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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (12): 1649-1668    DOI: 10.1007/s40333-025-0067-x     CSTR: 32276.14.JAL.0250067x
Research article     
Carbon pattern driven by land use/land cover in mountain-desert-oasis complex system
XU Aokang1, SHI Jing2,*(), SUN Zhichang3, MENG Xiangyun4
1Department of Geology, Northwest University, Xi'an 710069, China
2College of Ecology, Lanzhou University, Lanzhou 730000, China
3School of Economics and Management, Beihang University, Beijing 100191, China
4College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
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Abstract  

Optimizing the spatial pattern of carbon sequestration service is essential for advancing regional low-carbon development, accelerating the achievement of the "dual carbon" goals, and promoting the high-quality development of ecological environment. The carbon sequestration capacity within the mountain-desert-oasis system (MDOS), a unique landscape pattern, exhibits significant gradient characteristics, and its carbon sink potential can be substantially improved through multi-scale spatial optimization. This study employed the Integrated Valuation of Ecosystem Services and Tradeoff (InVEST) model to estimate carbon storage and sequestration (CSS) in the Gansu section of Heihe River Basin, China, a representative MDOS, based on land use/land cover (LULC) data from 1990 to 2020. The Patch-level Land Use Simulation (PLUS) model was coupled to simulate LULC and estimate carrying CSS under natural development (ND), ecological protection (EP), water constraint (WC), and economic development (ED) scenarios for 2035. Furthermore, the study constructed and optimized the CSS pattern on the basis of economic and ecological benefits, exploring the guiding significance of different scenarios for pattern optimization. The results showed that CSS spatial distribution is closely correlated with LULC pattern, and CSS is expected to improve in the future. CSS showed an overall increase across subsystems during 1990-2020, but varied across LULC types. CSS of construction land in all subsystems exhibited an increasing trend, while CSS of unused land showed a decreasing trend, with specific changes of 1.68×103 and 3.43×105 t, respectively. Regional CSS dynamics were mainly driven by conversions among unused land, cultivated land, and grassland. The CSS pattern of MDOS was divided into carbon sink functional region (CSFR), low carbon conservation region (LCCR), low carbon economic region (LCER), and economic development region (EDR). Water resources coordination served as the basis of pattern optimization, while the four dimensions—ecological carbon sink, low-carbon maintenance, agricultural carbon reduction and sink enhancement, and urban carbon emission reduction—framed the optimization framework. ND, EP, WC, and ED scenarios provided guidance as the basic reference, optimal benefit, "dual carbon" baseline, and upper development limit, respectively. Additionally, the detailed CSS sub-partitions of MDOS covered most potential scenarios of such ecosystems, demonstrating the applicability of these sub-partitions. These findings provide valuable references for enhancing CSS and hold important significance for low-carbon territorial spatial planning in the MDOS.

Key words： carbon storage and sequestration (CSS)      carbon sequestration      land use/land cover (LULC)      future scenarios      mountain-desert-oasis system (MDOS)      Heihe River Basin     
Received: 30 March 2025      Published: 31 December 2025
Corresponding Authors: *SHI Jing (E-mail: shijingxhj@163.com)
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XU Aokang
SHI Jing
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MENG Xiangyun
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XU Aokang, SHI Jing, SUN Zhichang, MENG Xiangyun. Carbon pattern driven by land use/land cover in mountain-desert-oasis complex system. Journal of Arid Land, 2025, 17(12): 1649-1668.

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http://jal.xjegi.com/10.1007/s40333-025-0067-x     OR     http://jal.xjegi.com/Y2025/V17/I12/1649

Fig. 1 Overview of the Gansu section of Heihe River Basin based on the satellite images. Satellite images are sourced from Geospatial Data Cloud (https://www.gscloud.cn). The administrative boundary is based on the standard map (GS(2023)2767) of the Map Service System (https://bzdt.ch.mnr.gov.cn/), and the standard map has not been modified.
Data type Data name Time span Resolution Data source
Land use Land use/land cover (LULC) 1990, 2000, 2010,
and 2020		 30 m Resource and Environmental Science Data Platform
(http://www.resdc.cn)
Natural condition Digital elevation model (DEM) 2020 30 m Geospatial Data Cloud
(http://www.gscloud.cn)
Slope 2020 30 m Extraction based on DEM data
Temperature 2020 1000 m Resource and Environmental Science Data Platform
(http://www.resdc.cn)
Precipitation
Wind erosion
Socio-economic condition Gross domestic product (GDP) 2020 1000 m Resource and Environmental Science Data Platform
(http://www.resdc.cn)
Population
Terrestrial ecosystem types
Location factor Distance from settlements 2020 30 m Extraction based on DEM data
Distance from water
Distance from roads 2020 30 m Resource and Environmental Science Data Platform
(http://www.resdc.cn)
Table 1 Data types and sources
Fig. 2 Research framework of this study. LULC, land use/land cover; DEM, digital elevation model; GDP, gross domestic product; MDOS, mountain-desert-oasis system; PLUS, Patch-level Land Use Simulation; LEAS, land expansion analysis strategy; CARS, CA model based on the multi-type random seed; ND, natural development; EP, ecological protection; WC, water constraint; ED, economic development; CSS, carbon storage and sequestration; InVEST, Integrated Valuation of Ecosystem Services and Tradeoff; ECC, economic contribution coefficient; ESC, ecological support coefficient; CSFR, carbon sink functional region; LCCR, low carbon conservation region; LCER, low carbon economic region; EDR, economic development region.
LULC type Carbon density (t/hm2)
Cabove Cbelow Csoil Cdead
Cultivated land 1.21 16.78 85.66 14.06
Woodland 12.19 24.13 164.39 14.06
Grassland 7.35 17.99 132.84 2.16
Water body 0.66 0.00 0.00 0.00
Construction land 0.55 0.00 0.00 0.00
Unused land 0.22 0.00 23.47 0.00
Table 2 Carbon density data adopted in this study
Fig. 3 Spatial distributions of the driving factors. (a), DEM; (b), slope; (c), temperature; (d), precipitation; (e), wind erosion; (f), GDP; (g), population; (h), distance from roads; (i), distance from water; (j), distance from settlements.
Fig. 4 Spatial distributions of simulated LULC (a1-a5) and actual LULC (b1-b5) in 2020 in the whole basin and four partial regions
Partition type Partition criteria Characteristics
Carbon sink functional region (CSFR) ECC<1 and ESC>1 Strong carbon sequestration capacity, rich carbon sink resources, and low economic efficiency
Low carbon conservation region (LCCR) ECC<1 and ESC<1 Weak carbon effect and low economic efficiency
Low carbon economic region (LCER) ECC>1 and ESC>1 Relatively low carbon sequestration capacity and relatively high economic efficiency, with a green development basis
Economic development region (EDR) ECC>1 and ESC<1 High economic efficiency and carbon emission and low carbon sequestration capacity
Table 3 CSS partitions based on carbon emission economic contribution coefficient (ECC) and ecological support coefficient (ESC)
Fig. 5 Spatial patterns of the mountain (a), desert (b), and oasis (c) systems in the Gansu section of Heihe River Basin. Ring represents the LULC proportion in each subsystem.
Fig. 6 Spatial distributions of LULC in 1990 (a1-a4), 2000 (b1-b4), 2010 (c1-c4), and 2020 (d1-d4), as well as the LULC simulations in 2035 under the ND (e1-e4), EP (f1-f4), WC (g1-g4), and ED (h1-h4) scenarios in the whole basin and three partial regions. Ring represents the LULC proportion.
Fig. 7 Spatial distributions of CSS patterns from 1990 to 2020 (a-d) and CSS dynamics during periods of 1990-2000 (e), 2000-2010 (f), 2010-2020 (g), and 1990-2020 (h), as well as the CSS simulations in 2035 under the ND (i), EP (j), WC (k), and ED (l) scenarios and CSS dynamics during 2020-2035 under the four scenarios (m-p)
Fig. 8 CSS dynamics of each LULC type in the mountain (a1-a6), desert (b1-b6), and oasis (c1-c6) systems in different years and under different scenarios. The line graph indicates the trend of CSS from 1990 to 2020.
Fig. 9 Spatial distributions of carbon emission ECC (a1) and ESC (a2) and CSS partitions (a3) of MDOS and the reclassification of CSS pattern in the mountain (b1-b4), desert (c1-c4), and oasis (d1-d4) systems. M-CSFR, mountain carbon sink functional region; M-SCSFR, mountain secondary carbon sink functional region; M-LCCR, mountain low carbon conservation region; D-LCCR, desert low carbon conservation region; O-LCER, oasis low carbon economic region; O-EDR, oasis economic development region. Ring represents the proportion of each CSS sub-partition in the corresponding subsystem. For the reclassification of CSS pattern in each subsystem, three partial regions were selected and compared on the basis of LULC maps.
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