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Combing remote sensing information entropy and machine learning for ecological environment assessment of Hefei-Nanjing-Hangzhou region, China

文献类型：期刊论文

作者Zhang, Hongyi2; Liu, Yong3; Li, Xinghua2; Feng, Ruitao4; Gong, Yuting1; Jiang, Yazhen5; Guan, Xiaobin1; Li, Shuang2
刊名JOURNAL OF ENVIRONMENTAL MANAGEMENT
出版日期2023
卷号325页码:14
ISSN号0301-4797
关键词Entropy method Random forest Remote sensing Urban ecological environment assessment (UEEA) UECIIMP
DOI10.1016/j.jenvman.2022.116533
通讯作者Li, Xinghua(lixinghua5540@whu.edu.cn) ; Jiang, Yazhen(jiangyz@lreis.ac.cn)
英文摘要Urban ecological environment is the basis of citizens' survival and development. A rapid and objective urban ecological environment assessment (UEEA) plays an important role in the urban sustainable development and environment protection. This study established an improved urban ecological comfort index (UECIIMP), which is based on our previous UECI and fully composed of four remote sensing indicators: normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), land surface temperature (LST), and aerosol optical depth (AOD), representing the greenness, dryness, heat, and atmospheric turbidity, respectively. Combining the entropy method and random forest (RF) algorithm, the weights of four indicators were calculated. To improve the accuracy of UECIIMP, the gap-filled quarterly mean results of each indicator with 30m resolution were obtained using the harmonic analysis of time series (HANTS) method and spatial-temporal information fusion based on non-local means filter (STNLFFM). UECI(IMP )was applied to the Hefei-Nanjing-Hangzhou Region to explore its spatiotemporal changes and response characteristics. Results show that the weights of UECIIMP fluctuate slightly (within 10%) before and after sensitivity analysis, with good stability and reliability. UECI(IMP )in Hangzhou > Hefei approximate to Nanjing, spring approximate to autumn > summer >> winter. From 2009 to 2019, UECI(IMP )has improved in all 33 districts of the Hefei-Nanjing-Hangzhou Region. The significant improvement of UECI(IMP )in 2014-2019 is 4.3 times than that in 2009-2014. The correlation between UECI and economic index indicates that eco nomic development has a positive impact on the urban ecological environment. The significant degradation of UECIIMP in the urban expansion area demonstrates a negative impact on the local environment from urban expansion.
WOS关键词SECURITY ASSESSMENT ; URBAN ; SUSTAINABILITY ; SYSTEM ; CITY ; INDICATORS ; TIANJIN ; STREAM ; FUZZY ; MODEL
资助项目National Natural Science Foundation of China (NSFC) ; [41901357]
WOS研究方向Environmental Sciences & Ecology
语种英语
出版者ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
WOS记录号WOS:000887073000005
资助机构National Natural Science Foundation of China (NSFC)
源URL[http://ir.igsnrr.ac.cn/handle/311030/187502]  
专题中国科学院地理科学与资源研究所
通讯作者Li, Xinghua; Jiang, Yazhen
作者单位1.Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China
2.Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China
3.CCCC Second Highway Consultants CO LTD, Wuhan 430056, Peoples R China
4.Shaanxi Normal Univ, Sch Geog & Tourism, Xian 710062, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Hongyi,Liu, Yong,Li, Xinghua,et al. Combing remote sensing information entropy and machine learning for ecological environment assessment of Hefei-Nanjing-Hangzhou region, China[J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT,2023,325:14.
APA Zhang, Hongyi.,Liu, Yong.,Li, Xinghua.,Feng, Ruitao.,Gong, Yuting.,...&Li, Shuang.(2023).Combing remote sensing information entropy and machine learning for ecological environment assessment of Hefei-Nanjing-Hangzhou region, China.JOURNAL OF ENVIRONMENTAL MANAGEMENT,325,14.
MLA Zhang, Hongyi,et al."Combing remote sensing information entropy and machine learning for ecological environment assessment of Hefei-Nanjing-Hangzhou region, China".JOURNAL OF ENVIRONMENTAL MANAGEMENT 325(2023):14.

入库方式： OAI收割

来源：地理科学与资源研究所

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