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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (5) : 172-182     DOI: 10.6046/zrzyyg.2024215
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Spatiotemporal evolution and prediction of ecosystem carbon storage in Xianyang City based on the PLUS-InVEST model
CHEN Qiuji(), XIE Mimi(), NAN Dandan, LUO Hao
College of Geomatics，Xi’an University of Science and Technology，Xi’an 710054，China
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Abstract  

Land use change is a primary driver of carbon storage changes in terrestrial ecosystems. Investigating its impact on carbon storage is crucial for optimizing territorial spatial planning and reducing regional carbon emissions. Focusing on Xianyang City，this study analyzed changes in land use and carbon storage over the past two decades （2000—2020） based on corresponding land-use data from 2000，2010，and 2020，using the patch-generating land use simulation （PLUS） and integrated valuation of ecosystem services and tradeoffs （InVEST） models. Moreover，it predicted the distribution of carbon storage in 2030 under four scenarios：natural growth，urban development，cropland protection，and ecological protection. The results indicate that in 2000，2010，and 2020，Xianyang City exhibited carbon storage of 10 047.534×104 t，10 120.754×104 t，and 10 030.210×104 t，respectively，characterized by a pattern of an initial increase followed by a decrease. The conversion of grassland to forest and cropland to construction land was identified as the main factor contributing to the increase and decrease in carbon storage，respectively. Among the four scenarios for 2030，cropland protection and ecological protection scenarios displayed increased carbon storage，while the urban development scenario experienced the most significant decline in carbon storage due to the rapid expansion of construction land. Areas with high carbon storage were mainly concentrated in northern Xianyang，whereas those with low carbon storage were distributed in the southern economic centers. Looking ahead，the future planning in Xianyang should fully consider the impacts of land use on carbon storage，ecological land protection，and restriction of extensive construction land expansion. By doing so，the city can achieve dual optimization of land use and carbon emissions. The findings provide a scientific basis and data reference for enhancing ecosystem carbon sink capacity and optimizing terrestrial spatial planning in Xianyang City.
Keywords patch-generating land use simulation （PLUS） model      integrated valuation of ecosystem services and tradeoffs （InVEST） model      land use change      carbon storage      Xianyang City     
ZTFLH:  TP79  
  X87  
Issue Date: 28 October 2025
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Qiuji CHEN
Mimi XIE
Dandan NAN
Hao LUO
Cite this article:   
Qiuji CHEN,Mimi XIE,Dandan NAN, et al. Spatiotemporal evolution and prediction of ecosystem carbon storage in Xianyang City based on the PLUS-InVEST model[J]. Remote Sensing for Natural Resources, 2025, 37(5): 172-182.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024215     OR     https://www.gtzyyg.com/EN/Y2025/V37/I5/172
Fig.1  Overview of the study area
土地利
用类型		 地上
碳密度		 地下
碳密度		 土壤
碳密度		 死亡有机
物碳密度
耕地 28.14 3.20 72.50 0
林地 24.56 5.97 87.30 13
草地 1.59 4.57 60.19 2.11
水域 0.30 0 0 0
建设用地 2.35 0 0 0
Tab.1  Carbon density of each land type in the study area （t/hm2）
地类 Q1 Q2 Q3 Q4
耕地 林地 草地 水域 建设
用地		 耕地 林地 草地 水域 建设
用地		 耕地 林地 草地 水域 建设
用地		 耕地 林地 草地 水域 建设
用地
耕地 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1
林地 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 0
草地 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0
水域 1 0 1 1 0 1 1 1 1 1 1 0 1 1 0 0 0 0 1 0
建设用地 1 1 1 1 1 0 1 1 0 1 1 0 0 0 1 1 1 1 1 1
Tab.2  Multi-scenario transition matrix
Fig.2  Spatial distribution of land use in Xianyang City from 2000 to 2020
Fig.3  Sankey diagram of land use type conversion in Xianyang City from 2000 to 2020
地类 2000年 2010年 2020年 2000—2020年
变化量
耕地 6 540.08 6 412.07 6 353.04 -187.03
林地 2 800.66 3 211.59 3 190.63 389.97
草地 690.08 477.30 464.57 -225.51
水域 0.15 0.11 0.10 -0.05
建设用地 16.56 19.69 21.85 5.29
总计 10 047.53 10 120.75 10 030.20 -17.33
Tab.3  Carbon storage of each land type in Xianyang City from 2000 to 2020 （104t）
Fig.4  Spatial distribution and variation of carbon storage in Xianyang City from 2000 to 2020
土地利用
类型转化		 面积/km2 植被碳储
量变化/
104 t		 土壤碳储
量变化/
104 t		 总碳储量
变化/
104 t
耕地-林地 105.66 -0.86 29.37 28.52
耕地-草地 67.61 -17.03 -6.90 -23.92
耕地-水域 16.98 -5.27 -12.31 -17.58
耕地-建设用地 428.14 -124.12 -310.40 -434.52
小计 618.39 -147.27 -300.24 -447.51
林地-耕地 78.48 0.64 -21.82 -21.18
林地-草地 121.35 -29.57 -46.11 -75.69
林地-水域 3.05 -0.92 -3.06 -3.99
林地-建设用地 1.84 -0.52 -1.85 -2.36
小计 204.72 -30.38 -72.84 -103.22
草地-耕地 119.98 30.21 12.24 42.45
草地-林地 394.00 96.02 149.72 245.74
草地-水域 3.51 -0.21 -2.18 -2.39
草地-建设用地 8.10 -0.31 -5.05 -5.36
小计 525.59 125.71 154.72 280.44
水域-耕地 28.84 8.95 20.91 29.86
水域-林地 2.86 0.87 2.87 3.74
水域-草地 3.91 0.23 2.44 2.67
水域-建筑用地 1.80 0.04 0.00 0.04
小计 37.42 10.08 26.22 36.30
建设用地-耕地 210.98 61.16 152.96 214.12
建设用地-林地 0.28 0.08 0.29 0.37
建设用地-草地 3.30 0.13 2.06 2.18
建设用地-水域 0.27 -0.01 0.00 -0.01
小计 214.83 61.36 155.30 216.66
总计 1 600.95 19.51 -36.84 -17.32
Tab.4  Changes in carbon stocks caused by land type transfers from 2000 to 2020
Fig.5  Spatial distribution of land use under four scenarios in Xianyang City in 2030
地类 面积/km2 变化率/%
2020年 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
耕地 6 118.11 6 072.93 6 036.62 6 145.38 6 103.87 -0.74 -1.33 0.45 -0.23
林地 2 438.76 2 421.02 2 419.87 2 425.98 2 467.97 -0.73 -0.77 -0.52 1.20
草地 678.60 663.24 661.78 663.83 655.74 -2.26 -2.48 -2.18 -3.37
水域 35.76 35.21 35.11 35.30 35.76 -1.53 -1.82 -1.29 0.01
建设用地 929.92 1 008.76 1 047.78 930.66 937.81 8.48 12.67 0.08 0.85
Tab.5  Land type area and change rate unde four scenarios in Xianyang City in 2030
地类 2020年 Q1 Q2 Q3 Q4
耕地 6 353.045 6 306.126 6 268.429 6 381.367 6 338.256
林地 3 190.634 3 167.423 3 165.910 3 173.910 3 228.844
草地 464.571 454.055 453.057 454.456 448.921
水域 0.107 0.106 0.105 0.106 0.107
建设用地 21.853 23.706 24.623 21.871 22.039
总计 10 030.210 9 951.414 9 912.123 10 031.711 10 038.168
Tab.6  The carbon storage of each land type in Xianyang City under four scenarios in 2030 （104t）
Fig.6  Spatial distribution of carbon storage in Xianyang City under four scenarios in 2030
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