Estimation of Dendrocalamus giganteus leaf area index by combining multi-source remote sensing data and machine learning optimization model
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. How...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1505414/full |
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| author | Zhen Qin Huanfen Yang Qingtai Shu Jinge Yu Zhengdao Yang Xu Ma Dandan Duan |
| author_facet | Zhen Qin Huanfen Yang Qingtai Shu Jinge Yu Zhengdao Yang Xu Ma Dandan Duan |
| author_sort | Zhen Qin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ffa2c898dde34299a43a20b81979cbc1 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-ffa2c898dde34299a43a20b81979cbc12025-01-15T12:39:22ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.15054141505414Estimation of Dendrocalamus giganteus leaf area index by combining multi-source remote sensing data and machine learning optimization modelZhen Qin0Huanfen Yang1Qingtai Shu2Jinge Yu3Zhengdao Yang4Xu Ma5Dandan Duan6College of Forestry, Southwest Forestry University, Kunming, Yunnan, ChinaCollege of Forestry, Southwest Forestry University, Kunming, Yunnan, ChinaCollege of Forestry, Southwest Forestry University, Kunming, Yunnan, ChinaSchool of Ecology and Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing, ChinaCollege of Forestry, Southwest Forestry University, Kunming, Yunnan, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaThe 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.https://www.frontiersin.org/articles/10.3389/fpls.2024.1505414/fullICESat-2/ATLASsentinel dataremote sensing datasequential gaussian conditional simulationoptimization algorithmLAI |
| spellingShingle | Zhen Qin Huanfen Yang Qingtai Shu Jinge Yu Zhengdao Yang Xu Ma Dandan Duan Estimation of Dendrocalamus giganteus leaf area index by combining multi-source remote sensing data and machine learning optimization model Frontiers in Plant Science ICESat-2/ATLAS sentinel data remote sensing data sequential gaussian conditional simulation optimization algorithm LAI |
| title | Estimation of Dendrocalamus giganteus leaf area index by combining multi-source remote sensing data and machine learning optimization model |
| title_full | Estimation of Dendrocalamus giganteus leaf area index by combining multi-source remote sensing data and machine learning optimization model |
| title_fullStr | Estimation of Dendrocalamus giganteus leaf area index by combining multi-source remote sensing data and machine learning optimization model |
| title_full_unstemmed | Estimation of Dendrocalamus giganteus leaf area index by combining multi-source remote sensing data and machine learning optimization model |
| title_short | Estimation of Dendrocalamus giganteus leaf area index by combining multi-source remote sensing data and machine learning optimization model |
| title_sort | estimation of dendrocalamus giganteus leaf area index by combining multi source remote sensing data and machine learning optimization model |
| topic | ICESat-2/ATLAS sentinel data remote sensing data sequential gaussian conditional simulation optimization algorithm LAI |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1505414/full |
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