文献类型：期刊文献

英文题名：Predictions of Spartina alterniflora leaf functional traits based on hyperspectral data and machine learning models

作者：Li, Wei[1,2] Zuo, Xueyan[1,2] Liu, Zhijun[1,2] Nie, Leichao[1,2] Li, Huazhe[1,2] Wang, Junjie[3] Dou, Zhiguo[1,2] Cai, Yang[1,2] Zhai, Xiajie[1,2] Cui, Lijuan[1,2]

第一作者：李卫

通信作者：Cui, LJ[1]

机构：[1]Chinese Acad Forestry, Inst Wetland Res, Beijing Key Lab Wetland Serv & Restorat, Beijing, Peoples R China;[2]Inst Ecol Conservat & Restorat, Chinese Acad Forestry, Beijing, Peoples R China;[3]Shenzhen Univ, Coll Life Sci & Oceanog, Shenzhen, Peoples R China

年份：2024

卷号：57

期号：1

外文期刊名：EUROPEAN JOURNAL OF REMOTE SENSING

收录：;EI(收录号：20240115304156);Scopus(收录号：2-s2.0-85180666444);WOS:【SCI-EXPANDED(收录号:WOS:001129656900001)】；

基金：This work was supported by the National Key R & D Program of China under Grant [2017YFC0506200]

语种：英文

外文关键词：Coastal wetlands; functional traits; hyperspectral data; machine learning; spartina alterniflora

摘要：Investigating the functional traits of Spartina alterniflora can provide insights towards understanding its invasion mechanism, and developing a method leaves can improve its management in coastal wetlands. Here, we examined the relationship between 11 leaf functional traits of S. alterniflora and hyperspectral data and investigated the feature bands through importance score analysis. Using original spectral and first-order differential conversion data of feature bands, we established four prediction models: random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and back propagation neural network (BPNN). The study results showed that: (1) the SVM model based on Random Forest Importance Score is well-suited for S. alterniflora leaf functional trait inversion; (2) the importance score of leaf functional traits differed, and first-order differential spectral data produced more bands with high scores compared with the original hyperspectral reflectance data; (3) first-order differential data modelling effects were slightly better than those of the original spectral data. However, the first-order differential treatment did not show a significant improvement in the validation accuracy compared with the original data, and the accuracy of some traits decreased. Our study provides a new methodological approach for improving the monitoring and management of S. alterniflora in coastal wetlands.