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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (1): 91-109    DOI: 10.1007/s40333-024-0051-x     CSTR: 32276.14.s40333-024-0051-x
Research article     
Spatiotemporal characteristics and driving mechanisms of land use/land cover (LULC) changes in the Jinghe River Basin, China
WANG Yinping1, JIANG Rengui1,*(), YANG Mingxiang2, XIE Jiancang1, ZHAO Yong2, LI Fawen3, LU Xixi4
1State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an 710048, China
2State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
3State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China
4Department of Geography, National University of Singapore, Singapore 117570, Singapore
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Abstract  

Understanding the trajectories and driving mechanisms behind land use/land cover (LULC) changes is essential for effective watershed planning and management. This study quantified the net change, exchange, total change, and transfer rate of LULC in the Jinghe River Basin (JRB), China using LULC data from 2000 to 2020. Through trajectory analysis, knowledge maps, chord diagrams, and standard deviation ellipse method, we examined the spatiotemporal characteristics of LULC changes. We further established an index system encompassing natural factors (digital elevation model (DEM), slope, aspect, and curvature), socio-economic factors (gross domestic product (GDP) and population), and accessibility factors (distance from railways, distance from highways, distance from water, and distance from residents) to investigate the driving mechanisms of LULC changes using factor detector and interaction detector in the geographical detector (Geodetector). The key findings indicate that from 2000 to 2020, the JRB experienced significant LULC changes, particularly for farmland, forest, and grassland. During the study period, LULC change trajectories were categorized into stable, early-stage, late-stage, repeated, and continuous change types. Besides the stable change type, the late-stage change type predominated the LULC change trajectories, comprising 83.31% of the total change area. The period 2010-2020 witnessed more active LULC changes compared to the period 2000-2010. The LULC changes exhibited a discrete spatial expansion trend during 2000-2020, predominantly extending from southeast to northwest of the JRB. Influential driving factors on LULC changes included slope, GDP, and distance from highways. The interaction detection results imply either bilinear or nonlinear enhancement for any two driving factors impacting the LULC changes from 2000 to 2020. This comprehensive understanding of the spatiotemporal characteristics and driving mechanisms of LULC changes offers valuable insights for the planning and sustainable management of LULC in the JRB.

Key words： land use/land cover (LULC) changes      driving mechanisms      trajectory analysis      geographical detector (Geodetector)      Grain for Green Project      Jinghe River Basin     
Received: 15 August 2023      Published: 31 January 2024
Corresponding Authors: *JIANG Rengui (E-mail: jrengui@163.com)
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WANG Yinping
JIANG Rengui
YANG Mingxiang
XIE Jiancang
ZHAO Yong
LI Fawen
LU Xixi
Cite this article:

WANG Yinping, JIANG Rengui, YANG Mingxiang, XIE Jiancang, ZHAO Yong, LI Fawen, LU Xixi. Spatiotemporal characteristics and driving mechanisms of land use/land cover (LULC) changes in the Jinghe River Basin, China. Journal of Arid Land, 2024, 16(1): 91-109.

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http://jal.xjegi.com/10.1007/s40333-024-0051-x     OR     http://jal.xjegi.com/Y2024/V16/I1/91

Fig. 1 Overview of the Jinghe River Basin (JRB) and the spatial distribution of railways, rivers, and highways in the JRB. DEM, digital elevation model. The data on railways, rivers, and highways were sourced from the National Catalogue Service for Geographic Information (https://www.webmap.cn/).
Fig. 2 Spatial distribution of reclassified land use/land cover (LULC) types in 2000 (a), 2010 (b), and 2020 (c) in the JRB
Category Factor Unit Time period Data source
Natural factors DEM m 2019 Geospatial Data Cloud (https://www.gscloud.cn/)
Slope - 2019 Calculated based on DEM
Aspect - 2019
Curvature - 2019
Socio-economic factors GDP 104 CNY/km2 2005 and 2015 Resource and Environment Science and Data Center (https://www.resdc.cn/)
Population 104 persons/km2 2005 and 2015
Accessibility factors Distance from railways km 2015 National Catalogue Service for Geographic Information (https://www.webmap.cn/)
Distance from highways km 2015
Distance from water km 2015
Distance from residents km 2015
Table 1 Driving factors of land use/land cover (LULC) changes selected in the study and their data sources
Fig. 3 Spatial distribution of driving factors including DEM (a), slope (b), aspect (c), and curvature (d) in 2019, and GDP (e), population (f), distance from railways (g), distance from highways (h), distance from water (i), and distance from residents (j) in 2015 in the JRB. GDP, gross domestic product.
Judgement criteria 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)) Bilinear enhancement
q(x1∩x2)=q(x1)+q(x2) Independence
q(x1∩x2)>q(x1)+q(x2) Nonlinear enhancement
Table 2 Judgement criteria and interaction types between any two driving factors on influencing the spatial differentiation of LULC changes
Fig. 4 Knowledge maps of LULC changes in the periods 2000-2010 (a) and 2010-2020 (b) in the JRB. Numbers 1-6 represent farmland, forest, grassland, urban land, water body, and bareland, respectively. The directed arrows signify the conversion directions of LULC changes, the thickness of each line indicates the transfer rate, and the number of lines corresponds to the number of LULC change trajectories.
Fig. 5 Variations in the net change, exchange and total change of each LULC type in the periods 2000-2010, 2010-2020, and 2000-2020 in the JRB
Fig. 6 Chord diagrams showing the LULC change trajectories in the periods 2000-2010 (a) and 2010-2020 (b) in the JRB. In these diagrams, the length of each arc represents the transfer area (both transfer-in area and transfer-out area) of each LULC type. The direction of each arrow indicates the transfer-in direction, while the thickness of each line denotes the magnitude of transfer-in area or transfer-out area.
LULC change type Number of trajectories Area
(km2)		 Area proportion of the total
area (%)		 LULC change trajectory
with the largest area		 Area (km2) Area proportion of the type area (%)
Stable change 6 39471.99 86.96 Farmland-farmland-farmland 19,461.01 49.30
Early-stage change 21 696.31 1.53 Grassland-forest-forest 311.64 44.76
Late-stage change 28 4931.23 10.86 Grassland-grassland-farmland 1213.98 24.62
Repeated change 21 224.26 0.49 Farmland-grassland-farmland 45.60 20.33
Continuous change 63 67.03 0.15 Grassland-forest-farmland 20.64 30.79
Table 3 Number of trajectories, areas, and area proportions of different LULC change types, along with the areas and area proportions of different LULC change trajectories (with the largest area) in the period 2000-2020
Number Change trajectory Change type Area (km2) Transfer rate (%) Cumulative transfer rate (%)
1 Grassland-grassland-farmland Late-stage change 1213.98 20.51 20.51
2 Farmland-farmland-grassland Late-stage change 1152.00 19.46 39.97
3 Forest-forest-grassland Late-stage change 706.26 11.93 51.91
4 Grassland-grassland-forest Late-stage change 655.67 11.08 62.98
5 Farmland-farmland-urban land Late-stage change 573.37 9.69 72.67
6 Grassland-forest-forest Early-stage change 311.64 5.27 77.94
7 Farmland-farmland-forest Late-stage change 267.78 4.52 82.46
8 Grassland-grassland-farmland Late-stage change 196.05 3.31 85.77
9 Farmland-urban land-urban land Early-stage change 102.46 1.73 87.50
10 Water body-farmland-farmland Early-stage change 68.08 1.15 88.65
Table 4 Description of the first ten LULC change trajectories in the period 2000-2020
Fig. 7 Spatial variations in the standard deviation ellipses of LULC changes and their mean centers from the period 2000-2010 to the period 2010-2020 in the JRB. SDEX and SDEY represent the spatial coordinates of the mean center of the standard deviation ellipse along the X and Y axes, respectively.
Study period Long axis length (km) Short axis length (km) Area (km2) Perimeter (km) Azimuth angle (°)
2000-2010 84.28 52.18 13,813.60 434.63 175.41
2010-2020 107.29 71.04 23,942.30 566.03 163.71
Table 5 Parameters of the standard deviation ellipses of LULC changes in the periods 2000-2010 and 2010-2020
Fig. 8 Factor detector results (indicated by the q values) showing the influence of individual driving factors on the spatial differentiation of LULC changes in the JRB during 2000-2010 (a) and 2010-2020 (b). x1 to x10 corresponded to DEM, slope, aspect, curvature, GDP, population, distance from railways, distance from highways, distance from water, and distance from residents, respectively. q is the explanatory power of driving factors on the spatial differentiation of LULC changes.
Fig. 9 Interaction detector results and q values for various driving factors influencing the spatial differentiation of farmland (a), forest (b), grassland (c), urban land (d), and water body (e) changes in the JRB in the period 2000-2010. x1 to x10 denoted DEM, slope, aspect, curvature, GDP, population, distance from railways, distance from highways, distance from water, and distance from residents, respectively. NE, nonlinear enhancement; BE, bilinear enhancement.
Fig. 10 Interaction detector results and q values for various driving factors influencing the spatial differentiation of farmland (a), forest (b), grassland (c), urban land (d), water body (e), and bareland (f) changes in the JRB in the period 2010-2020. x1 to x10 corresponded to DEM, slope, aspect, curvature, GDP, population, distance from railways, distance from highways, distance from water, and distance from residents, respectively. NE, nonlinear enhancement; BE, bilinear enhancement.
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