文献类型：期刊文献

英文题名：A Leaf Chlorophyll Content Estimation Method for Populus deltoides (Populus deltoides Marshall) Using Ensembled Feature Selection Framework and Unmanned Aerial Vehicle Hyperspectral Data

作者：Chen, Zhulin[1,2] Wang, Xuefeng[1,2] Qiao, Shijiao[3] Liu, Hao[1,4] Shi, Mengmeng[1,2] Chen, Xingjing[1,2] Jiang, Haiying[5] Zou, Huimin[6]

第一作者：Chen, Zhulin

通信作者：Wang, XF[1];Wang, XF[2]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]State Forestry & Grassland Adm, Key Lab Forest Management & Growth Modelling, Beijing 100091, Peoples R China;[3]Beijing Normal Univ, Fac Geog Sci, Innovat Res Ctr Satellite Applicat, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China;[4]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[5]Harbin Engn Univ, Coll Comp Sci & Technol, Harbin 150006, Peoples R China;[6]Natl Ocean Technol Ctr, Tianjin 300112, Peoples R China

年份：2024

卷号：15

期号：11

外文期刊名：FORESTS

收录：;EI(收录号：20244817444240);Scopus(收录号：2-s2.0-85210270782);WOS:【SCI-EXPANDED(收录号:WOS:001366975400001)】；

基金：This research was funded by Scientific and Technological Innovation 2030-Major Projects, grant number 2023ZD0405605.

语种：英文

外文关键词：leaf chlorophyll content estimation; hyperspectral data; unmanned aerial vehicle; feature selection; Populus deltoides Marshall

摘要：Leaf chlorophyll content (LCC) is a key indicator in representing the photosynthetic capacity of Populus deltoides (Populus deltoides Marshall). Unmanned aerial vehicle (UAV) hyperspectral imagery provides an effective approach for LCC estimation, but the issue of band redundancy significantly impacts model accuracy and computational efficiency. Commonly used single feature selection algorithms not only fail to balance computational efficiency with optimal set search but also struggle to combine different regression algorithms under dynamic set conditions. This study proposes an ensemble feature selection framework to enhance LCC estimation accuracy using UAV hyperspectral data. Firstly, the embedded algorithm was improved by introducing the SHapley Additive exPlanations (SHAP) algorithm into the ranking system. A dynamic ranking strategy was then employed to remove bands in steps of 10, with LCC models developed at each step to identify the initial band subset based on estimation accuracy. Finally, the wrapper algorithm was applied using the initial band subset to search for the optimal band subset and develop the corresponding model. Three regression algorithms including gradient boosting regression trees (GBRT), support vector regression (SVR), and gaussian process regression (GPR) were combined with this framework for LCC estimation. The results indicated that the GBRT-Optimal model developed using 28 bands achieved the best performance with R2 of 0.848, RMSE of 1.454 mu g/cm2 and MAE of 1.121 mu g/cm2. Compared with a model performance that used all bands as inputs, this optimal model reduced the RMSE value by 24.37%. In addition to estimating biophysical and biochemical parameters, this method is also applicable to other hyperspectral imaging tasks.