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Frontiers of Earth Science

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Front. Earth Sci.    2019, Vol. 13 Issue (1) : 1-17    https://doi.org/10.1007/s11707-018-0683-2
RESEARCH ARTICLE
Spatial-temporal variations of natural suitability of human settlement environment in the Three Gorges Reservoir Area—A case study in Fengjie County, China
Jieqiong LUO1,2,3, Tinggang ZHOU4,5, Peijun DU1,2,3(), Zhigang XU1,2,3
1. Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China
2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4. School of Geographical Sciences, Southwest University, Chongqing 400715, China
5. Key Laboratory of Eco-environments in Three Gorges Reservoir Region (Ministry of Education), Chongqing 400715, China
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Abstract

With rapid environmental degeneration and socio-economic development, the human settlement environment (HSE) has experienced dramatic changes and attracted attention from different communities. Consequently, the spatial-temporal evaluation of natural suitability of the human settlement environment (NSHSE) has become essential for understanding the patterns and dynamics of HSE, and for coordinating sustainable development among regional populations, resources, and environments. This study aims to explore the spatial-temporal evolution of NSHSE patterns in 1997, 2005, and 2009 in Fengjie County near the Three Gorges Reservoir Area (TGRA). A spatially weighted NSHSE model was established by integrating multi-source data (e.g., census data, meteorological data, remote sensing images, DEM data, and GIS data) into one framework, where the Ordinary Least Squares (OLS) linear regression model was applied to calculate the weights of indices in the NSHSE model. Results show that the trend of natural suitability has been first downward and then upward, which is evidenced by the disparity of NSHSE existing in the south, north, and central areas of Fengjie County. Results also reveal clustered NSHSE patterns for all 30 townships. Meanwhile, NSHSE has significant influence on population distribution, and 71.49% of the total population is living in moderate and high suitable districts.
Keywords natural suitability of human settlement environment      ordinary least squares model      global and local spatial autocorrelation analyses      Three Gorges Reservoir Area (TGRA)      Fengjie County     
Corresponding Author(s): Peijun DU   
Online First Date: 01 February 2018    Issue Date: 25 January 2019
 Cite this article:   
Jieqiong LUO,Tinggang ZHOU,Peijun DU, et al. Spatial-temporal variations of natural suitability of human settlement environment in the Three Gorges Reservoir Area—A case study in Fengjie County, China[J]. Front. Earth Sci., 2019, 13(1): 1-17.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0683-2
https://academic.hep.com.cn/fesci/EN/Y2019/V13/I1/1
Fig.1  Study area: Fengjie County (Chongqing City, China) with 30 townships. 1- Yong’an, 2- Baidi, 3- Caotang, 4- Fenhe, 5- Kangle, 6- Dashu, 7- Zhuyuan, 8- Gongping, 9- Zhuyi, 10- Jiagao, 11- Yangshi, 12- Tuxiang, 13- Xinglong, 14- Qinglong, 15- Xinmin, 16- Yongle, 17- Yanwan, 18- Ping’an, 19- Hongtu, 20- Qinglian, 21- Shigang, 22- Kangping, 23- Wuma, 24- Taihe Tujiazu, 25- Anping, 26- Hefeng, 27- Fengping, 28- Chang’an Tujiazu, 29- Longqiao Tujiazu, 30- Yunwu Tujiazu.
Fig.2  The overall flowchart of the proposed method.
Data Sources Year Resolution or scale
Meteorological data Chongqing Municipal Bureau of Meteorology 1997, 2005, 2009 30 m
DEM data USGS Earth Resources Observation and Science (EROS) Center 2003 30 m
Landsat TM images USGS Earth Resources Observation and Science (EROS) Center 1997, 2005, 2009 30 m
Land cover data Geomatics Center of Chongqing 1997, 2005, 2009 1:10,000
Administrative division map Geomatics Center of Chongqing 2009 1:10,000
Census data Fengjie County Bureau of Statistics 1997, 2005, 2009
Tab.1  Basic information of the six datasets used in this study
Fig.3  THI and K range for various thermal sensation and stress levels.
Land use type Cropland Woodland Water
Paddy field Dry land Forest Shrubbery Scattered woodland Others River and canal Lake Beach Reservoir and pond
Weight 0.08 0.07 0.1 0.08 0.06 0.06 0.05 0.05 0.03 0.04
Land use type Grassland Construction land Unused land
High coverage Medium coverage Low coverage Urban land Rural settlement Others Bare land Stone land Others
Weight 0.06 0.05 0.04 0.08 0.06 0.04 0.02 0.01 0.02
Tab.2  The weight of various land cover types
Evaluation index Correlation coefficient (ci, i=1,2,3,4) Weight (wi, i=1,2,3,4)
RAI 0.48933 0.33577
HTC 0.17144 0.11764
WRI 0.20800 0.14273
LCI 0.58856 0.40386
Tab.3  Correlation coefficient and weight between each index and population data
Fig.4  Spatial distribution of NSHSE indices of Fengjie County in 1997, 2005, and 2009.
Fig.5  Classification maps of NSHSE indices of Fengjie County in 1997, 2005, and 2009.
Fig.6  Classification maps of NSHSE of Fengjie County in 1997, 2005, and 2009.
Suitable type 1997 2005 2009
Area/km2 Percent/% Area/km2 Percent/% Area/km2 Percent/%
Unsuitable area 151.73 3.71 264.95 6.48 162.85 3.98
Critical suitable area 758.30 18.55 805.27 19.70 568.87 13.92
Low suitable area 1725.60 42.23 1333.23 32.62 1059.84 25.93
Moderate suitable area 1157.72 28.33 1232.51 30.16 1500.86 36.72
High suitable area 293.65 7.18 451.04 11.04 794.58 19.45
Tab.4  Statistical areas of five suitable types of NSHSE of Fengjie County in 1997, 2005, and 2009
Indices Conceptualization of spatial relationships Moran’s I index Z-score (significant at the 0.10 level) p-value Global spatial pattern
RAI Inverse distance 0.63 4.08 0.00 Clustered
RAI Continuity edges and corners 0.57 4.72 0.00 Clustered
HTC1997 Inverse distance 0.22 1.63 0.10 Random
HTC1997 Continuity edges and corners 0.10 1.11 0.27 Random
HTC2005 Inverse distance 0.25 1.77 0.08 Clustered
HTC2005 Continuity edges and corners 0.25 2.29 0.02 Clustered
HTC2009 Inverse distance 0.23 1.64 0.10 Random
HTC2009 Continuity edges and corners 0.26 2.33 0.02 Clustered
WRI1997 Inverse distance 0.79 5.01 0.00 Clustered
WRI1997 Continuity edges and corners 0.77 6.23 0.00 Clustered
WRI2005 Inverse distance 0.48 3.13 0.00 Clustered
WRI2005 Continuity edges and corners 0.45 3.74 0.00 Clustered
WRI2009 Inverse distance 0.28 2.00 0.05 Clustered
WRI2009 Continuity edges and corners 0.34 3.00 0.00 Clustered
LCI1997 Inverse distance 0.61 4.09 0.00 Clustered
LCI1997 Continuity edges and corners 0.56 4.86 0.00 Clustered
LCI2005 Inverse distance 0.67 4.32 0.00 Clustered
LCI2005 Continuity edges and corners 0.58 4.78 0.00 Clustered
LCI2009 Inverse distance 0.72 4.58 0.00 Clustered
LCI2009 Continuity edges and corners 0.61 5.05 0.00 Clustered
NSHSE1997 Inverse distance 0.41 2.78 0.01 Clustered
NSHSE1997 Continuity edges and corners 0.32 2.83 0.00 Clustered
NSHSE2005 Inverse distance 0.57 3.84 0.00 Clustered
NSHSE2005 Continuity edges and corners 0.56 4.78 0.00 Clustered
NSHSE2009 Inverse distance 0.38 2.54 0.01 Clustered
NSHSE2009 Continuity edges and corners 0.37 3.22 0.00 Clustered
Tab.5  Global spatial pattern analysis based on two spatial conceptualizations
Fig.7  LISA spatial analyses of NSHSE indices with inverse distance as the conceptualization of spatial relationships for Fengjie County in 1997, 2005, and 2009.
Fig.8  LISA spatial analyses of NSHSE indices with continuity edges and corners as the conceptualization of spatial relationships for Fengjie County in 1997, 2005, and 2009.
Fig.9  Population distribution map of Fengjie County in 2009.
Fig.10  Population distribution statistics of 30 townships in Fengjie County in 2009.
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