Human Settlement Analysis Based on Multi-temporal Remote Sensing Data: A Case Study of Xuzhou City, China

被引:0
|
作者
ZHU Jishuai [1 ]
TIAN Shufang [2 ]
TAN Kun [1 ]
DU Peijun [3 ]
机构
[1] Jiangsu Key Laboratory of Resources and Environment Information Engineering, China University of Mining and Technology
[2] China University of Geosciences
[3] Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University
基金
中国国家自然科学基金;
关键词
habitability; human settlement; Landsat; human settlement environment development index(HSEDI) model;
D O I
暂无
中图分类号
F299.2 [中国]; P237 [测绘遥感技术];
学科分类号
1204 ; 1404 ;
摘要
To evaluate urban human settlement, we propose a human settlement environment development index(HSEDI) model by choosing vegetation coverage, land surface temperature, impervious surfaces, slope, wetness, and water condition as the evaluation factors. We applied the proposed model to Xuzhou City, Jiangsu Province, China. Landsat-5 Thematic Mapper(TM) images from 1998 to 2010 and digital elevation model(DEM) data with a 30-m resolution were used to calculate the values of the six evaluation factors. The HSEDI value in Xuzhou City was found to be between 2.24 and 8.10 from 1998 to 2010, and it was further divided into five levels, uninhabitable, moderately uninhabitable, generally inhabitable, moderately inhabitable, and inhabitable. The best HSEDI value was in 2007. The generally inhabitable region was about 100.98 km2, covering 30.87% of the total area in 2007; the moderately inhabitable region was about 170.58 km2 covering 52.15% of the total area; the inhabitable region was about 32.03 km2, covering 9.79% of the total area; the percentage of the uninhabitable region was zero; and that of the moderately uninhabitable region was very small, less than 1.00%. Moreover, we analyzed the habitability in the respect of spatial patterns and change detection. Results show that the degraded regions of habitability quality are mainly located in the urban fringe and the improved regions are mainly located in the main urban and rural areas. Reason for the degraded habitability quality is the rapid progress of urbanization. However, the increase in urban green spaces and the construction of the main urban area promoted the improved habitability quality. Besides, we further analyzed socio-economic and socio-demographic data to confirm the results of the habitability analysis. The results indicate that the human settlement in Xuzhou City is in a satisfactory condition, but some efforts should be made to control the possible uninhabitable and moderately uninhabitable regions, and to improve the quality of the generally inhabitable regions.
引用
收藏
页码:389 / 400
页数:12
相关论文
共 50 条
  • [31] Assessing the impact of urbanization on net primary productivity using multi-scale remote sensing data: a case study of Xuzhou, China
    Tan, Kun
    Zhou, Songyang
    Li, Erzhu
    Du, Peijun
    FRONTIERS OF EARTH SCIENCE, 2015, 9 (02) : 319 - 329
  • [32] Assessing the impact of urbanization on net primary productivity using multi-scale remote sensing data:a case study of Xuzhou,China
    Kun TAN
    Songyang ZHOU
    Erzhu LI
    Peijun DU
    Frontiers of Earth Science, 2015, 9 (02) : 319 - 329
  • [33] Assessing the impact of urbanization on net primary productivity using multi-scale remote sensing data: a case study of Xuzhou, China
    Kun Tan
    Songyang Zhou
    Erzhu Li
    Peijun Du
    Frontiers of Earth Science, 2015, 9 : 319 - 329
  • [34] Winter Wheat Yield Estimation Based on Multi-Temporal and Multi-Sensor Remote Sensing Data Fusion
    Li, Yang
    Zhao, Bo
    Wang, Jizhong
    Li, Yanjun
    Yuan, Yanwei
    AGRICULTURE-BASEL, 2023, 13 (12):
  • [35] Land Use/Land Cover Change Analysis Using Multi-Temporal Remote Sensing Data: A Case Study of Tigris and Euphrates Rivers Basin
    Al-Taei, Azher Ibrahim
    Alesheikh, Ali Asghar
    Boloorani, Ali Darvishi
    LAND, 2023, 12 (05)
  • [36] Nitrogen Monitoring and Sugar Yield Estimation Analysis of Sugar Beet Based on Multisource and Multi-temporal Remote Sensing Data
    Wang, Jingyun
    Hu, Xiaohang
    Dong, Xinjiu
    Liu, Shuo
    Li, Yanli
    SUGAR TECH, 2025,
  • [37] A parametric model for classifying land cover and evaluating training data based on multi-temporal remote sensing data
    Glanz, Hunter
    Carvalho, Luis
    Sulla-Menashe, Damien
    Friedl, Mark A.
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2014, 97 : 219 - 228
  • [38] MULTI-TEMPORAL CHANGE DETECTION BASED ON CHINA'S DOMESTIC HYPERSPECTRAL REMOTE SENSING SATELLITE IMAGES
    Lu, Xuanning
    Liu, Sicong
    Du, Kechen
    IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XXVIII, 2022, 12267
  • [39] Study on urban heat island of Shanghai by using multi-temporal remote sensing data and air temperature data
    Zhu, Shanyou
    Zhang, Guixin
    Chen, Jian
    2009 JOINT URBAN REMOTE SENSING EVENT, VOLS 1-3, 2009, : 197 - 201
  • [40] An Analysis of the Evolution of Nanjing's Green Space Based on Multi-temporal Remote Sensing Images
    Xu, Hao
    Qian, Gang
    TRENDS IN BUILDING MATERIALS RESEARCH, PTS 1 AND 2, 2012, 450-451 : 1331 - +