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FOREST ABOVEGROUND BIOMASS ESTIMATION FROM HIGH-RESOLUTION IMAGERY IN WUHAN CITY, CHINA

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成果类型:
会议论文
作者:
Mamat, Ayzohra;Liu, Xueyi;Huang, Wenli;Feng, Tianqi;Yang, Xinyi;...
通讯作者:
Huang, W
作者机构:
[Huang, Wenli; Yang, Xinyi; Mamat, Ayzohra; Feng, Tianqi] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.
[Song, Danxia; Liu, Xueyi; Mamat, Ayzohra] China Agr Univ, Coll Land Sci & Technol, Beijing, Peoples R China.
Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.
通讯机构:
[Huang, W ] W
Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.
语种:
英文
关键词:
forest aboveground biomass;texture features;random forest;Jilin-1;high-resolution images
期刊:
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
ISSN:
2153-6996
年:
2023
页码:
3311-3314
会议名称:
IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
会议论文集名称:
IEEE International Symposium on Geoscience and Remote Sensing IGARSS
会议时间:
JUL 16-21, 2023
会议地点:
Pasadena, CA
会议主办单位:
[Mamat, Ayzohra;Huang, Wenli;Feng, Tianqi;Yang, Xinyi] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.^[Mamat, Ayzohra;Liu, Xueyi;Song, Danxia] China Agr Univ, Coll Land Sci & Technol, Beijing, Peoples R China.^Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.
出版地:
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者:
IEEE
ISBN:
979-8-3503-2010-7
基金类别:
National Key R&D Program of China [2022YFB3903304]; Fundamental Research Funds for the Central Universities [2042022kf1073]
机构署名:
本校为其他机构
院系归属:
城市与环境科学学院
摘要:
Current assessments of urban forest carbon storage were missing or largely underestimating their values due to limited spatial resolution. In this study, combining field plot measurements and satellite imagery, a wall-to-wall forest biomass map were generated at a very high spatial resolution (5 m) over urban areas in Wuhan City, China. Specifically, a series of characteristic metrics were extracted from Jilin-1 satellite images, including multispectral reflectances, vegetation indices, and texture features. The estimations of forest aboveground biomass from three machine learning models were ...

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