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

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

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
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 mul-tiple regression,artificial neural networks,and Random Forest.The proposed approach may provide an alter-native solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.

Forest aboveground biomassDrone LiDARAllometric relationshipPower lawTree heightVegetation index

Qiuli Yang、Yanjun Su、Tianyu Hu、Shichao Jin、Xiaoqiang Liu、Chunyue Niu、Zhonghua Liu、Maggi Kelly、Jianxin Wei、Qinghua Guo

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State Key Laboratory of Vegetation and Environmental Change,Institute of Botany,Chinese Academy of Sciences,Beijing,100093,China

University of Chinese Academy of Sciences,Beijing,100049,China

Plant Phenomics Research Centre,Academy for Advanced Interdisciplinary Studies,Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry,Nanjing Agricultural University,Nanjing,210095,China

Department of Environmental Sciences,Policy and Management,University of California,Berkeley,CA,94720-3114,USA

Division of Agriculture and Natural Resources,University of California,Berkeley,CA,94720-3114,USA

College of Geography and Remote Sensing Sciences,Xinjiang University,Urumqi,Xinjiang,830017,China

Xinjiang Lidar Applied Engineering Technology Research Center,Urumqi,Xinjiang,830002,China

Xinjiang Land and Resources Information Center,Urumqi,Xinjiang,830002,China

Institute of Remote Sensing and Geographic Information System,School of Earth and Space Sciences,Peking University,Beijing,100871,China

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中国科学院战略规划重点项目国家自然科学基金国家自然科学基金国家自然科学基金

XDA19050401418713323197157541901358

2022

森林生态系统(英文版)
北京林业大学

森林生态系统(英文版)

CSTPCDCSCDSCI
影响因子：0.09
ISSN：2095-6355
年,卷(期)：2022.9(5)
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