The impact of socioeconomic development on land-use and land-cover change (LUCC) in river basins varies spatially and temporally. Exploring the spatiotemporal evolutionary trends and drivers of LUCC under regional disparities is the basis for the sustainable development and management of basins. In this study, the Weihe River Basin (WRB) in China was selected as a typical basin, and the WRB was divided into the upstream of the Weihe River Basin (UWRB), the midstream of the Weihe River Basin (MWRB), the downstream of the Weihe River Basin (DWRB), the Jinghe River Basin (JRB), and the Luohe River Basin (LRB). Based on land-use data (cultivated land, forestland, grassland, built-up land, bare land, and water body) from 1985 to 2020, we analyzed the spatiotemporal heterogeneity of LUCC in the WRB using a land-use transfer matrix and a dynamic change model. The driving forces of LUCC in the WRB in different periods were detected using the GeoDetector, and the selected influencing factors included meteorological factors (precipitation and temperature), natural factors (elevation, slope, soil, and distance to rivers), social factors (distance to national highway, distance to railway, distance to provincial highway, and distance to expressway), and human activity factors (population density and gross domestic product (GDP)). The results indicated that the types and intensities of LUCC conversions showed considerable disparities across different sub-basins, where complex conversions among cultivated land, forestland, and grassland occurred in the LRB, JRB, and UWRB, with higher dynamic change before 2000. The conversion of other land-use types to built-up land was concentrated in the UWRB, MWRB, and DWRB, with substantial increases after 2000. Additionally, the driving effects of the influencing factors on LUCC in each sub-basin also exhibited distinct diversity, with the LRB and JRB being influenced by the meteorological and social factors, and the UWRB, MWRB, and DWRB being driven by human activity factors. Moreover, the interaction of these influencing factors indicated an enhanced effect on LUCC. This study confirmed the spatiotemporal heterogeneity effects of socioeconomic status on LUCC in the WRB under regional differences, contributing to the sustainable development of the whole basin by managing sub-basins according to local conditions.
WU Jingyan, LUO Jungang, ZHANG Han, YU Mengjie. Driving forces behind the spatiotemporal heterogeneity of land-use and land-cover change: A case study of the Weihe River Basin, China. Journal of Arid Land, 2023, 15(3): 253-273.
Fig. 1Geographical location of the Weihe River Basin (WRB) in the Yellow River Basin (a) and overview of the WRB and its sub-basins (b). UWRB, the upstream of the Weihe River Basin; MWRB, the midstream of the Weihe River Basin; DWRB, the downstream of the Weihe River Basin; JRB, Jinghe River Basin; LRB, Luohe River Basin.
Category
Parameter
Type
Scale/resolution
Period
Data source
Land use
LUCC
Raster
30 m×30 m
1985-2020 (interval of 5 a)
Big Earth Data Science Engineering Program (https://data.casearth.cn)
Meteorological factors
Precipitation
Numeric
Annual
1980-2019
National Meteorological Science Data Center (https://data.cma.cn)
Temperature
Numeric
Annual
1980-2019
Natural factors
Elevation (DEM)
Raster
30 m×30 m
2009
Geospatial Data Cloud (http://www.gscloud.cn)
Slope
Raster
30 m×30 m
2009
Soil
Raster
1000 m×1000 m
2010
Resource and Environment Science and Data Center (https://www.resdc.cn)
Distance to rivers
Vector
1:250,000
2015
Social factors
Distance to national highway
Vector
1:250,000
2015
National Cryosphere Desert Data Center (http://www.ncdc.ac.cn)
Distance to railway
Vector
1:250,000
2015
Distance to provincial highway
Vector
1:250,000
2015
Distance to expressway
Vector
1:250,000
2015
Human activity factors
Population density
Raster
1000 m×1000 m
1985-2015 (interval of 5 a)
Resource and Environment Science and Data Center (https://www.resdc.cn)
Gross domestic product (GDP)
Raster
1000 m×1000 m
1985-2015 (interval of 5 a)
Table 1 Details of the datasets of land-use and land-cover change (LUCC) and its influencing factors
Condition
Interaction
q(x1∩x2)<Min(q(x1), q(x2))
Weaken, nonlinear
Min(q(x1), q(x2))<q(x1∩x2)<Max(q(x1), q(x2))
Weaken
q(x1∩x2)>Max(q(x1), q(x2))
Enhance
q(x1∩x2)=q(x1)+q(x2)
Independent
q(x1∩x2)>q(x1)+q(x2)
Enhance, nonlinear
Table 2 Description about the types of interaction detection
Fig. 2Spatial distribution and area proportions of land-use types in the WRB in 1985 (a), 1990 (b), 1995 (c), 2000 (d), 2005 (e), 2010 (f), 2015 (g), and 2020 (h)
Fig. 3Area proportions of cultivated land (a), forestland (b), grassland (c), built-up land (d), bare land (e), and water body (f) in different sub-basins in the WRB from 1985 to 2020 at an interval of 5 a
Fig. 4Structure chart of the transfers of different land-use types in the WRB in 1985-1990 (a), 1990-1995 (b), 1995-2000 (c), 2000-2005 (d), 2005-2010 (e), 2010-2015 (f), 2015-2020 (g), and 1985-2020 (h)
Period
Bare land
Built-up land
Cultivated land
Forestland
Grassland
Water body
Total
1985-1990
Bare land
195.75
0.26
217.75
0.01
697.08
0.00
1110.85
Built-up land
0.00
1662.52
0.00
0.00
0.00
0.00
1662.52
Cultivated land
123.60
239.19
52,719.58
1658.10
7723.83
12.26
62,476.57
Forestland
0.05
0.72
1057.96
33,275.83
1366.82
8.49
35,709.87
Grassland
174.28
7.21
8313.03
1208.17
23,868.92
0.21
33,571.82
Water body
0.00
0.05
40.90
0.99
0.07
31.52
73.52
Total
493.67
1909.94
62,349.22
36,143.11
33,656.72
52.48
134,605.14
Period
Bare land
Built-up land
Cultivated land
Forestland
Grassland
Water body
Total
1990-1995
Bare land
222.72
0.22
80.13
0.00
190.44
0.16
493.67
Built-up land
0.00
1909.94
0.00
0.00
0.00
0.00
1909.94
Cultivated land
130.40
141.41
55,346.24
780.44
5858.73
91.99
62,349.22
Forestland
0.00
0.03
1362.27
33,410.33
1367.65
2.83
36,143.11
Grassland
561.60
10.52
6211.72
808.50
26,063.58
0.80
33,656.72
Water body
0.00
0.07
6.14
0.73
1.15
44.40
52.48
Total
914.72
2062.18
63,006.50
35,000.01
33,481.56
140.17
134,605.14
Period
Bare land
Built-up land
Cultivated land
Forestland
Grassland
Water body
Total
1995-2000
Bare land
321.44
0.65
217.20
3.69
371.01
0.74
914.72
Built-up land
0.00
2062.18
0.00
0.00
0.00
0.00
2062.18
Cultivated land
429.78
356.81
50,557.45
3187.17
8430.70
44.59
63,006.50
Forestland
1.84
0.16
1448.02
31,426.82
2121.59
1.58
35,000.01
Grassland
843.62
18.22
9856.08
2349.17
20,405.21
9.27
33,481.56
Water body
4.38
0.80
76.55
3.09
8.42
46.93
140.17
Total
1601.06
2438.83
62,155.29
36,969.94
31,336.93
103.11
134,605.14
Period
Bare land
Built-up land
Cultivated land
Forestland
Grassland
Water body
Total
2000-2005
Bare land
764.48
3.56
330.92
6.68
495.06
0.36
1601.06
Built-up land
0.00
2438.83
0.00
0.00
0.00
0.00
2438.83
Cultivated land
40.82
543.03
60,262.11
305.95
991.16
12.23
62,155.29
Forestland
0.02
2.97
28.63
36,920.91
17.18
0.23
36,969.94
Grassland
35.69
27.33
780.64
214.70
30,277.49
1.07
31,336.93
Water body
0.10
0.03
2.00
0.16
0.25
100.57
103.11
Total
841.11
3015.74
61,404.30
37,448.40
31,781.14
114.46
134,605.14
Period
Bare land
Built-up land
Cultivated land
Forestland
Grassland
Water body
Total
2005-2010
Bare land
720.35
3.50
56.27
1.02
59.91
0.07
841.11
Built-up land
0.00
3015.74
0.00
0.00
0.00
0.00
3015.74
Cultivated land
31.09
284.17
60,336.69
195.52
542.35
14.47
61,404.30
Forestland
0.03
1.05
15.18
37,424.47
7.10
0.58
37,448.40
Grassland
80.09
19.26
511.56
218.02
30,951.17
1.03
31,781.14
Water body
0.24
0.05
4.49
0.19
0.50
108.99
114.46
Total
831.80
3323.76
60,924.19
37,839.24
31,561.02
125.14
134,605.14
Period
Bare land
Built-up land
Cultivated land
Forestland
Grassland
Water body
Total
2010-2015
Bare land
546.55
3.90
116.26
0.54
164.41
0.13
831.80
Built-up land
0.00
3323.76
0.00
0.00
0.00
0.00
3323.76
Cultivated land
20.10
353.17
59,947.23
119.64
466.57
17.47
60,924.19
Forestland
0.13
1.32
23.55
37,802.43
11.45
0.37
37,839.24
Grassland
21.10
19.96
473.44
146.40
30,898.60
1.52
31,561.02
Water body
0.28
0.02
3.28
0.21
0.35
120.99
125.14
Total
588.16
3702.14
60,563.76
38,069.22
31,541.38
140.48
134,605.14
Period
Bare land
Built-up land
Cultivated land
Forestland
Grassland
Water body
Total
2015-2020
Bare land
503.83
7.02
45.48
0.26
31.46
0.10
588.16
Built-up land
0.00
3702.14
0.00
0.00
0.00
0.00
3702.14
Cultivated land
26.07
1025.93
58,830.17
177.73
487.87
15.99
60,563.76
Forestland
0.26
8.12
58.83
37,973.94
26.73
1.35
38,069.22
Grassland
42.12
51.44
735.70
151.70
30,557.59
2.84
31,541.38
Water body
1.69
1.59
3.34
0.17
0.37
133.32
140.48
Total
573.97
4796.25
59,673.51
38,303.79
31,104.03
153.60
134,605.14
Period
Bare land
Built-up land
Cultivated land
Forestland
Grassland
Water body
Total
1985-2020
Bare land
141.64
2.45
477.52
9.95
467.65
11.63
1110.85
Built-up land
0.00
1662.52
0.00
0.00
0.00
0.00
1662.52
Cultivated land
177.51
3014.65
45,972.82
4013.84
9212.04
85.70
62,476.57
Forestland
2.09
21.35
1546.09
31,390.12
2739.03
11.19
35,709.87
Grassland
249.99
89.12
11,650.04
2889.12
18,684.02
9.54
33,571.82
Water body
2.73
6.16
27.04
0.75
1.28
35.55
73.52
Total
573.97
4796.25
59,673.51
38,303.79
31,104.03
153.60
134,605.14
Table S1 Transfer matrix of land-use and land-cover change (LUCC) in different periods (unit: km2)
Fig. 5Spatial distribution of other land-use types converted to cultivated land (a1-a8) and forestland (b1-b8) in the WRB in 1985-1990 (a1 and b1), 1990-1995 (a2 and b2), 1995-2000 (a3 and b3), 2000-20005 (a4 and b4), 2005-2010 (a5 and b5), 2010-2015 (a6 and b6), 2015-2020 (a7 and b7), and 1985-2020 (a8 and b8)
Fig. 6Spatial distribution of other land-use types converted to grassland (a1-a8) and built-up land (b1-b8) in the WRB in 1985-1990 (a1 and b1), 1990-1995 (a2 and b2), 1995-2000 (a3 and b3), 2000-20005 (a4 and b4), 2005-2010 (a5 and b5), 2010-2015 (a6 and b6), 2015-2020 (a7 and b7), and 1985-2020 (a8 and b8)
Fig. 7Single land-use dynamic index (including relative land-use dynamic index and absolute land-use dynamic index) values of land-use types in the WRB in 1985-1990 (a), 1990-1995 (b), 1995-2000 (c), 2000-2005 (d), 2005-2010 (e), 2010-2015 (f), 2015-2020 (g), and 1985-2020 (h). Note that values of relative or absolute land-use dynamic index less than zero represent decrease in the area of land-use type, and values of relative or absolute land-use dynamic index greater than zero represent increase in the area of land-use type.
1985-1990
1990-1995
1995-2000
2000-2005
2005-2010
2010-2015
2015-2020
Comprehensive land-use dynamic index (%)
0.20
0.40
0.90
0.40
0.20
0.18
0.39
Table 3 Comprehensive land-use dynamic index in the Weihe River Basin (WRB) in 1985-2020 at an inverval of 5 a
Fig. 8Variations of q values for different influencing factors of LUCC in the WRB and its sub-basins from 1985 to 2020. (a), LRB; (b), JRB; (c), UWRB; (d), MWRB; (e), DWRB; (f), WRB. q, the value of driving forces of the influencing factor; Tem, temperature; Pre, precipitation; DEM, elevation; DISTri, distance to rivers; DISTnh, distance to national highway; DISTph, distance to provincial highway; DISTex, distance to expressway; DISTra, distance to railway; Pop, population density; GDP, gross domestic product.
Fig. 9Average q value of each influencing factor of LUCC in the WRB and its sub-basins. (a), LRB; (b), JRB; (c), UWRB; (d), MWRB; (e), DWRB; (f), WRB. q, the value of driving forces of the influencing factor; Tem, temperature; Pre, precipitation; DEM, elevation; DISTri, distance to rivers; DISTnh, distance to national highway; DISTph, distance to provincial highway; DISTex, distance to expressway; DISTra, distance to railway; Pop, population density; GDP, gross domestic product.
Fig. 10Heat map of the 5-a average interaction of influencing factors of LUCC in the WRB and its sub-basins from 1985 to 2020. (a), LRB; (b), JRB; (c), UWRB; (d), MWRB; (e), DWRB; (f), WRB. Tem, temperature; Pre, precipitation; DEM, elevation; DISTri, distance to rivers; DISTnh, distance to national highway; DISTph, distance to provincial highway; DISTex, distance to expressway; DISTra, distance to railway; Pop, population density; GDP, gross domestic product.
Fig. 11Proportion of the average q value of each influencing factor of LUCC in each sub-basin of the WRB. q, the value of driving forces of the influencing factor; Tem, temperature; Pre, precipitation; DEM, elevation; DISTri, distance to rivers; DISTnh, distance to national highway; DISTph, distance to provincial highway; DISTex, distance to expressway; DISTra, distance to railway; Pop, population density; GDP, gross domestic product.
Batunacun, Nendel C, Hu Y F, et al. 2018. Land-use change and land degradation on the Mongolian Plateau from 1975 to 2015—A case study from Xilingol, China. Land Degradation & Development, 29(6): 1595-1606.
doi: 10.1002/ldr.v29.6
[3]
Chen M X, Li Y, Gong Y H, et al. 2016. The population distribution and trend of urbanization pattern on two sides of Hu Huanyong population line: A tentative response to Premier Li Keqiang. Acta Geographica Sinica, 71(2): 179-193. (in Chinese)
doi: 10.11821/dlxb201602001
[4]
Du J, Shi C X. 2012. Effects of climatic factors and human activities on runoff of the Weihe River in recent decades. Quaternary International, 282: 58-65.
doi: 10.1016/j.quaint.2012.06.036
[5]
Guo B, Wang Y, Pei L, et al. 2021. Determining the effects of socioeconomic and environmental determinants on chronic obstructive pulmonary disease (COPD) mortality using geographically and temporally weighted regression model across Xi'an during 2014-2016. Science of The Total Environment, 756: 143869, doi: 10.1016/j.scitotenv.2020.143869.
doi: 10.1016/j.scitotenv.2020.143869
[6]
Hasselmann F, Csaplovics E, Falconer I, et al. 2010. Technological driving forces of LUCC: Conceptualization, quantification, and the example of urban power distribution networks. Land Use Policy, 27(2): 628-637.
doi: 10.1016/j.landusepol.2009.08.016
Jian S Q, Zhao C Y, Fang S M, et al. 2015. Effects of different vegetation restoration on soil water storage and water balance in the Chinese Loess Plateau. Agricultural and Forest Meteorology, 206: 85-96.
doi: 10.1016/j.agrformet.2015.03.009
[9]
Jiang L, Yu J, Wen J H, et al. 2021. Risk assessment of extreme flood in the north bank of the Hangzhou Bay under land use change scenarios. Progress in Geography, 40(8): 1355-1370. (in Chinese)
doi: 10.18306/dlkxjz.2021.08.009
[10]
Kindu M, Angelova D, Schneider T, et al. 2020. Monitoring of urban growth patterns in rapidly growing Bahir Dar City of northwest Ethiopia with 30 year Landsat imagery record. ISPRS International Journal of Geo-Information, 9(9): 548, doi: 10.3390/ijgi9090548.
doi: 10.3390/ijgi9090548
Ma Y L, Xu R S. 2010. Remote sensing monitoring and driving force analysis of urban expansion in Guangzhou City, China. Habitat International, 34(2): 228-235.
doi: 10.1016/j.habitatint.2009.09.007
[13]
Nhi P T T, Khoi D N, Trang N T T, et al. 2022. Hydrological impacts of future climate and land use/cover changes in the Lower Mekong Basin: a case study of the Srepok River Basin, Vietnam. Environmental Monitoring and Assessment, 194(Suppl 2): 768, doi: 10.1007/s10661-022-10175-9.
doi: 10.1007/s10661-022-10175-9
[14]
Nie T, Dong G T, Jiang X H, et al. 2021. Spatio-temporal changes and driving forces of vegetation coverage on the Loess Plateau of northern Shaanxi. Remote Sensing, 13(4): 613, doi: 10.3390/rs13040613.
doi: 10.3390/rs13040613
[15]
Ning J, Liu J Y, Kuang W H, et al. 2018. Spatiotemporal patterns and characteristics of land-use change in China during 2010-2015. Journal of Geographical Sciences, 28(5): 547-562.
doi: 10.1007/s11442-018-1490-0
[16]
Omer A, Ma Z G, Yuan X, et al. 2021. A hydrological perspective on drought risk-assessment in the Yellow River Basin under future anthropogenic activities. Journal of Environmental Management, 289: 112429, doi: 10.1016/j.jenvman.2021.112429.
doi: 10.1016/j.jenvman.2021.112429
[17]
Shaharum N S N, Shafri H Z M, Gambo J, et al. 2018. Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms. Remote Sensing Applications: Society and Environment, 10: 24-35.
doi: 10.1016/j.rsase.2018.01.002
[18]
Song F, Su F L, Mi C X, et al. 2021. Analysis of driving forces on wetland ecosystem services value change: A case in Northeast China. Science of The Total Environment, 751: 141778, doi: 10.1016/j.scitotenv.2020.141778.
doi: 10.1016/j.scitotenv.2020.141778
[19]
Song Y Z, Wang J F, Ge Y, et al. 2020. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data. GIScience & Remote Sensing, 57(5): 593-610.
[20]
Sun X, Lu Z M, Li F, et al. 2018. Analyzing spatio-temporal changes and trade-offs to support the supply of multiple ecosystem services in Beijing, China. Ecological Indicators, 94: 117-129.
doi: 10.1016/j.ecolind.2018.06.049
Wang J F, Li X H, Christakos G, et al. 2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. International Journal of Geographical Information Science, 24(1): 107-127.
doi: 10.1080/13658810802443457
[23]
Wang J T, Peng J, Zhao M Y, et al. 2017. Significant trade-off for the impact of Grain-for-Green Programme on ecosystem services in North-western Yunnan, China. Science of The Total Environment, 574: 57-64.
doi: 10.1016/j.scitotenv.2016.09.026
[24]
Wei H, Xiong L Y, Tang G A, et al. 2021. Spatial-temporal variation of land use and land cover change in the glacial affected area of the Tianshan Mountains. CATENA, 202: 105256, doi: 10.1016/j.catena.2021.105256.
doi: 10.1016/j.catena.2021.105256
[25]
Wei X D, Lin L G, Luo P P, et al. 2022. Spatiotemporal pattern and driving force analysis of multi-functional coupling coordinated development of cultivated land. Transactions of the Chinese Society of Agricultural Engineering, 38(4): 260-269. (in Chinese)
[26]
Wu J Y, Luo J G, Zhang H, et al. 2022a. Projections of land use change and habitat quality assessment by coupling climate change and development patterns. Science of The Total Environment, 847: 157491, doi: 10.1016/j.scitotenv.2022.157491.
doi: 10.1016/j.scitotenv.2022.157491
[27]
Wu J Y, Luo J G, Du X Z, et al. 2022b. Optimizing water allocation in an inter-basin water diversion project with equity-efficiency tradeoff: A bi-level multiobjective programming model under uncertainty. Journal of Cleaner Production, 371: 133606, doi: 10.1016/j.jclepro.2022.133606.
doi: 10.1016/j.jclepro.2022.133606
[28]
Wu R, Li Z G, Wang S J, 2021. The varying driving forces of urban land expansion in China: Insights from a spatial-temporal analysis. Science of The Total Environment, 766: 142591, doi: 10.1016/j.scitotenv.2020.142591.
doi: 10.1016/j.scitotenv.2020.142591
[29]
Wulder M A, White J C, Goward S N, et al. 2008. Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sensing of Environment, 112(3): 955-969.
doi: 10.1016/j.rse.2007.07.004
[30]
Xu C, Jiang Y N, Su Z H, et al. 2022. Assessing the impacts of Grain-for-Green Programme on ecosystem services in Jinghe River basin, China. Ecological Indicators, 137: 108757, doi: 10.1016/j.ecolind.2022.108757.
doi: 10.1016/j.ecolind.2022.108757
[31]
Yang C, Wei T X, Li Y R, 2022. Simulation and spatio-temporal variation characteristics of LULC in the context of urbanization construction and ecological restoration in the Yellow River Basin. Sustainability, 14(2): 789, doi: 10.3390/su14020789.
doi: 10.3390/su14020789
[32]
Yang Y Y. 2021. Evolution of habitat quality and association with land-use changes in mountainous areas: A case study of the Taihang Mountains in Hebei Province, China. Ecological Indicators, 129: 107967, doi: 10.1016/j.ecolind.2021.107967.
doi: 10.1016/j.ecolind.2021.107967
[33]
Yuan Z F, Zhou L, Sun D Q, et al. 2022. Impacts of urban expansion on the loss and fragmentation of cropland in the major grain production areas of China. Land, 11(1): 130, doi: 10.3390/land11010130.
doi: 10.3390/land11010130
[34]
Zhan Q Q, Zhao W, Yang M J, et al. 2021. A long-term record (1995-2019) of the dynamics of land desertification in the middle reaches of Yarlung Zangbo River basin derived from Landsat data. Geography and Sustainability, 2(1): 12-21.
doi: 10.1016/j.geosus.2021.01.002
[35]
Zhang X, Liu L Y, Chen X D, et al. 2021. GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth System Science Data, 13(6): 2753-2776.
doi: 10.5194/essd-13-2753-2021
[36]
Zhou J, Fu B J, Gao G Y, et al. 2016. Effects of precipitation and restoration vegetation on soil erosion in a semi-arid environment in the Loess Plateau, China. CATENA, 137: 1-11.
doi: 10.1016/j.catena.2015.08.015
[37]
Zhou L, Dang X W, Sun Q K, et al. 2020. Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Sustainable Cities and Society, 55: 102045, doi: 10.1016/j.scs.2020.102045.
doi: 10.1016/j.scs.2020.102045
