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Integration of deep learning algorithms with a Bayesian method for improved characterization of tropical deforestation frontiers using Sentinel-1 SAR imagery

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成果类型:
期刊论文
作者:
Sun, Rui;Zhao, Feng;Huang, Chengquan;Huang, Huabing;Lu, Zhong;...
通讯作者:
Zhao, F
作者机构:
[Zhao, Ping; Ni, Xiang; Meng, Ran; Sun, Rui] Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China.
[Zhao, Feng] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.
[Huang, Chengquan] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA.
[Huang, Huabing] Sun Yat sen Univ, Sch Geospatial Engn & Sci, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China.
[Lu, Zhong] Southern Methodist Univ, Roy M Huffington Dept Earth Sci, Dallas, TX 75275 USA.
通讯机构:
[Zhao, F ] C
Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Deforestation seasonality;Fine-scale temporal pattern;SAR speckle noise;Residual learning;Bayesian updating;Brazilian Amazon
期刊:
Remote Sensing of Environment
ISSN:
0034-4257
年:
2023
卷:
298
基金类别:
National Natural Science Foundation of China [41901382]; Key Research and Development Program of Heilongjiang, China [2022ZX01A25]; HZAU interdisciplinary sciences research fund [101510321040, 2662022JC007]; Fundamental Research Funds for the Central Universities, Beijing, China [2662022ZHYJ002]
机构署名:
本校为通讯机构
院系归属:
城市与环境科学学院
摘要:
Tropical deforestation frontiers continue to expand at alarming rates, but their fine-scale temporal patterns (e.g., start timing, patch forming speed, temporal clustering within a year) remain unresolved. Previous deforestation monitoring focus on the annual dynamics or the timely identification of deforestation activities; however, improved methods are needed for accurate mapping of deforestation patches at higher temporal resolution (i.e., sub-monthly) to better reveal their fine-scale temporal dynamics. We propose an optimization method inte-grating the spatial and temporal context informa...

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