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Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

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成果类型:
期刊论文
作者:
Zhang, Fanyi;Tian, Xin;Zhang, Haibo;Jiang, Mi
通讯作者:
Xin Tian
作者机构:
[Zhang, Fanyi; Tian, Xin] Southeast Univ, Sch Transportat, Dept Surveying & Mapping Engn, Nanjing 211189, Peoples R China.
[Zhang, Haibo] Hengyang Normal Univ, Coll Geog & Tourism, Hengyang 421002, Peoples R China.
[Jiang, Mi] Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Guangzhou 510275, Peoples R China.
通讯机构:
[Xin Tian] D
Department of Surveying and Mapping Engineering, School of Transportation, Southeast University, Nanjing 211189, China<&wdkj&>Author to whom correspondence should be addressed.
语种:
英文
关键词:
carbon density;biomass;deep learning;multisource remote sensing;regression models
期刊:
Remote Sensing
ISSN:
2072-4292
年:
2022
卷:
14
期:
13
页码:
3022-
基金类别:
This research was funded by the National Natural Science Foundation of China (grant numbers 41801244, 42074008 and 51979040).
机构署名:
本校为其他机构
院系归属:
地理与旅游学院
摘要:
Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored th...

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