Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 13

Published: April 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.

Language: Английский