; successive projections algorithm; mangroves; SPAD measurements

摘要：Mangroves have important roles in regulating climate change, and in reducing the impact of wind and waves. Analysis of the chlorophyll content of mangroves is important for monitoring their health, and their conservation and management. Thus, this study aimed to apply four regression models, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Partial Least Squares (PLS) and Adaptive Boosting (AdaBoost), to study the inversion of Soil Plant Analysis Development (SPAD) values obtained from near-ground hyperspectral data of three dominant species, Bruguiera sexangula (Lour.) Poir. (B. sexangula), Ceriops tagal (Perr.) C. B. Rob. (C. tagal) and Rhizophora apiculata Blume (R. apiculata) in Qinglan Port Mangrove Nature Reserve. The accuracy of the model was evaluated using R-2, RMSE, and MAE. The mean SPAD values of R. apiculata (SPAD(avg) = 66.57), with a smaller dispersion (coefficient of variation of 6.59%), were higher than those of C. tagal (SPAD(avg) = 61.56) and B. sexangula (SPAD(avg) = 58.60). The first-order differential transformation of the spectral data improved the accuracy of the prediction model; R-2 was mostly distributed in the interval of 0.4 to 0.8. The accuracy of the XGBoost model was less affected by species differences with the best stability, with RMSE at approximately 3.5 and MAE at approximately 2.85. This study provides a technical reference for large-scale detection and management of mangroves.