Evaluating forest aboveground biomass estimation model using simulated ALS point cloud from an individual-based forest model and 3D radiative transfer model across continents
Abstract
Area-based approach (ABA) has been widely employed for estimating forest aboveground biomass (AGB) using airborne laser scanning (ALS) data. However, its scalability is limited due to challenges in model generalization across different forest types and regions. The selection of sensitive variables from ALS data is crucial for constructing robust forest AGB estimation models, yet this selection varies significantly among forest types and regions. Traditionally, assessing the influence of variable selection is hindered by the lack of accurate reference forest AGB values. Computer simulation-based method provides a perspective for exploring these challenges. This study employs an individual-based forest growth process model, FORMIND, coupled with a 3D radiative transfer model (RTM), LESS, to evaluate the transferability of ABA-based forest AGB estimation models and the generalization of ALS-derived variables. We used six virtual 3D forest scenes and two real-world forest sites, representing a range of global forest types, along with their simulated ALS data, to develop a forest AGB estimation model using the random forest algorithm, which allowed us to analyze the importance of various variables. We assessed model transferability through cross-comparison. Additionally, we validated the model using field plots and ALS data collected from two distinct regions. The results showed that the canopy surface area and volume extracted using the α-shape algorithm and parameters fitted…
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Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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