LIDAR-Based Forest Biomass Remote Sensing: A Review of Metrics, Methods, and Assessment Criteria for the Selection of Allometric Equations
Author:
Borsah Abraham Aidoo1,
Nazeer Majid1ORCID,
Wong Man Sing12ORCID
Affiliation:
1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
2. Research Institute for Land and Space, The Hong Kong Polytechnic University, Hong Kong, China
Abstract
The increasing level of atmospheric carbon dioxide and its effects on our climate system has become a global environment issue. The forest ecosystem is essential for the stability of carbon in the atmosphere as it operates as a carbon sink and provides a habitat for numerous species. Therefore, our understanding of the structural elements of the forest ecosystem is vital for the estimation of forest biomass or terrestrial carbon stocks. Over the last two decades, light detection and ranging (LIDAR) technology has significantly revolutionized our understanding of forest structures and enhanced our ability to monitor forest biomass. This paper presents a review of metrics for forest biomass estimation, outlines metrics selection methods for biomass modeling, and addresses various assessment criteria for the selection of allometric equations for the aboveground forest biomass estimations, using LIDAR data. After examining one hundred publications written by different authors between 1999 and 2023, it was observed that LIDAR technology has become a dominant data collection tool for aboveground biomass estimation with most studies focusing on the use of airborne LIDAR data for the plot-level analysis on a local scale. Parametric-based models dominated in most studies with coefficient of determination (R2) and root mean square error (RMSE) as assessment criteria. In addition, mean top canopy height (MCH) and quadratic mean height (QMH) were reported as strong predictors for aboveground biomass (AGB) estimation. Pixel-based uncertainty analysis was found to be a reliable method for assessing spatial variations in uncertainties.
Funder
Collaborative Research Fund
Research Institute for Land and Space
Hong Kong Polytechnic University’s Start-up Fund
Reference100 articles.
1. FAO (2023, August 21). Global Forest Resources Assessment 2010. Available online: https://www.fao.org/publications/card/en/c/e4fa9d60-5207-5a96-976c-cd2e6f3519a5.
2. The Structure, Distribution, and Biomass of the World’s Forests;Pan;Annu. Rev. Ecol. Evol. Syst.,2013
3. WMO (2023, August 19). Essential Climate Variables. Available online: https://public.wmo.int/en/programmes/global-climate-observing-system/essential-climate-variables.
4. The Relevance of Forest Structure for Biomass and Productivity in Temperate Forests: New Perspectives for Remote Sensing;Fischer;Surv. Geophys.,2019
5. Kreuzberg, E. (2023, April 01). EO College MOOC Course Material_Biomass Estimation. Available online: https://eo-college.org/courses/forest-monitoring/lessons/biomass-estimation-2/.