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Journal of Arid Land  2026, Vol. 18 Issue (1): 17-33    DOI: 10.1016/j.jaridl.2026.01.005     CSTR: 32276.14.JAL.20250315
Research article     
Projection and reclassification of land use types in Lanzhou, Northwest China
ZHU Rong1,2, JIANG Youyan3,*(), LEI Runzhi3
1Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
2Gansu Institute of Architectural Design and Research, Lanzhou 730020, China
3Lanzhou Regional Climate Center, Lanzhou 730020, China
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Abstract  

Land use in arid and semi-arid regions has a substantial effect on climate, environment, and biodiversity, thereby projecting the spatiotemporal changes in land use and the subsequent effects. This study employed the locally calibrated Future Land Use Simulation (FLUS) model, which coupled system dynamics with cellular automata and integrated an artificial neural network algorithm and a roulette wheel selection mechanism. We projected future land use (2020-2100) dynamics of Lanzhou, a typical river valley city in Northwest China, under three different Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The simulation results were validated and subsequently reclassified using the International Geosphere Biosphere Programme (IGBP) system to produce a dataset suitable for driving climatic and environmental models. Under the SSP1-2.6 scenario, urban and built-up land expanded consistently, whereas irrigated cropland and pasture as well as grassland contracted continuously. Conversely, the SSP5-8.5 scenario was characterized by a contraction of urban and built-up land, and relative stability of irrigated cropland and pasture as well as grassland. The SSP2-4.5 scenario presented a more complex trade-off, where urban and built-up land and grassland increased first and then decreased, whereas irrigated cropland and pasture followed an opposite trajectory. A significant inverse relationship between urban and built-up land and irrigated cropland and pasture was observed under all scenarios, underscoring the fundamental spatial competition that prevailed in this land-constrained valley city. Furthermore, the negative correlation of grassland with urban and built-up land, coupled with the positive correlation of grassland with irrigated cropland and pasture under both the SSP1-2.6 and SSP5-8.5 scenarios, indicated an evolution from broad confrontation to intricate internal trade-offs within the urban-agricultural-ecological system. This study underscored the critical influence of regional topographic and hydrological constraints on land-use evolution in arid regions, providing guidance for water resource management and ecosystem protection in Lanzhou, with applications for sustainable land-use planning in other arid and semi-arid river valley cities.



Key wordsland use changes      Future Land Use Simulation (FLUS) model      International Geosphere Biosphere Programme (IGBP)      Shared Socioeconomic Pathways (SSPs)      arid and semi-arid regions      Northwest China     
Received: 15 July 2025      Published: 31 January 2026
Corresponding Authors: *JIANG Youyan (E-mail: jiangyouyan1981@163.com)
Cite this article:

ZHU Rong, JIANG Youyan, LEI Runzhi. Projection and reclassification of land use types in Lanzhou, Northwest China. Journal of Arid Land, 2026, 18(1): 17-33.

URL:

http://jal.xjegi.com/10.1016/j.jaridl.2026.01.005     OR     http://jal.xjegi.com/Y2026/V18/I1/17

Fig. 1 Location of Lanzhou City and overview of Lanzhou based on DEM (digital elevation model)
Data name Year Resolution Source
GDP 2020 1 km Resource and Environmental Science Data Platform (https://www.resdc.cn)
Population 2020 1 km Resource and Environmental Science Data Platform (https://www.resdc.cn)
DEM 2015 30 m Geospatial Data Cloud (https://www.gscloud.cn)
Slope 2020 1 km Calculated based on DEM
Aspect 2020 1 km Calculated based on DEM
Distance from railway 2020 1 km OSM road data (https://www.openstreetmap.org/) processed based on Euclidean distance
Distance from first-order stream 2020 1 km OSM road data (https://www.openstreetmap.org/) processed based on Euclidean distance
Distance from downtown 2020 1 km OSM road data (https://www.openstreetmap.org/) processed based on Euclidean distance
Distance from highway 2020 1 km OSM road data (https://www.openstreetmap.org/) processed based on Euclidean distance
Distance from provincial road 2020 1 km OSM road data (https://www.openstreetmap.org/) processed based on Euclidean distance
Precipitation 2020 1 km Resource and Environmental Science Data Platform (https://www.resdc.cn)
Air temperature 2020 1 km Resource and Environmental Science Data Platform (https://www.resdc.cn)
Table 1 Description of the driving factors of land use changes in Lanzhou
Fig. 2 Technology workflow used in this study. LUH2, Land Use Harmonization 2; IGBP, International Geosphere Biosphere Programme; FLUS, Future Land Use Simulation; SSP, Shared Socioeconomic Pathway.
Initial land use type in LUH2 Reclassification of land use type LUCC classification code
C3 annual crops Farmland 1
C3 perennial crops
C4 annual crops
C4 perennial crops
C3 nitrogen-fixing crops
Primary forest Forest 2
Potential secondary forest
Anthropogenically managed rangeland Grassland 3
Rangeland
Urban land Urban land 5
Non-forested primary land Bare land 6
Potentially non-forested secondary land
Table 2 Reclassification of land use types in Lanzhou
Land use type IGBP land use type classification code Land use change classification code
Urban and built-up land 1 51, 53, 54
Dry cropland and pasture 2
Irrigated cropland and pasture 3 11, 12, 52
Mixed dry/irrigated cropland and pasture 4
Cropland/grassland mosaic 5
Cropland/woodland mosaic 6
Grassland 7 31, 32, 33, 34
Shrubland 8 23, 22
Mixed shrubland/grassland 9 24, 25
Savannah 10
Deciduous broadleaf forest 11
Deciduous needleleaf forest 12
Evergreen broadleaf forest 13
Evergreen needleleaf forest 14 21
Mixed forest 15
Water bodies 16 41, 42, 43, 46, 99
Herbaceous wetland 17 45, 64
Wooden wetland 18
Barren or sparsely vegetated land 19 61, 62, 63, 65, 67
Herbaceous tundra 20
Wooded tundra 21
Mixed tundra 22
Bare ground tundra 23 66
Snow or ice 24 44
Table 3 Mapping relationship between IGBP and LUCC classification in Lanzhou
Fig. 3 Land use distribution patterns in Lanzhou. (a), actual land use in 2015; (b), actual land use in 2020; (c), simulated land use in 2020 using the FLUS model.
2020 2015
Urban and built-up land Irrigated cropland and pasture Grassland Shrubland Mixed shrubland/grassland Evergreen needleleaf forest Water bodies Barren or sparsely vegetated land Snow or ice
Urban and built-up land 267 159 121 5 13 5 6 19 0
Irrigated cropland and pasture 32 1939 1566 55 27 16 32 15 0
Grassland 13 1590 5913 196 9 66 9 34 1
Shrubland 0 65 170 179 2 29 0 0 5
Mixed shrubland/grassland 2 31 7 0 18 0 3 0 0
Evergreen needleleaf forest 2 24 112 56 0 232 1 0 0
Water bodies 12 39 10 1 2 0 9 1 0
Barren or sparsely vegetated land 0 22 31 3 0 0 0 20 0
Snow or ice 0 0 2 2 0 0 0 0 2
Table 4 Transformation matrix of land use types between 2015 and 2020 (unit: km2)
Table 5 Estimated areas of various land use types in Lanzhou in the future under different SSP scenarios
Fig. 4 Land use demand in Lanzhou in the future under different SSP scenarios
Fig. 5 Spatial patterns of land use types in Lanzhou in 2030 under different SSP scenarios. (a), SSP1-2.6; (b), SSP2-4.5; (c), SSP5-8.5.
Fig. 6 Spatial patterns of land use types in Lanzhou in 2050 under different SSP scenarios. (a), SSP1-2.6; (b), SSP2-4.5; (c), SSP5-8.5.
Fig. 7 Spatial patterns of land use types in Lanzhou in 2100 under different SSP scenarios. (a), SSP1-2.6; (b), SSP2-4.5; (c), SSP5-8.5.
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