Improving aboveground biomass estimation of natural forests on the Tibetan Plateau using spaceborne LiDAR and machine learning algorithms

Fugen Jiang; Hua Sun; Kaisen Ma; Liyong Fu; Jie Tang

Ecological Indicators (Oct 2022)

Improving aboveground biomass estimation of natural forests on the Tibetan Plateau using spaceborne LiDAR and machine learning algorithms

Fugen Jiang,
Hua Sun,
Kaisen Ma,
Liyong Fu,
Jie Tang

Affiliations

Fugen Jiang: Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China; Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, Hunan 410004, China; Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, Hunan 410004, China
Hua Sun: Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China; Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, Hunan 410004, China; Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, Hunan 410004, China; Corresponding author at: Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
Kaisen Ma: Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China; Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, Hunan 410004, China; Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, Hunan 410004, China
Liyong Fu: Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China; Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Jie Tang: Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China; Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, Hunan 410004, China; Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, Hunan 410004, China

Journal volume & issue: Vol. 143
p. 109365

Abstract

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Natural forests have the most complex structure and richest biodiversity among terrestrial ecosystems and are essential for maintaining the carbon balance and stability of the biosphere. Aboveground biomass (AGB) is a primary indicator used to evaluate forests and can directly measure forest growth and the quality of natural forests. Accurate and rapid AGB estimations can significantly improve the efficiency of forest management and deepen the understanding of the forest carbon cycle. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), one of the most recently launched spaceborne light detection and ranging (LiDAR) instruments, can penetrate forest canopies and obtain accurate, large-scale forest vertical parameter measurements. However, the discrete footprints offered by ICESat-2 cannot provide comprehensive spatial AGB distribution data. In this study, an optimized extreme learning machine (ELM) method was proposed to estimate the AGB of natural forests on the eastern Qinghai-Xizang Plateau, China. The synthetic Sentinel-2 images acquired from the Google Earth Engine (GEE) were synergized with ICESat-2 to realize continuous AGB mapping. To verify the effectiveness of the optimized method, support vector machine (SVM), k-nearest neighbor (kNN), and backpropagation (BP) neural network models were also established for comparison. The measured AGB data extracted from the forest management inventory (FMI) were used for model accuracy evaluation. The results show that the optimized ELM achieved the best estimation effect among all the analyzed models, with an R2 value of 0.68 and a root mean square error (RMSE) value of 25.14 mg/ha. The optimized ELM obtained the minimum RMSE and greatly improved the AGB prediction efficiency. These findings prove the ability of ICESat-2 in estimating the AGB of natural forests, which can provide a new way for large-scale forest resources investigation in high-altitude areas with a harsh environment.

Published in Ecological Indicators

ISSN: 1470-160X (Print); 1872-7034 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General): Ecology
Website: https://www.sciencedirect.com/journal/ecological-indicators

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