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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.
Highlights
BackgroundForest aboveground biomass (AGB) refers to the dry weight of tree components that are located above the ground surface (Popescu 2007)
We analyzed the articles based on three different variables, namely data source, forest biomes, and method used for AGB modeling (Section. 3.1.3)
We conducted a quantitative analysis to determine the number of studies conducted by country, evaluation metrics for AGB models, and a summary of variables used in AGB modeling
Summary
BackgroundForest aboveground biomass (AGB) refers to the dry weight of tree components that are located above the ground surface (Popescu 2007). Accurate AGB monitoring is essential for implementing climate-smart forestry practices, such as reduced deforestation, reforestation prospects, and enhanced management for carbon sequestration to increase forest carbon storage (Johnson et al 2022; Shen et al 2016; Shephard et al 2022). Forest aboveground biomass is a prominent measure to track progress towards Sustainable Development Goals of the United Nations and the Paris Agreement on Climate Change (Herold et al 2019). The reporting of forest carbon stocks is a requirement for countries ratifying the Kyoto protocol to the United Nations Framework Convention on Climate Change (UNFCCC) (Secretariat 2016). Based on the protocol, industrialized countries have limits on greenhouse gas emissions. These countries promote sustainable forestry development to help mitigate climate change in the long term by preventing the introduction of carbon into the atmosphere and enhancing carbon sequestration. Accurate AGB estimation has gained significant interest due to the growing need for carbon monitoring
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