An accurate fringe extraction model of small- and medium-sized urban areas using multi-source data

被引:4
作者
Li, Jianfeng [1 ,2 ,3 ,4 ,5 ]
Peng, Biao [1 ,2 ,5 ]
Liu, Siqi [1 ,2 ,5 ]
Ye, Huping [3 ]
Zhang, Zhuoying [4 ,6 ]
Nie, Xiaowei [4 ,7 ]
机构
[1] Shaanxi Land Engn Construct Grp Co Ltd, Technol Innovat Ctr Land Engn & Human Settlements, Xian, Peoples R China
[2] Xi An Jiao Tong Univ, Xian, Peoples R China
[3] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
[4] Chinese Acad Sci, Inst Tibetan Plateau Res, State Key Lab Tibetan Plateau Earth Syst Environm, Beijing, Peoples R China
[5] Shaanxi Prov Land Engn Construct Grp Co Ltd, Inst Land Engn & Technol, Xian, Peoples R China
[6] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
[7] Tibet Univ, Coll Sci, Tibet, Peoples R China
关键词
landscape disorder degree; kernel density estimation (KDE); night light intensity; geographical detector (Geodetector); urban fringe; KERNEL DENSITY-ESTIMATION; REMOTE-SENSING IMAGERY; LAND-USE; IDENTIFICATION; REGION; CHINA; CLASSIFICATION; URBANIZATION; SELECTION; CITIES;
D O I
10.3389/fenvs.2023.1118953
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Urban fringes are of great significance to urban development as connecting hubs between urban and rural areas. However, there are many problems in urban fringes, including disorderly spatial layout, waste of social resources, and low quality of human settlements. Rapid and accurate identification of urban fringes has important practical significance for optimizing urban spatial layout, controlling urban unlimited expansion, and protecting land resources. Given the lack of suitable and high-quality fringe extraction models for small- and medium-sized urban areas, this study was based on Gaofen-2 (GF-2) imagery, Suomi National Polar-orbiting Partnership Visible Infrared Imager Radiometer Suite (NPP-VIIRS) imagery, point of interest (POI) data, and WorldPop data, taking the landscape disorder degree, POI kernel density, and night light intensity as urban feature factors and constructing a fringe extraction model of small- and medium-sized urban areas (FEM-SMU). Taking Hantai District in China as the study area, the results of the model were compared to the landscape disorder degree threshold method and POI kernel density breakpoint analysis method, while the generality of the model was further tested in Shangzhou and Hanbin Districts. The results show that the FEM-SMU model has evident improvements over the conventional methods in terms of accuracy, detail, and integrity, and has higher versatility, which can better meet the research needs of small- and medium-sized urban fringes.
引用
收藏
页数:11
相关论文
共 58 条
[11]   Effect of land-centered urbanization on rural development: A regional analysis in China [J].
Feng, Weilun ;
Liu, Yansui ;
Qu, Lulu .
LAND USE POLICY, 2019, 87
[12]   Land-use change in the 'edgelands': Policies and pressures in London's rural-urban fringe [J].
Gant, Robert L. ;
Robinson, Guy M. ;
Fazal, Shahab .
LAND USE POLICY, 2011, 28 (01) :266-279
[13]   Clustering Urban Multifunctional Landscapes Using the Self-Organizing Feature Map Neural Network Model [J].
Gao, Yang ;
Feng, Zhe ;
Wang, Yang ;
Liu, Jin-Long ;
Li, Shuang-Cheng ;
Zhu, Yu-Kun .
JOURNAL OF URBAN PLANNING AND DEVELOPMENT, 2014, 140 (02)
[14]   Bandwidth selection for kernel density estimation: a review of fully automatic selectors [J].
Heidenreich, Nils-Bastian ;
Schindler, Anja ;
Sperlich, Stefan .
ASTA-ADVANCES IN STATISTICAL ANALYSIS, 2013, 97 (04) :403-433
[15]   Apply fringe identification to understand urban economic development in China: in case of Wuhan [J].
Liu H. .
Arabian Journal of Geosciences, 2021, 14 (13)
[16]   Delineating Urban Fringe Area by Land Cover Information Entropy-An Empirical Study of Guangzhou-Foshan Metropolitan Area, China [J].
Huang, Junyi ;
Zhou, Qiming ;
Wu, Zhifeng .
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2016, 5 (05)
[17]  
Ikram ST, 2017, J KING SAUD UNIV-COM, V29, P462, DOI 10.1016/j.jksuci.2015.12.004
[18]   Remote sensing of the urban heat island effect across biomes in the continental USA [J].
Imhoff, Marc L. ;
Zhang, Ping ;
Wolfe, Robert E. ;
Bounoua, Lahouari .
REMOTE SENSING OF ENVIRONMENT, 2010, 114 (03) :504-513
[19]   Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine [J].
Li, Haitao ;
Gu, Haiyan ;
Han, Yanshun ;
Yang, Jinghui .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2010, 31 (06) :1453-1470
[20]   Spatiotemporal distribution characteristics and mechanism analysis of urban population density: A case of Xi'an, Shaanxi, China [J].
Li, Jingang ;
Li, Jianwei ;
Yuan, Yangzi ;
Li, Guifang .
CITIES, 2019, 86 :62-70