Aboveground biomass estimation using multimodal remote sensing observations and machine learning in mixed temperate forest.

ABSTRACT: Plants sequester carbon in their aboveground components, making aboveground tree biomass a key metric for assessing forest carbon storage. Traditional methods of aboveground biomass (AGB) estimation via Forest Inventory and Analysis (FIA) plots lack sufficient sampling intensity to directly produce accurate estimates at fine granularities. Increasing the sampling intensity with additional FIA plots would be labor and time intensive, particularly for large-scale carbon studies. Utilizing remote sensing (RS) data, such as Airborne Light Detection and Ranging (LiDAR), aerial imagery, and satellite images can significantly enhance the efficiency of forest carbon monitoring efforts. The principal objective of this study is to utilize the random forest (RF) algorithm to build predictive AGB mode

SUBMITTER: Lamahewage SHG 

PROVIDER: S-EPMC12375767 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature