Forest Aboveground Biomass Estimation Using Airborne LiDAR: A Systematic Review and Meta-Analysis Article Swipe

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Nisham Thapa , Lana L. Narine , Alan E. Wilson ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1007/s44392-025-00029-w

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 (I 2 = 91.67% and Q = 399.97) as compared to the overall meta-analysis (I 2 = 96.38% and Q = 6648.28). The findings provide new insights to researchers for advancing AGB estimation accuracy using airborne LiDAR.

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Environmental Science

Concepts

Environmental science Biomass (ecology) Lidar Remote sensing Estimation Forestry Geography Ecology Biology Engineering Systems engineering

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Type: review
Language: en
Landing Page: https://doi.org/10.1007/s44392-025-00029-w
PDF: https://link.springer.com/content/pdf/10.1007/s44392-025-00029-w.pdf
OA Status: hybrid
Cited By: 2
References: 69
Related Works: 10
OpenAlex ID: https://openalex.org/W4410903197

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OpenAlex ID: https://openalex.org/W4410903197

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DOI: https://doi.org/10.1007/s44392-025-00029-w

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Title: Forest Aboveground Biomass Estimation Using Airborne LiDAR: A Systematic Review and Meta-Analysis

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Type: review

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Language: en

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Publication year: 2025

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Publication date: 2025-05-30

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Authors: Nisham Thapa, Lana L. Narine, Alan E. Wilson

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Landing page: https://doi.org/10.1007/s44392-025-00029-w

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PDF URL: https://link.springer.com/content/pdf/10.1007/s44392-025-00029-w.pdf

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OA URL: https://link.springer.com/content/pdf/10.1007/s44392-025-00029-w.pdf

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Concepts: Environmental science, Biomass (ecology), Lidar, Remote sensing, Estimation, Forestry, Geography, Ecology, Biology, Engineering, Systems engineering

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Cited by: 2

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References (count): 69

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abstract_inverted_index.modeling	65, 177, 206
abstract_inverted_index.multiple	171
abstract_inverted_index.produced	208
abstract_inverted_index.research	28
abstract_inverted_index.sources,	100
abstract_inverted_index.tropical	167
abstract_inverted_index.variable	204
abstract_inverted_index.6648.28).	233
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abstract_inverted_index.Preferred	138
abstract_inverted_index.Reporting	139
abstract_inverted_index.advancing	242
abstract_inverted_index.articles.	152
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abstract_inverted_index.detection	52
abstract_inverted_index.different	98
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abstract_inverted_index.preferred	189
abstract_inverted_index.published	130
abstract_inverted_index.Systematic	142
abstract_inverted_index.controlled	202
abstract_inverted_index.estimation	5, 76, 126, 244
abstract_inverted_index.evaluation	191
abstract_inverted_index.milestones	72
abstract_inverted_index.regression	173
abstract_inverted_index.supporting	13
abstract_inverted_index.systematic	116
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abstract_inverted_index.summarizing	91
abstract_inverted_index.availability	45
abstract_inverted_index.initiatives.	23
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abstract_inverted_index.comprehensive	69
abstract_inverted_index.heterogeneous	210
abstract_inverted_index.meta-analysis	119, 197, 225
abstract_inverted_index.peer-reviewed	36, 121
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citation_normalized_percentile.is_in_top_10_percent	True