Above ground tree biomass modeling using machine learning algorithms in western Terai Sal Forest of Nepal.

ABSTRACT: The monitoring of forest biomass is a crucial biophysical parameter in forest ecosystems, as it provides valuable information for managing forests sustainably and tracking carbon circulation statistics. To achieve sustainable forest management, it is essential to monitor and study forest resources, particularly biomass. This study aimed to model above ground tree biomass (AGTB) using Machine Learning Algorithms (MLAs) in the western terai Sal forest of Nepal. AGTB was calculated using a systematic inventory sample plot, while spectral and textural variables were processed and masked for the study area using Sentinel-2A satellite imagery. Three MLAs namely support vector machine (SVM), random forest (RF), and stochastic gradient boosting (SGB), were employed for modeling with eight categori

SUBMITTER: Singh B 

PROVIDER: S-EPMC10665687 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature