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Forest Biomass Estimation with LiDAR Data

Dengsheng Lu,Xiandie Jiang,Yunhe Li,Ruoqi Wang,Guiying Li

Published 2025 in National Remote Sensing Bulletin

ABSTRACT

： Forests as the largest carbon sink in the terrestrial ecosystems play important roles in mitigating climate change and maintaining ecological balance, thus, it is required to accurately map forest biomass distribution at timely manner. Remote sensing-based biomass estimation has obtained great attention in the past three decades, in particular, LiDAR due to its capability of capturing three-dimensional structure of forest stands has become an important data source for forest biomass estimation. LiDAR data can be acquired from different platforms such as close-ground, airborne and spaceborne, thus, they are used for biomass estimation at different scales such as individual trees, forest stands and landscapes. Many studies using LiDAR data for forest biomass estimation have been conducted, but no comprehensive review has been made so far. Therefore, this paper attempts to provide an overview of current situations of using LiDAR technologies for forest biomass estimation and discuss the challenges and potential solutions to improve biomass modeling performance at different scales. The research situations and existing problems on the biomass estimation at individual tree, plot, and landscape scales based on LiDAR data from different platforms (e.g., close-ground, airborne and spaceborne) were first described and the combination of LiDAR and other data sources such as optical, microwave radar, and auxiliary data for improvement of forest biomass estimation were then summarized and discussed; Different modeling methods such as regression, machine learning, deep learning, and hybrid methods were overviewed and the potential solutions to improve modeling accuracy through stratification were discussed; The potential factors causing biomass estimation uncertainty, the methods for examining and identifying uncertainty factors were described and then potential strategies to optimize the modeling procedure were discussed; The model transferability at time and space scales and the importance and challenge of constructing a universal forest biomass estimation model were then discussed. This paper highlighted the unique characteristics of LiDAR data from different platforms and indicated the necessity of incorporating LiDAR with other remotely sensed data for improving forest biomass estimation. This paper also indicated the importance of developing an optimized modeling procedure through examining modeling uncertainty and the values of developing a universal biomass estimation model through combination of physically based models and machine learning methods. This paper provided researchers a better understanding of the current situations of LiDAR technologies in forest biomass estimation research, and new insights for better employing relevant LiDAR data for improving forest biomass estimation at different scales.

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