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Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (3): 452-476    DOI: 10.1016/j.jaridl.2026.03.006     CSTR: 32276.14.JAL.20250289
Research article     
Impact of land use change on carbon storage based on the PLUS-InVEST model: A case study in the urban belt along the Yellow River, China
SHI Hanqi1,2,3, DUAN Huan'e1,2,3,*(), LI Xuemei1,2,3, WANG Guigang1,2,3, CHEN Ahui1,2,3, LIANG Dengrui1,2,3
1Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
3Key Laboratory of Science and Technology in Surveying & Mapping Gansu Province, Lanzhou 730070, China
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Abstract  

Terrestrial ecosystems are vital for maintaining equilibrium in the global carbon cycle. Land use and land cover change (LUCC), which is influenced mainly by urbanization and ecological policies, impacts terrestrial ecosystem carbon storage significantly. In this study, spatiotemporal carbon storage changes in the urban belt along the Yellow River in the Ningxia Hui Autonomous Region, China, were estimated through a model that integrated patch-generating land use simulation (PLUS) and integrated valuation of ecosystem services and tradeoffs (InVEST) models from 1993 to 2033. The results revealed that: (1) from 1993 to 2023, the expansion of built-up land and cropland was derived mainly from unused land and grassland, whereas water body and woodland remained relatively stable. Projections to 2033 have indicated that LUCC will continue and be concentrated primarily in the Ningxia Plain; (2) carbon storage increased by a net 5.01×106 Mg C from 1993 to 2023; (3) the spatial distribution of carbon storage revealed that high-value areas were predominantly located in the Helan Mountains and the Ningxia Plain, whereas low-value areas were found in the Tengger Desert; (4) scenario projections indicated that by 2033, the ecological protection scenario (EPS) would achieve a 0.18×106 Mg C increase by reducing the conversion of woodland to cropland and grassland to built-up land, while increasing the conversion of unused land to grassland. In contrast, the natural development scenario (NDS), cropland protection scenario (CPS), and urban development scenario (UDS) decreased carbon storage by 0.60×106, 0.21×106, and 0.42×106 Mg C, respectively; and (5) spatial autocorrelation analysis revealed that high-high carbon storage clusters formed belt-like patterns along the Ningxia Plain and the Helan Mountains, whereas the low-low carbon storage clusters were concentrated in northern Zhongwei City, western Qingtongxia City, western Dawukou District, and the urbanized areas within the central Ningxia Plain. Overall, the study results revealed the close coupling relationship between LUCC and carbon storage functions. Furthermore, the study establishes a framework for carbon management that balances ecological protection with coordinated urban development for the urban belt as well as for similar arid and semi-arid areas. On the basis of these findings, this study provides decision-makers with guidance to optimize ecosystem carbon storage via land use, which plays a key role in developing future land use policies and achieving the ''dual carbon'' goals.

Key words： carbon storage      land use change      patch-generating land use simulation (PLUS) model      integrated valuation of ecosystem services and tradeoffs (InVEST) model      Moran's I      ecological protection     
Received: 24 June 2025      Published: 31 March 2026
Corresponding Authors: *DUAN Huan'e (E-mail: duanhuane@mail.lzjtu.cn)
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SHI Hanqi
DUAN Huan'e
LI Xuemei
WANG Guigang
CHEN Ahui
LIANG Dengrui
Cite this article:

SHI Hanqi, DUAN Huan'e, LI Xuemei, WANG Guigang, CHEN Ahui, LIANG Dengrui. Impact of land use change on carbon storage based on the PLUS-InVEST model: A case study in the urban belt along the Yellow River, China. Journal of Arid Land, 2026, 18(3): 452-476.

URL:

http://jal.xjegi.com/10.1016/j.jaridl.2026.03.006     OR     http://jal.xjegi.com/Y2026/V18/I3/452

Fig. 1 Overview of the study area. HN, Huinong District; DWK, Dawukou District; PL, Pingluo County; HL, Helan County; XX, Xixia District; XQ, Xingqing District; JF, Jinfeng District; YN, Yongning County; LW, Lingwu City; QTX, Qingtongxia City; LT, Litong District; ZN, Zhongning County; SPT, Shapotou District.
Data type Data name Year Resolution Source
Land use data Land use 1993, 2003,
2013, and 2023		 30 m Annual land cover datasets and its dynamics in China from 1985 to 2023 (https://doi.org/10.5281/zenodo.12779975)
Natural environmental
factors		 DEM 2020 30 m Geospatial Data Cloud
(https://www.gscloud.cn)
Slope 2020 30 m DEM
Aspect 2020 30 m
Soil type 1995 1 km Resource and Environmental Science Data Center (https://www.resdc.cn)
Annual mean precipitation 2022 1 km National Tibetan Plateau Scientific Data Center
(https://data.tpdc.ac.cn)
Annual mean temperature 2022 1 km
Socio-economic factors GDP 2020 1 km Resource and Environmental Science Data Center (https://www.resdc.cn)
Population 2022 1 km ORNL Land Scan Viewer
(https://landscan.ornl.gov)
Spatial
accessibility
factors		 Distance to primary roads 2023 30 m Open Street Map
(https://www.openstreetmap.org)
Distance to secondary roads 2023 30 m
Distance to tertiary roads 2023 30 m
Distance to river 2023 30 m
Distance to highways 2023 30 m
Distance to railways 2023 30 m
Distance to city center 2023 30 m National Bureau of Statistics of China (https://www.stats.gov.cn)
Table 1 Research data and resources
Fig. 2 Flowchart of the research method. PLUS, patch-generating land use simulation; CA, cellular automata; OA, overall accuracy; FOM, figure of merit; InVEST, integrated valuation of ecosystem services and tradeoffs.
Land use type Aboveground carbon density Belowground carbon density Soil carbon density Dead organic matter carbon density
(Mg C/hm2)
Cropland 1.67 0.25 63.72 0.67
Woodland 27.99 6.97 73.45 1.26
Grassland 0.27 2.76 59.12 0.06
Water body 0.19 0.65 11.88 0.82
Unused land 0.81 1.30 14.95 0.64
Built-up land 1.21 3.01 48.71 0.18
Table 2 Carbon density values under different land use types
Fig. 3 Contribution of driving factors in land use and land cover change (LUCC). DEM, digital elevation model; GDP, gross domestic product.
Fig. 4 Land use transformation. (a), 1993-2003; (b), 2003-2013; (c), 2013-2023; (d), 1993-2023.
Fig. 5 Distribution and areas of land use in 1993 (a), 2003 (b), 2013 (c) and 2023 (d)
Fig. 6 Distribution and areas of land use under four scenarios in 2033. (a), NDS (natural development scenario); (b), CPS (cropland protection scenario); (c), EPS (ecological protection scenario); (d), UDS (urban development scenario).
Fig. 7 Land use transformation under four scenarios from 2023 to 2033. (a), NDS; (b), CPS; (c), EPS; (d), UDS.
Fig. 8 Spatial distribution of carbon storage in 1993 (a), 2003 (b), 2013 (c), and 2023 (d). (a1)-(a3), (b1)-(b3), (c1)-(c3), and (d1)-(d3) represent the spatial distribution of carbon storage in zones 1, 2, and 3 in 1993, 2003, 2013, and 2023, respectively.
Fig. 9 Spatiotemporal changes of carbon storage from 1993 to 2023. (a), 1993-2003; (b), 2003-2013; (c), 2013-2023; (d), 1993-2023. The bar chart denotes the county-level percentage change in carbon storage.
Fig. 10 Spatial distribution of carbon storage in 2033. (a), NDS; (b), CPS; (c), EPS; (d), UDS. (a1)-(a3), (b1)-(b3), (c1)-(c3), and (d1)-(d3) represent the spatial distribution of carbon storage in zones 1, 2, and 3 for NDS, CPS, EPS, and UDS, respectively.
Fig. 11 Spatiotemporal changes of carbon storage from 2023 to 2033. (a), NDS; (b), CPS; (c), EPS; (d), UDS. The bar chart denotes the county-level percentage change in carbon storage.
Land use transformation Variation in area (km2) Variation in carbon storage (Mg C)
NDS CPS EPS UDS NDS CPS EPS UDS
A→B 0.47 0.39 0.34 0.46 2056.67 1709.34 1463.47 2013.74
A→C 387.48 367.57 378.62 365.65 -158,842.47 -150,679.30 -155,208.78 -149,891.98
A→D 79.03 78.24 83.65 81.55 -417,032.76 -412,872.26 -441,454.30 -430,335.89
A→E 11.67 11.34 10.37 11.35 -56,710.73 -55,096.68 -50,376.98 -55,184.16
A→F 427.37 413.73 410.81 429.38 -563,938.31 -545,934.43 -542,077.13 -566,581.89
B→C 8.53 2.78 0.54 8.17 -40,472.78 -13,203.31 -2584.28 -38,772.71
C→A 1652.74 1674.89 1583.08 1559.14 677,514.32 686,594.68 648,958.70 639,146.00
C→B 14.89 8.84 5.91 14.13 70,681.06 41,955.00 28,042.61 67,050.25
C→D 67.00 68.56 68.51 67.87 -326,092.03 -333,683.41 -333,433.73 -330,332.34
C→E 894.68 838.97 753.80 844.35 -3,981,545.67 -3,733,591.73 -3,354,587.62 3,757,538.82
C→F 376.24 306.43 285.91 370.52 -342,236.12 -278,730.10 -260,065.69 -337,033.59
D→A 102.46 105.23 103.26 103.17 540,688.78 555,298.01 544,915.77 544,459.83
D→B 0.02 0.02 0.02 0.02 190.34 199.00 199.00 207.65
D→C 23.34 20.56 19.53 20.73 113,612.25 100,050.27 95,038.99 100,900.08
D→E 2.53 2.51 2.50 2.50 1054.92 1045.92 1041.41 1042.92
D→F 27.75 28.00 25.56 28.33 109,829.15 110,808.66 101,152.53 112,126.54
E→A 372.27 376.21 353.87 348.73 1,809,271.46 1,828,421.45 1,719,877.15 1,694,878.93
E→B 0.12 0.12 0.11 0.12 1100.81 1059.42 1051.15 1100.81
E→C 1460.06 1474.75 1514.63 1489.10 6,497,592.53 6,562,985.44 6,740,423.82 6,626,848.36
E→D 16.61 16.36 17.75 16.45 -6925.88 -6823.05 -7400.61 -6858.33
E→F 139.63 130.15 135.23 142.53 494,361.31 460,806.95 478,788.65 504,641.12
F→A 9.71 7.99 10.72 4.34 12,809.36 10,545.81 14,144.21 5726.56
F→C 9.04 5.72 0.69 0.71 8225.80 5204.98 627.91 644.28
F→D 7.98 7.80 8.54 8.03 -31,575.66 -30,870.42 -33,784.00 -31,796.49
F→E 0.13 0.13 0.92 0.13 -449.30 -458.86 -3266.21 -452.49
Total 6091.76 5947.29 5774.86 5917.49 4,413,120.22 4,804,690.63 5,191,443.11 4,595,961.54
Table 3 Carbon storage variations induced by land use transitions from 1993 to 2033
Fig. 12 Spatial autocorrelation analysis of carbon storage. (a), 1993; (b), 2003; (c), 2013; (d), 2023; (e), NDS; (f), CPS; (g), EPS; (h), UDS.
Regression tree Sampling rate (%) Patch generation threshold Expansion coefficient Percentage of seed (%)
70 20.00 0.9 0.1 0.10
Table S1 Parameter settings of the PLUS model
Fig. S1 Spatial pattern of actual (a) and simulated (b) land use distribution in 2023
Type NDS CPS EPS UDS
A B C D E F A B C D E F A B C D E F A B C D E F
A 1 0 1 1 1 1 1 0 0 0 0 0 1 0 1 1 1 1 1 0 1 1 1 1
B 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0
C 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
D 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1
E 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1
F 1 0 0 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 0 0 1 0 1
Table S2 Land use transfer matrix
Scenario 5th
percentile
(×106 Mg C)		 50th
percentile
(×106 Mg C)		 95th
percentile
(×106 Mg C)		 Mean
(×106 Mg C)		 Standard
deviation
(×106 Mg C)		 Coefficient
of
variation (%)		 Number
of iterations
NDS -0.665 -0.603 -0.538 -0.601 0.035 5.80 1000
CPS -0.248 -0.212 -0.175 -0.211 0.016 7.60 1000
EPS 0.162 0.180 0.198 0.181 0.011 6.10 1000
UDS -0.481 -0.421 -0.362 -0.420 0.028 6.70 1000
Table S3 Carbon storage change uncertainty analysis in 2033
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