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Estimation of Dendrocalamus giganteus leaf area index by combining multi-source remote sensing data and machine learning optimization model

文献类型： 外文期刊

作者： Qin, Zhen 1 ; Yang, Huanfen 1 ; Shu, Qingtai 1 ; Yu, Jinge 2 ; Yang, Zhengdao 1 ; Ma, Xu 3 ; Duan, Dandan 4 ;

作者机构： 1.Southwest Forestry Univ, Coll Forestry, Kunming, Yunnan, Peoples R China

2.Nanjing Univ Informat Sci & Technol, Sch Ecol & Appl Meteorol, Nanjing, Peoples R China

3.Xinjiang Univ, Coll Geog & Remote Sensing Sci, Urumqi, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing, Peoples R China

关键词： ICESat-2/ATLAS; sentinel data; remote sensing data; sequential gaussian conditional simulation; optimization algorithm; LAI; machine learning models

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2025 年 15 卷

页码：

收录情况： SCI

摘要： The Leaf Area Index (LAI) is an essential parameter that affects the exchange of energy and materials between the vegetative canopy and the surrounding environment. Estimating LAI using machine learning models with remote sensing data has become a prevalent method for large-scale LAI estimation. However, existing machine learning models have exhibited various flaws, hindering the accurate estimation of LAI. Thus, a new method for large-scale estimation of Dendrocalamus giganteus LAI was proposed, which integrates ICESat-2/ATLAS, and Sentinel-1/-2 data, and refines machine learning models through the application of Bayesian Optimization (BO), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Simulated Annealing (SA). First, spatial interpolation was performed using the Sequential Gaussian Conditional Simulation (SGCS) method. Then, multi-source remote sensing data were leveraged to optimize feature variables through the Pearson correlation coefficient approach. Subsequently, optimization algorithms were applied to Random Forest Regression (RFR), Gradient Boosting Regression Tree (GBRT), and Support Vector Machine Regression (SVR) models, leading to efficient large-scale LAI estimation. The results showed that the BO-GBRT model achieved high accuracy in LAI estimation, with a coefficient of determination (R 2) of 0.922, a root mean square error (RMSE) of 0.263, a mean absolute error (MAE) of 0.187, and an overall estimation accuracy (P 1) of 92.38%. Compared to existing machine learning methods, the proposed approach demonstrated superior performance. This method holds significant potential for large-scale forest LAI inversion and can facilitate further research on other forest structure parameters.

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