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

文献类型：期刊论文

作者Yang, Qiuli1; Su, Yanjun1; Hu, Tianyu1; Jin, Shichao9; Liu, Xiaoqiang1; Niu, Chunyue1; Liu, Zhonghua1; Kelly, Maggi7,8; Wei, Jianxin4,5,6; Guo, Qinghua3
刊名FOREST ECOSYSTEMS
出版日期2022
卷号9
ISSN号2095-6355
关键词Forest aboveground biomass Drone LiDAR Allometric relationship Power law Tree height Vegetation index
DOI10.1016/j.fecs.2022.100059
文献子类Article
英文摘要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 broadleaf-leaved mixed forest, and then tropical broadleaf forest. Furthermore, the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression, artificial neural networks, and Random Forest. The proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.
学科主题Forestry
电子版国际标准刊号2197-5620
出版地BEIJING
WOS关键词REMOTELY-SENSED DATA ; TROPICAL FOREST ; METABOLIC ECOLOGY ; CARBON STOCKS ; GENERAL-MODEL ; TREE SIZE ; NDVI ; IMAGERY ; PREDICTION ; PATTERNS
WOS研究方向Science Citation Index Expanded (SCI-EXPANDED)
语种英语
出版者KEAI PUBLISHING LTD
WOS记录号WOS:000843257400001
资助机构Strategic Priority Research Program of Chinese Academy of Sciences [XDA19050401] ; National Natural Science Foundation of China [41871332, 31971575, 41901358]
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/29013]  
专题植被与环境变化国家重点实验室
作者单位1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
2.Peking Univ, Inst Remote Sensing & Geog Informat Syst, Sch Earth & Space Sci, Beijing 100871, Peoples R China
3.Xinjiang Land & Resources Informat Ctr, Urumqi 830002, Xinjiang, Peoples R China
4.Xinjiang Lidar Appl Engn Technol Res Ctr, Urumqi 830002, Xinjiang, Peoples R China
5.Xinjiang Univ, Coll Geog & Remote Sensing Sci, Urumqi 830017, Xinjiang, Peoples R China
6.Univ Calif Berkeley, Div Agr & Nat Resources, Berkeley, CA 94720 USA
7.Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA
8.Nanjing Agr Univ, Acad Adv Interdisciplinary Studies, Plant Phen Res Ctr, Collaborat Innovat Ctr Modern Crop Prod Cosponsore, Nanjing 210095, Peoples R China
9.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Qiuli,Su, Yanjun,Hu, Tianyu,et al. Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes[J]. FOREST ECOSYSTEMS,2022,9.
APA Yang, Qiuli.,Su, Yanjun.,Hu, Tianyu.,Jin, Shichao.,Liu, Xiaoqiang.,...&Guo, Qinghua.(2022).Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes.FOREST ECOSYSTEMS,9.
MLA Yang, Qiuli,et al."Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes".FOREST ECOSYSTEMS 9(2022).

入库方式： OAI收割

来源：植物研究所

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