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

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

Mapping wildfire susceptibility in the tropical region of Brunei: a machine learning and explainable AI approach using google earth engine with remote sensing data

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Май 14, 2025

Язык: Английский