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Abstract
Forest aboveground biomass (AGB) estimation is crucial for understanding carbon dynamics and supporting Reducing Emissions from Deforestation and Forest Degradation (REDD +) initiatives. It has gained significant research interest, evident in the skyrocketing number of peer-reviewed journal articles over the past decade alone. The availability of free and open-access airborne light detection and ranging (LiDAR) data has further accelerated the development of advanced AGB modeling approaches. However, a comprehensive summary of milestones achieved in AGB estimation using airborne LiDAR is still lacking. Our study aims to fill this gap by summarizing AGB model errors with respect to different data sources, forest biomes, and methods used. The overall objective of the study was to conduct a systematic review and meta-analysis of peer-reviewed journal articles on AGB estimation using airborne LiDAR published between 2013 and 2023. We followed the Preferred Reporting Items for Systematic Reviews and Meta- Analysis (PRISMA) framework to select 52 articles. Results indicate that most studies on AGB using airborne LiDAR were carried out in tropical biomes and employed multiple linear regression analysis as the modeling method. Results also show Root Mean Square Error as the most preferred model evaluation metric. Additionally, we concluded that meta-analysis of studies with a controlled predictor variable and modeling method produced less heterogeneous results ( I2 = 91.67% and Q = 399.97) as compared to the overall meta-analysis ( I2 = 96.38% and Q = 6648.28). The findings provide new insights to researchers for advancing AGB estimation accuracy using airborne LiDAR.
Citation
Shephard, Noah; Narine, Lana; Peng, Yucheng; Maggard, Adam. 2022. Climate-Smart Forestry in the Southern United States. Forests, 13(9), 1460.
