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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1505414
This article is part of the Research Topic Accurate Measurement and Dynamic Monitoring of Forest Parameters View all articles

Estimation of Dendrocalamus giganteus Leaf Area Index by Combining Multi-Source Remote Sensing Data and Machine Learning Optimization Model

Provisionally accepted
Zhen Qin Zhen Qin 1Huanfen Yang Huanfen Yang 1*Qingtai Shu Qingtai Shu 1*Jinge Yu Jinge Yu 2Zhengdao Yang Zhengdao Yang 1*Xu Ma Xu Ma 3Dandan Duan Dandan Duan 4*
  • 1 College of Forestry, Southwest Forestry University, Kunming, China
  • 2 School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province, China
  • 3 College of Geography and Remote Sensing Science, Xinjiang University, Urumqi, Xinjiang Uyghur Region, China
  • 4 Information Technology Research Center, Beiing Academy of Agriculture and Forestry Sciences, Beijing, China

The final, formatted version of the article will be published soon.

    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 (R2) 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 (P1) 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.

    Keywords: ICESat-2/ATLAS, Sentinel data, Remote sensing data, Sequential Gaussian Conditional Simulation, Optimization algorithm, LAI, machine learning models

    Received: 02 Oct 2024; Accepted: 12 Dec 2024.

    Copyright: © 2024 Qin, Yang, Shu, Yu, Yang, Ma and Duan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Huanfen Yang, College of Forestry, Southwest Forestry University, Kunming, China
    Qingtai Shu, College of Forestry, Southwest Forestry University, Kunming, China
    Zhengdao Yang, College of Forestry, Southwest Forestry University, Kunming, China
    Dandan Duan, Information Technology Research Center, Beiing Academy of Agriculture and Forestry Sciences, Beijing, China

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