Effects of Forest Canopy Structure on Forest Aboveground Biomass Estimation Using Landsat Imagery

Abstract

Accurate remote sensing-based forest aboveground biomass (AGB) estimation is important for accurate understanding of carbon accounting and climate change at a large scale. However, over- and underestimation are common in the process, resulting in inaccurate AGB estimations. Here, the AGB was estimated and mapped by combining Landsat 8 images and forest inventory data in western Hunan Province, China. We used forest canopy density (FCD) mapper to quantify the forest canopy structure. The linear model (LR) and piecewise model with FCD gradients (classified by k-means clustering; sparse, medium, and dense) were developed to estimate AGB for each forest type (coniferous, broadleaf, mixed, and total forests). The piecewise model considered the following scenarios: piecewise model using the variables of LR model (PM), and piecewise model using the variables selected for different FCD gradients (PMV). The PM (R2:0.45–0.56) and PMV (R2:0.63–0.75) models showed better agreement between observed and predicted AGB than the LR (R2:0.18–0.27) models, and the PMV model was the most accurate for each forest type. The PM and PMV models performed better than LR models at different FCD gradients. The PM and PMV models can better alleviate the over- and underestimations of the LR models. At different FCD gradients, the PMV models had different variables, indicating that the correlation between the AGB and spectral variables was different. Overall, FCD is an important forest parameter that influences AGB estimation, and the piecewise model has potential to improve remote sensing-based AGB estimation.