Abstract
Abstract. Forest is one of the most crucial Earth’s resources. Forest above-ground biomass (AGB) mapping has been research endeavors for a long time in many applications since it provides valuable information for carbon cycle monitoring, deforestation, and forest degradation monitoring. A methodology to rapidly and accurately estimate AGB is essential for forest monitoring purposes. Thus, the main objective of this paper was to investigate the performance of decision tree-based models to predict AGB at a site in Huntington Wild Forest (HWF) in Essex County, NY using continuous forest inventory (CFI) plots. The results of decision tree, random forest, and deep forest regression models were compared using light detection and ranging (LiDAR), Landsat 5 TM, and a combination of them. The results illustrated the importance of integration of Landsat 5 TM and LiDAR data, which benefits from both vertical forest structure and spectral information reflected by canopy cover. In addition, the deep forest model with a root mean square error (RMSE) of 51.63 Mg/ha and R-squared (R2) of 0.45 outperformed other regression tree-based models, regardless of the dataset.
Highlights
Forest is considered as one of the most valuable Earth resources, which is required to be monitored in a timely manner (Bastin et al 2017)
Forest aboveground biomass (AGB) plays a crucial role in carbon sequestration, which contributes to global climate change issues
The main objective of this paper is to address the capability of the combination of light detection and ranging (LiDAR) and optical data for accurate AGB estimation
Summary
Forest is considered as one of the most valuable Earth resources, which is required to be monitored in a timely manner (Bastin et al 2017). Sustainable forest management is of paramount significance for many applications, namely forest productivity, monitoring carbon sequestration, and investigating deforestation. Forest aboveground biomass (AGB) plays a crucial role in carbon sequestration, which contributes to global climate change issues Accurate AGB estimation has been an area of interest for many researchers. Conventional field measurement techniques provide an accurate estimation of AGB while they are labor-intensive, costly, time-consuming, and not applicable for large regions Li, Im, and Beier 2013). Remote sensing data paved the road for a cost-effective AGB estimation over large areas
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