Spatiotemporal Changes in The Urban Landscape Pattern and Driving Forces of LUCC Characteristics in The Urban Agglomeration on The Northern Slope of The Tianshan Mountains from 1995 to 2018
Abstract
:1. Introduction
2. Study Area and Data Source
2.1. Study Area
2.2. Data Sources and Pre-Processing
3. Research methods
3.1. Dynamic Degree of Land Use
3.2. Landscape Pattern Analysis
3.3. Degree of Comprehensive Land Use
3.4. Spatial Autocorrelation Analysis
3.5. Driving Force Analysis
4. Results
4.1. Temporal and Spatial Changes in Land Use Types
4.2. Analysis on the Dynamic Degree of Land Use
4.3. Land-Use Transfer Matrix
4.4. Landscape Pattern Analysis
4.5. Degree of Comprehensive Land Use
4.5.1. Spatial Clustering of the Degree of Comprehensive Land Use
4.5.2. Analysis of Driving Factors based on Geographic Detector
5. Discussions
5.1. Spatial Distribution of Landscape Patterns and LUCC Changing Trend
5.2. Influencing Factors of Urban Expansion
5.3. Research Limitations and Future Work
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Primary Type | Secondary Type | |
---|---|---|
Name | Number | Name |
Cropland | 11 | Cultivated land |
12 | Dry land | |
Forest | 21 | Has woodland |
22 | Bush forest | |
23 | Sparse woodland | |
24 | Other woodland | |
Grassland | 31 | High coverage grassland |
32 | Medium coverage grassland | |
33 | Low coverage grassland | |
Water | 41 | Canal |
42 | Lake | |
43 | Reservoir pond | |
44 | Permanent glacier snow | |
45 | Tidal flat | |
46 | Beach | |
Urban | 51 | Urban land |
52 | Rural settlement | |
53 | Other construction land | |
Unused | 61 | Sand |
62 | Gobi Desert | |
63 | Saline-alkali land | |
64 | Wetlands | |
65 | Bare land | |
66 | Bare rock | |
67 | Other | |
99 | Sea |
Landscape Metrics | Unit | Meaning |
---|---|---|
Patch density | pcs/km2 | The larger the value, the greater the number of patches |
Edge density | Meter/km2 | The larger the value, the greater the edge length of the patches |
Largest patch index | - | The larger the value, the larger the proportion of the largest patches to the landscape area |
Shannon diversity index | - | The larger the value, the greater the patches’ heterogeneity |
Land-Use Type | Unused Land | Forest Land, Grassland, and Water Bodies | Cultivated Land | Urban Land |
---|---|---|---|---|
Grading index | 1 | 2 | 3 | 4 |
Judgment Basis | Interaction Type |
---|---|
q(X1∩X2) < Min(q(X1), q(X2)) | Nonlinear weakening |
Min(q(X1), q(X2)) < q(X1∩X2) < Max(q(X1), q(X2) | Single-factor nonlinear weakening |
q(X1∩X2) > Max(q(X1), q(X2) | Two-factor enhancement |
q(X1∩X2) = q(X1) + q(X2) | Independent |
q(X1∩X2) > q(X1) + q(X2) | Nonlinear enhancement |
1995 | 2018 | |||||||
---|---|---|---|---|---|---|---|---|
Grassland | Cropland | Urban | Forest | Water | Urban | Total | ||
UANSTM | Grassland | 27,130.2 | 5988.4 | 666.2 | 574.1 | 301.3 | 5884.9 | 40,545.2 |
Cropland | 830.3 | 9076.9 | 781.5 | 24.8 | 59.7 | 50.3 | 10,823.4 | |
Urban | 71.1 | 271.1 | 628 | 1.8 | 2.3 | 107.9 | 1082.2 | |
Forest | 2101.4 | 557 | 68.9 | 1156.4 | 36.4 | 202.2 | 4122.3 | |
Water | 372 | 39.5 | 20.4 | 0.8 | 964.5 | 1700.4 | 3097.6 | |
Urban | 3845.3 | 1873.2 | 210.3 | 27.8 | 292.9 | 20,387.8 | 26,637.2 | |
Total | 34,350.3 | 17,806 | 2375.4 | 1785.6 | 1657 | 28,333.5 | 86,307.9 | |
Urumqi | Grassland | 11,662.8 | 305.8 | 526.4 | 234.9 | 107.1 | 2687.5 | 15,524.6 |
Cropland | 468.5 | 1666.9 | 414.3 | 12.5 | 12 | 18 | 2592.3 | |
Urban | 18.6 | 43.7 | 481.4 | 3.1 | 0.5 | 5.8 | 553.1 | |
Forest | 583.4 | 55.3 | 69.2 | 514.4 | 6.9 | 8 | 1237.2 | |
Water | 83.1 | 1.9 | 7.9 | 0 | 180.5 | 455.4 | 728.9 | |
Urban | 1305 | 98 | 115.5 | 9.1 | 116.2 | 4847.4 | 6491.3 | |
Changji | Grassland | 5575.1 | 500.8 | 81.7 | 104.1 | 43.9 | 488.4 | 6793.9 |
Cropland | 191.4 | 1786.3 | 223.6 | 15 | 17 | 2233.5 | ||
Urban | 8.7 | 41.3 | 46.2 | 0.7 | 97 | |||
Forest | 752.6 | 59.1 | 1.4 | 277.9 | 19.2 | 1110.1 | ||
Water | 56.2 | 39.8 | 17 | 0.1 | 102.8 | 251.7 | 467.5 | |
Urban | 349.4 | 434.3 | 1.8 | 7.3 | 3946.3 | 4739 | ||
Shihezi | Grassland | 130 | 33.3 | 31.3 | 0 | 0.4 | 0.5 | 195.4 |
Cropland | 12.3 | 448 | 126.4 | 4.5 | 3.4 | 3.1 | 597.7 | |
Urban | 0.3 | 5.9 | 60.4 | 66.6 | ||||
Forest | 0.3 | 4.8 | 6.3 | 0.6 | 12 | |||
Water | 9.2 | 3 | 1.7 | 2.9 | 0.5 | 17.3 | ||
Urban | 0.5 | 0.4 | 0.1 | 0.1 | 0.2 | 1.3 | ||
Karamay | Grassland | 2922.4 | 1033.8 | 150.5 | 1.2 | 93.8 | 523.4 | 4725.1 |
Cropland | 66.8 | 643.5 | 35.3 | 4.5 | 1.7 | 9.1 | 761 | |
Urban | 50.9 | 30.3 | 200.9 | 2.4 | 189.7 | 474.1 | ||
Forest | 280.6 | 149.9 | 23.3 | 0.5 | 8.1 | 140.1 | 602.5 | |
Water | 8.5 | 0.4 | 0.2 | 69.3 | 3.5 | 81.9 | ||
Urban | 1604.5 | 458.9 | 169.8 | 0.3 | 61.3 | 6726.3 | 9021.1 | |
Fukang | Grassland | 3486.2 | 578.7 | 99.4 | 101.1 | 19.4 | 5376 | 9660.8 |
Cropland | 39.1 | 752.6 | 38.8 | 10.6 | 3.3 | 844.3 | ||
Urban | 11.2 | 56.9 | 65.1 | 0.4 | 22.6 | 156.3 | ||
Forest | 155.7 | 99.2 | 3.5 | 163.8 | 3.7 | 0.4 | 426.3 | |
Water | 5.1 | 3.8 | 0.5 | 0.2 | 80.7 | 31.3 | 121.7 | |
Urban | 720.6 | 426.4 | 53.9 | 3 | 38.1 | 4221.7 | 5463.8 | |
Hutubi | Grassland | 6394.3 | 1669.9 | 48.8 | 173.1 | 33.1 | 135 | 8454.2 |
Cropland | 204.2 | 2054.6 | 116.6 | 1.4 | 35 | 6.8 | 2418.6 | |
Urban | 8.3 | 64.5 | 50.4 | 0.1 | 0 | 0.1 | 123.4 | |
Forest | 440.4 | 69.3 | 3.7 | 379.6 | 1.1 | 10.3 | 904.3 | |
Water | 184.4 | 6 | 6.2 | 0.6 | 73.9 | 176.8 | 448 | |
Urban | 668.8 | 224.1 | 30.8 | 0.1 | 25.4 | 5223.5 | 6172.7 | |
Manas | Grassland | 3942.7 | 1486.6 | 68.9 | 190.2 | 32.8 | 415.5 | 6136.7 |
Cropland | 150.9 | 2748.1 | 132.8 | 8.7 | 13.4 | 16.5 | 3070.5 | |
Urban | 17.9 | 86.7 | 72.2 | 0 | 0.2 | 0.1 | 177.2 | |
Forest | 352.1 | 93.2 | 3.9 | 252.7 | 10 | 13.5 | 725.5 | |
Water | 87.7 | 4 | 4.6 | 212.7 | 236.6 | 545.7 | ||
Urban | 231.2 | 291.2 | 3.8 | 2.9 | 107 | 6637.7 | 7273.9 | |
Wujiaqu | Grassland | 140.8 | 27.3 | 4.9 | 7.8 | 4 | 184.9 | |
Cropland | 74.2 | 280.6 | 47.4 | 0.7 | 8.3 | 411.2 | ||
Urban | 4.8 | 6.7 | 13.9 | 0 | 0.3 | 25.7 | ||
Forest | 2.6 | 3.3 | 2.4 | 0 | 0.8 | 9.1 | ||
Water | 1.1 | 2.5 | 0.3 | 14.1 | 3.4 | 21.3 | ||
Urban | 72.3 | 5.6 | 5.3 | 1.4 | 6.9 | 91.6 | ||
Wusu | Grassland | 10,274.1 | 2652.8 | 80.2 | 220.0 | 113.1 | 1319.8 | 14,660.1 |
Cropland | 144.3 | 2597.8 | 146.4 | 3.1 | 10.2 | 3.3 | 2905.1 | |
Urban | 1.5 | 48.4 | 44.5 | 0.1 | 0.6 | 95.1 | ||
Forest | 933.8 | 164.3 | 5.1 | 353.7 | 20.5 | 157.4 | 1634.8 | |
Water | 129.7 | 6.7 | 0.2 | 0.2 | 441.5 | 775.9 | 1354.1 | |
Urban | 2052.3 | 430.0 | 20.9 | 31.2 | 166.1 | 4740.7 | 7441.1 | |
Shawan | Grassland | 7014.2 | 2395.1 | 72.6 | 68 | 113.7 | 543.2 | 10,206.9 |
Cropland | 165.3 | 4242.6 | 162.9 | 13.2 | 10.8 | 4.9 | 4599.6 | |
Urban | 13.5 | 132.8 | 136.9 | 0.1 | 0.2 | 0 | 283.6 | |
Forest | 541.2 | 372.8 | 11.4 | 267.4 | 20.5 | 47.5 | 1260.8 | |
Water | 136.9 | 5.6 | 0.6 | 0.4 | 654.9 | 1,297 | 2095.5 | |
Urban | 410.4 | 1301.1 | 7.6 | 7.6 | 40.3 | 4050.4 | 5817.3 | |
Kuitun | Grassland | 654.4 | 1043.7 | 121.2 | 7 | 16.9 | 1.2 | 1844.4 |
Cropland | 24.2 | 252.3 | 28.1 | 0.2 | 2.4 | 0.2 | 307.5 | |
Urban | 5.7 | 38.4 | 44.1 | |||||
Forest | 6.1 | 21.9 | 1.4 | 0.2 | 29.6 | |||
Water | 7.9 | 0.5 | 0.1 | 3.2 | 11.7 | |||
Urban | 23.2 | 22.4 | 0.5 | 11.5 | 57.5 |
Factors | Q | |||||
---|---|---|---|---|---|---|
1995 | 2000 | 2005 | 2010 | 2015 | 2018 | |
DEM | 0.041 | 0.053 | 0.089 | 0.045 | 0.062 | 0.062 |
GDP | 0.047 | 0.020 | 0.008 | 0.012 | 0.011 | 0.011 |
NDVI | 0.422 | 0.023 | 0.067 | 0.595 | 0.590 | 0.590 |
River density | 0.274 | 0.032 | 0.059 | 0.384 | 0.374 | 0.374 |
Precipitation | 0.142 | 0.020 | 0.021 | 0.053 | 0.050 | 0.050 |
Population | 0.022 | 0.036 | 0.003 | 0.010 | 0.013 | 0.013 |
Sunshine hours | 0.023 | 0.025 | 0.018 | 0.093 | 0.086 | 0.086 |
Temperature | 0.078 | 0.039 | 0.009 | 0.122 | 0.119 | 0.119 |
Slope | 0.011 | 0.005 | 0.036 | 0.014 | 0.019 | 0.019 |
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Zhao, Y.; Kasimu, A.; Gao, P.; Liang, H. Spatiotemporal Changes in The Urban Landscape Pattern and Driving Forces of LUCC Characteristics in The Urban Agglomeration on The Northern Slope of The Tianshan Mountains from 1995 to 2018. Land 2022, 11, 1745. https://doi.org/10.3390/land11101745
Zhao Y, Kasimu A, Gao P, Liang H. Spatiotemporal Changes in The Urban Landscape Pattern and Driving Forces of LUCC Characteristics in The Urban Agglomeration on The Northern Slope of The Tianshan Mountains from 1995 to 2018. Land. 2022; 11(10):1745. https://doi.org/10.3390/land11101745
Chicago/Turabian StyleZhao, Yongyu, Alimujiang Kasimu, Pengwen Gao, and Hongwu Liang. 2022. "Spatiotemporal Changes in The Urban Landscape Pattern and Driving Forces of LUCC Characteristics in The Urban Agglomeration on The Northern Slope of The Tianshan Mountains from 1995 to 2018" Land 11, no. 10: 1745. https://doi.org/10.3390/land11101745
APA StyleZhao, Y., Kasimu, A., Gao, P., & Liang, H. (2022). Spatiotemporal Changes in The Urban Landscape Pattern and Driving Forces of LUCC Characteristics in The Urban Agglomeration on The Northern Slope of The Tianshan Mountains from 1995 to 2018. Land, 11(10), 1745. https://doi.org/10.3390/land11101745