Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes

作者：Qiuli Yang, Yanjun Su, Tianyu Hu, Shichao Jin, Xiaoqiang Liu, Chunyue Niu, Zhonghua Liu, Maggi Kelly, Jianxin Wei, Qinghua Guo · 发表于：Forest Ecosystems · 年份：2022 · DOI：10.1016/j.fecs.2022.100059 · 被引用次数：66 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Forest ecology and management

Accurate estimates of forest aboveground biomass (AGB) are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets. However, combining the advantages of active and passive data sources to improve estimation accuracy remains challenging. Here, we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral information. Over 60 km2 of drone light detection and ranging (LiDAR) data and 1,370 field plot measurements, covering the four major forest types of China (coniferous forest, sub-tropical broadleaf forest, coniferous and broadleaf-leaved mixed forest, and tropical broadleaf forest), were collected together with Sentinel-2 images to evaluate the proposed approach. The results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum values. Compared with structural attributes used alone, combining structural and spectral information can improve the estimation accuracy of AGB, increasing R2 by about 10% and reducing the root mean square error by about 22%; the accuracy of the proposed approach can yield a R2 of 0.7 in different forests types. The proposed approach performs the best in coniferous forest, followed by sub-tropical broadleaf forest, coniferous and bro...