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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (1): 71-90    DOI: 10.1007/s40333-024-0001-7
Research article     
Land use change and its driving factors in the ecological function area: A case study in the Hedong Region of the Gansu Province, China
WEI Zhudeng(), DU Na, YU Wenzheng
School of Geographical Science, Nanjing University of Information Science & Technology, Nanjing 210044, China
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Abstract  

Land use and cover change (LUCC) is important for the provision of ecosystem services. An increasing number of recent studies link LUCC processes to ecosystem services and human well-being at different scales recently. However, the dynamic of land use and its drivers receive insufficient attention within ecological function areas, particularly in quantifying the dynamic roles of climate change and human activities on land use based on a long time series. This study utilizes geospatial analysis and geographical detectors to examine the temporal dynamics of land use patterns and their underlying drivers in the Hedong Region of the Gansu Province from 1990 to 2020. Results indicated that grassland, cropland, and forestland collectively accounted for approximately 99% of the total land area. Cropland initially increased and then decreased after 2000, while grassland decreased with fluctuations. In contrast, forestland and construction land were continuously expanded, with net growth areas of 6235.2 and 455.9 km2, respectively. From 1990 to 2020, cropland was converted to grassland, and both of them were converted to forestland as a whole. The expansion of construction land primarily originated from cropland. From 2000 to 2005, land use experienced intensified temporal dynamics and a shift of relatively active zones from the central to the southeastern region. Grain yield, economic factors, and precipitation were the major factors accounting for most land use changes. Climatic impacts on land use changes were stronger before 1995, succeeded by the impact of animal husbandry during 1995-2000, followed by the impacts of grain production and gross domestic product (GDP) after 2000. Moreover, agricultural and pastoral activities, coupled with climate change, exhibited stronger enhancement effects after 2000 through their interaction with population and economic factors. These patterns closely correlated with ecological restoration projects in China since 1999. This study implies the importance of synergy between human activity and climate change for optimizing land use via ecological patterns in the ecological function area.

Key words： land use      land type      geographic detector      driving mechanism      Hedong Region     
Received: 16 June 2023      Published: 31 January 2024
Corresponding Authors: *WEI Zhudeng (E-mail: weizhudeng@126.com)
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WEI Zhudeng
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WEI Zhudeng, DU Na, YU Wenzheng. Land use change and its driving factors in the ecological function area: A case study in the Hedong Region of the Gansu Province, China. Journal of Arid Land, 2024, 16(1): 71-90.

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http://jal.xjegi.com/10.1007/s40333-024-0001-7     OR     http://jal.xjegi.com/Y2024/V16/I1/71

Data type Index Code Unit Source
Socioeconomic factor Resident population X1 Person Gansu Provincial Bureau of Statistics, Gansu Economic Information Network, and the statistical yearbooks of cities (prefectures) from 1990 to 2020
Large livestock inventory X2 Sheep
Total GDP X3 CNY
Added value of the primary industry X4 CNY
Added value of the secondary industry X5 CNY
Added value of the tertiary industry X6 CNY
Grain yield X7 kg
Natural factor Annual precipitation X8 mm Meteorological stations observations from National Meteorological Science Data Center of China
Average annual temperature X9 °C
Land use change Change rates of land use area Y km2/a CLCD product published by Yang and Huang (2021)
Table 1 Data type and source of the study area
Fig. 1 Spatial distribution of land use in the Hedong Region from 1990 to 2020. (a), 1990; (b), 2000; (c), 2010; (d), 2020.
Land type 1990 2000 2010 2020
Area
(km2)		 Percentage
(%)		 Area
(km2)		 Percentage
(%)		 Area
(km2)		 Percentage
(%)		 Area
(km2)		 Percentage
(%)
Cropland 43,122.25 24.25 46,236.36 26.00 42,592.18 23.95 40,925.14 23.02
Forest land 31,790.77 17.88 33,567.97 18.88 35,077.61 19.73 38,026.00 21.38
Grassland 101,084.90 56.85 96,650.30 54.35 98,497.77 55.39 96,768.41 54.42
Water body 429.13 0.24 366.39 0.21 372.95 0.21 468.11 0.26
Construction land 325.93 0.18 432.48 0.24 600.95 0.34 781.83 0.44
Bare land 1064.33 0.60 563.77 0.32 675.81 0.38 847.78 0.48
Table 2 Land use area and percentage in the Hedong Region from 1990 to 2020
Fig. 2 Spatial distribution of conversion of land use in the Hedong Region from 1990 to 2020
Land type Cropland Forest land Grassland Water body Bare land Construction land Shrinkage
(km2)
Cropland 29,983.12 1726.70 11,019.61 27.64 32.66 332.53 13,139.13
Forest land 330.91 30,902.50 556.09 0.73 0.00 0.53 888.26
Grassland 10,508.27 5391.43 84,517.35 105.98 462.47 99.37 16,567.52
Water body 13.44 5.28 83.04 312.82 1.83 12.71 116.30
Bare land 88.59 0.08 592.11 15.87 350.78 16.90 713.55
Construction land 0.82 0.00 0.21 5.07 0.04 319.79 6.14
Expansion 10,942.02 7123.50 12,251.06 155.29 496.99 462.04 -
Table 3 Land use transition matrix in the Hedong Region from 1990 to 2020
Fig. 3 Sankey diagram for five-year period changes in land use types in the Hedong Region from 1990 to 2020. Grassland1990 means the grassland area in 1990. (a), 1990-1995; (b), 1995-2000; (c), 2000-2005; (d), 2005-2010; (e), 2010-2015; (f), 2015-2020.
Fig. 4 Dynamic degree index of land use types in the Hedong Region from 1990 to 2020
Fig. 5 Spatial distribution of county-level land use dynamics for different periods from 1990 to 2020. (a), 1990- 1995; (b), 1995-2000; (c), 2000-2005; (d), 2005-2010; (e), 2010-2015; (f), 2015-2020.
Code Cropland P
value		 Forest
land		 P
value		 Grassland P
value		 Water
body		 P
value		 Bare land P
value		 Construction
land		 P
value
X1 0.24 0.20 0.20 0.28 0.15 0.37 0.11 0.63 0.10 0.70 0.11 0.59
X2 0.26 0.20 0.21 0.06 0.29 0.15 0.32 0.01 0.10 0.66 0.23 0.07
X3 0.36 0.01 0.45 0.00 0.40 0.00 0.49 0.00 0.14 0.15 0.35 0.01
X4 0.23 0.02 0.09 0.59 0.20 0.03 0.11 0.59 0.14 0.45 0.30 0.02
X5 0.35 0.00 0.26 0.04 0.24 0.10 0.09 0.73 0.13 0.48 0.36 0.00
X6 0.23 0.07 0.12 0.40 0.25 0.05 0.09 0.75 0.09 0.62 0.45 0.00
X7 0.42 0.00 0.19 0.21 0.58 0.00 0.11 0.47 0.14 0.43 0.20 0.12
X8 0.35 0.00 0.18 0.37 0.46 0.00 0.48 0.01 0.10 0.67 0.12 0.59
X9 0.12 0.44 0.23 0.10 0.10 0.66 0.15 0.29 0.19 0.22 0.18 0.24
Table 4 Explained variance of driving factors for land use change from 1990 to 2020
Fig. 6 Interactive effects of driving factors for land use change from 1990 to 2020. The red dots indicate enhanced effect of bifactor. (a), cropland; (b), forest land; (c), grassland; (d), water body; (e), bare land; (f), construction land. X1-X9 mean nine explanatory variables that are listed in Table 1.
Fig. 7 Single-factor analysis for driving land use change from 1990 to 2020. (a), cropland; (b), forest land; (c), grassland; (d), water body; (e), bare land; (f), construction land. X1-X9 mean nine explanatory variables that are listed in Table 1.
Fig. 8 Interaction detection of driving factors for land use change in different periods from 1990 to 2020. a-f are the changes of the cropland, forest land, grassland, water body, bare land, and construction land in different periods (1990-1995, 1995-2000, 2000-2005, 2005-2010, 2010-2015, and 2015-2020), respectively. X1-X9 mean nine explanatory variables that are listed in Table 1
Land type 1990-1995 1995-2000
X1 X2 X3 X4 X5 X6 X7 X8 X9 X1 X2 X3 X4 X5 X6 X7 X8 X9
Cropland 7 7 6 7 7 7 7 7 5 9 8 6 9 8 8 8 9 7
QU SD QU SD QU QU SD SD SD QU SD QU SD QU QU GI NB SD
Forest land 6 7 5 6 6 7 7 6 7 9 3 8 9 9 9 9 7 8
SD SD QU QU QU QU GI QU GI QU EI QU QU QU QU QU QU QU
Grassland 9 9 9 8 9 8 9 7 9 8 8 6 8 8 8 8 8 7
QU SD QU QU QU QU SD GI NB QU SD QU SD QU QU GI SD SD
Water body 4 9 8 8 9 6 5 9 9 9 8 9 7 8 9 9 8 7
GI QU QU QU QU QU QU NB QU QU SD QU QU QU QU QU EI SD
Bare land 6 7 7 5 7 7 7 7 5 7 7 7 7 6 6 7 6 6
SD SD QU GI QU QU GI NB GI QU QU QU QU SD QU GI SD QU
Construction land 5 7 3 7 5 7 6 7 7 7 9 5 3 8 7 9 6 9
QU SD GI QU QU QU QU SD NB QU QU SD GI NB QU SD GI QU
Land type 2000-2005 2005-2010
X1 X2 X3 X4 X5 X6 X7 X8 X9 X1 X2 X3 X4 X5 X6 X7 X8 X9
Cropland 8 9 8 9 6 5 7 9 9 7 7 7 7 9 7 5 8 7
QU QU QU SD QU QU GI QU QU QU SD QU EI QU QU GI QU SD
Forestland 8 9 7 6 8 3 9 5 9 9 7 9 9 8 9 8 5 9
QU QU GI SD QU GI GI GI SD QU SD QU QU QU QU QU GI QU
Grassland 5 7 7 4 6 5 7 5 7 6 7 7 9 9 9 3 8 7
QU QU GI GI QU QU GI GI QU QU SD QU SD QU QU GI QU SD
Water body 8 7 6 8 7 9 5 5 9 9 7 7 7 9 9 3 9 8
QU QU GI QU QU QU QU GI SD QU SD QU QU QU QU GI QU EI
Bare land 9 4 9 9 9 9 9 9 9 9 9 9 7 9 9 9 8 9
QU GI QU QU QU QU GI EI QU QU QU QU GI QU QU QU QU QU
Construction land 8 7 5 9 7 5 9 6 9 9 8 9 9 7 5 8 8 8
QU SD SD SD QU QU QU EI SD QU SD QU GI GI SD NB QU QU
Land type 2010-2015 2015-2020
X1 X2 X3 X4 X5 X6 X7 X8 X9 X1 X2 X3 X4 X5 X6 X7 X8 X9
Cropland 9 6 9 9 9 8 6 9 8 6 9 6 8 8 6 7 8 8
QU EI QU GI QU QU NB QU QU NB QU QU QU QU NB QU SD QU
Forest land 9 9 9 8 8 9 9 8 8 9 7 9 9 9 9 7 9 7
QU QU QU QU QU QU QU GI EI QU QU QU QU QU QU QU QU EI
Grassland 9 6 9 5 9 8 6 9 8 6 8 9 7 5 6 7 9 8
QU EI QU QU QU QU NB QU QU NB QU QU QU QU NB QU QU SD
Water body 8 6 3 9 9 9 9 8 8 9 8 9 9 9 8 9 9 9
QU EI SD QU QU QU QU SD GI QU SD QU QU QU QU QU QU QU
Bare land 9 6 9 9 7 9 6 9 9 7 8 9 9 7 9 6 8 9
QU QU QU QU GI QU NB QU SD QU SD QU QU QU QU SD GI SD
Construction land 9 7 4 7 7 3 9 9 8 8 8 7 8 8 9 9 9 6
QU QU GI GI GI SD QU EI QU QU QU QU QU QU QU QU QU EI
Land type 1990-2020
X1 X2 X3 X4 X5 X6 X7 X8 X9
Cropland 7 7 9 6 8 8 4 8 8
SD SD QU QU QU QU GI GI QU
Forest land 7 7 8 6 8 8 9 9 9
SD SD QU SD QU QU QU SD QU
Grassland 7 7 7 4 9 8 4 8 9
SD SD QU EI QU QU GI GI QU
Water body 9 3 4 9 9 9 8 9 8
QU GI GI QU QU QU QU EI SD
Bare land 9 9 6 9 9 8 9 9 9
QU QU QU QU QU QU QU QU QU
Construction land 9 8 9 8 8 6 8 9 9
QU QU QU QU QU QU QU EI QU
Table S1 Optimal number of intervals and discretization method for driving factors
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