Remote sensing and integration of machine learning algorithms for above-ground biomass estimation in Larix principis-rupprechtii Mayr plantations: a case study using Sentinel-2 and Landsat-9 data in northern China DOI Creative Commons

Jamshid Ali,

Haoran Wang, Kaleem Mehmood

и другие.

Frontiers in Environmental Science, Год журнала: 2025, Номер 13

Опубликована: Апрель 2, 2025

Estimating above-ground biomass (AGB) is important for ecological assessment, carbon stock evaluation, and forest management. This research assesses the performance of machine learning algorithms XGBoost, SVM, RF using data from Sentinel-2 Landsat-9 satellites. The study influence significant spectral bands vegetation indices on accuracy AGB estimate. results presented in paper indicate that were more effective than data. mainly because it had higher spatial resolution, which enabled model gradients structural attributes accurately. XGBoost performed best with an R 2 0.82 RMSE 0.73 Mg/ha 0.80 0.71 Landsat-9. In current study, SVM also showed a substantial 0.79 0.76 For Sentinel-2, random achieved 0.74 0.93 Mg/ha, Landsat 9 yielded 0.72 0.88 Mg/ha. Thus, variable importance analysis, have predicting AGB. As expected their application research, these predictors consistently emerged as highly across models datasets. demonstrates potential integrating remote sensing to achieve accurate efficient assessment.

Язык: Английский

Interaction Mechanism of Biochar Dissolved Organic Matter (BDOM) and Tetracycline for Environmental Remediation DOI
Yun Zhu, Jinlong Yan,

Fengfeng Sui

и другие.

Environmental Research, Год журнала: 2025, Номер unknown, С. 121405 - 121405

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Performance of multiscale landscape metrics as indicators of soil C, N, P, and physicochemical properties of NFV in ML reaches of Yellow River Basin, China DOI Creative Commons
Chenhui Wei,

Kaili Chen,

Yang Zhan

и другие.

Ecological Indicators, Год журнала: 2025, Номер 173, С. 113391 - 113391

Опубликована: Март 30, 2025

Язык: Английский

Процитировано

0
Remote sensing and integration of machine learning algorithms for above-ground biomass estimation in Larix principis-rupprechtii Mayr plantations: a case study using Sentinel-2 and Landsat-9 data in northern China DOI Creative Commons

Jamshid Ali,

Haoran Wang, Kaleem Mehmood

и другие.

Frontiers in Environmental Science, Год журнала: 2025, Номер 13

Опубликована: Апрель 2, 2025

Estimating above-ground biomass (AGB) is important for ecological assessment, carbon stock evaluation, and forest management. This research assesses the performance of machine learning algorithms XGBoost, SVM, RF using data from Sentinel-2 Landsat-9 satellites. The study influence significant spectral bands vegetation indices on accuracy AGB estimate. results presented in paper indicate that were more effective than data. mainly because it had higher spatial resolution, which enabled model gradients structural attributes accurately. XGBoost performed best with an R 2 0.82 RMSE 0.73 Mg/ha 0.80 0.71 Landsat-9. In current study, SVM also showed a substantial 0.79 0.76 For Sentinel-2, random achieved 0.74 0.93 Mg/ha, Landsat 9 yielded 0.72 0.88 Mg/ha. Thus, variable importance analysis, have predicting AGB. As expected their application research, these predictors consistently emerged as highly across models datasets. demonstrates potential integrating remote sensing to achieve accurate efficient assessment.

Язык: Английский

Процитировано

0