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耦合PLUS-InVEST模型的多情景土地利用变化及其对碳储量影响

雒舒琪 胡晓萌 孙媛 闫彩 张鑫

雒舒琪, 胡晓萌, 孙媛, 闫彩, 张鑫. 耦合PLUS-InVEST模型的多情景土地利用变化及其对碳储量影响[J]. 中国生态农业学报 (中英文), 2023, 31(2): 300−314 doi: 10.12357/cjea.20220520
引用本文: 雒舒琪, 胡晓萌, 孙媛, 闫彩, 张鑫. 耦合PLUS-InVEST模型的多情景土地利用变化及其对碳储量影响[J]. 中国生态农业学报 (中英文), 2023, 31(2): 300−314 doi: 10.12357/cjea.20220520
LUO S Q, HU X M, SUN Y, YAN C, ZHANG X. Multi-scenario land use change and its impact on carbon storage based on coupled Plus-Invest model[J]. Chinese Journal of Eco-Agriculture, 2023, 31(2): 300−314 doi: 10.12357/cjea.20220520
Citation: LUO S Q, HU X M, SUN Y, YAN C, ZHANG X. Multi-scenario land use change and its impact on carbon storage based on coupled Plus-Invest model[J]. Chinese Journal of Eco-Agriculture, 2023, 31(2): 300−314 doi: 10.12357/cjea.20220520

耦合PLUS-InVEST模型的多情景土地利用变化及其对碳储量影响

doi: 10.12357/cjea.20220520
基金项目: 陕西省科技统筹创新计划项目(2016KTZDNY-01-01)资助
详细信息
    作者简介:

    雒舒琪, 主要研究方向为水文水资源和生态学研究。E-mail: luoshuqi@nwafu.edu.cn

    通讯作者:

    张鑫, 主要研究方向为水文水资源与3S技术应用。E-mail: zhxin@nwsuaf.edu.cn

  • 中图分类号: TV11

Multi-scenario land use change and its impact on carbon storage based on coupled Plus-Invest model

Funds: This study was supported by the Science and Technology Coordinated Innovation Plan Project of Shaanxi Province (2016KTZDNY-01-01).
More Information
  • 摘要: 土地利用/覆被变化(LUCC)是陆地生态系统碳储量变化的重要原因, LUCC往往受政策的限制, 从而影响碳储量变化。预测政策指引下的西安市2030年LUCC, 分析其对碳储量的影响, 对西安市政策制定、土地利用结构调整、实现“双碳”目标具有重要意义。本研究基于2000年、2010年和2020年土地利用数据(LULC), 选取11个驱动因子, 根据西安市“十四五”政策规划建立自然发展(Q1)、生态保护(Q2)和城镇发展(Q3) 3个情景, 采用PLUS模型预测并分析西安市2030年土地利用空间分布格局, 并耦合InVEST模型评估西安市在不同发展情景的碳储量变化。研究表明: 1) PLUS模型在西安市的适用性较强, 模型总体精度为0.93, Kappa系数为0.89。2) 2000—2020年西安市建设面积、草地、水体数量增加, 耕地、林地、湿地面积减少, 从转移方向上看, 主要由耕地转为建设用地。3) 2030年, Q1情景延续了以往发展模式, Q2情景下林地、水体等生态用地数量均较2020年有所增加, Q3情景下建设用地大幅增加, 增幅为10.42%。4) LUCC是导致生态系统碳储量变化的主要原因, 2030年Q1情景下碳储量总量较2020年减少373.28 t, 说明延续以往的发展模式会使碳储量总量减少; Q2情景下碳储量总量较2020年增加564.73 t, 说明一定的生态保护措施保护了林地、湿地等生态用地和耕地的数量, 限制了碳密度较高的生态用地和耕地等转化成碳密度较低的建设用地, 可以减缓陆地生态系统碳储量减少趋势, 增加西安市碳储量; Q3情景下碳储量减少734.15 t, 城市化进程的加快, 建设用地规模扩大, 大量的建设用地占用生态用地和耕地, 从而使碳储量大幅减少。研究表明建设用地大幅扩张侵占生态用地和耕地是造成生态系统碳储量流失的主要原因, 实施科学、合理的生态保护措施, 可以很好地解决因经济发展而造成的碳储量下降问题。
  • 图  1  研究区陕西省西安市概况图

    Figure  1.  Location and general situation of the study area of Xi’an City

    图  2  研究区PLUS模型输入的驱动因子数据的空间分布

    Figure  2.  Spatial distribution of various influencing factors of Patch-generating Land Use Simulation (PLUS) in the study area

    图  3  不同发展情景下研究区限制发展区域的分布

    Figure  3.  Distribution of restricted development areas under different development scenarios in 2030 in the study area

    图  4  研究区2030年规划交通、规划开发区以及秦岭保护区

    Figure  4.  Planed transportation, development zones and restricted development areas of Qinling Pretected Area and Xixian New Area of the study area in 2030

    图  5  研究区2020年实际与模拟的土地利用类型分布

    Figure  5.  Actual and simulated distribution of different land use types in 2020 of the study area

    图  6  2000年、2010年和2020年研究区域土地利用格局图

    Figure  6.  Land use patterns of the study area in 2000, 2010 and 2020

    图  7  2030年3种情景下研究区域土地利用格局

    Figure  7.  Land use patterns of the study area under three development scenarios in 2030

    图  8  2000—2030年不同情景土地利用转移空间格局

    Figure  8.  Spatial patterns of land use transfer under different development scenarios from 2000 to 2030 in the study area

    图  9  2000—2020年西安市碳储量变化特征的空间分布

    Figure  9.  Distribution of carbon storage changes from 2000 to 2020 in the study area

    图  10  2020—2030年不同发展情景下研究区碳储量变化

    Figure  10.  Changes in carbon stocks from 2020 to 2030 under different development scenarios in the study area

    表  1  PLUS模型输入数据来源及处理

    Table  1.   Data sources and processing of drivers of Patch-generating Land Use Simulation (PLUS)

    数据类型
    Data type
    数据名称
    Data name
    数据精度
    Data accuracy
    数据来源及处理
    Data source and processing
    土地利用数据
    Land use data
    土地利用数据
    Land use data
    30 m http://globeland30.org/
    社会经济数据
    Socio-economic data
    人口 Population 1 km http://www.resdc.cn/data
    国内生产总值 Gross domestic product 1 km
    夜光灯数据 Glow-in-the-dark data 1 km http://www.resdc.cn/data
    到铁路距离 Distance to railway 1 km
    到高速/国道/省道距离
    Distance to highway/national highway/provincial highway
    1 km
    到水域距离 Distance to openwater 1 km
    气候和环境数据
    Climate and environmental data
    土壤类型 Soil type 1 km http://vdb3.soil.csdb.cn/
    土壤侵蚀模数 Modulus of soil erosion 1 km http://www.resdc.cn/data
    年平均气温 Mean annual temperature 1 km
    年平均降水量 Mean annual precipitation 1 km
    数字高程数据 Digital elevation model 30 m http://www.gscloud.cn/search
    坡度
    Slope
    30 m ArcGIS中由DEM生成
    Generated by DEM in ArcGIS
    未来驱动数据 Future-driven data 未来交通规划 Future transportation planning 1 km http://www.shaanxi.gov.cn/
    规划开发区 Planned development zone 1 km
    碳密度数据 Carbon density data 碳密度数据 Carbon density data 1 km http://www.cnern.org.cn/
    下载: 导出CSV

    表  2  PLUS模型模拟2030年不同发展情景下不同土地利用类型的邻域权重

    Table  2.   Neighborhood weights of different land use types under different development scenarios in 2030 simulated with Patch-generating Land Use Simulation (PLUS) in the study area

    发展情景 Development scenario耕地 Cultivated land林地 Forest草地 Grassland湿地 Wetland水体 Water建设用地 Construction land
    自然发展 Business as usual0.43800.06160.03850.00040.01470.4468
    生态保护 Ecological protection0.44130.05830.05180.00040.01960.4286
    城镇发展 Town development0.46340.03630.03380.00030.01490.4514
    下载: 导出CSV

    表  3  不同发展情景下土地利用转移矩阵

    Table  3.   Transfer cost matrix of each land use type under different development scenarios

    发展情景
    Development scenario
    耕地
    Cultivated land
    林地
    Forest
    草地
    Grassland
    湿地
    Wetland
    水体
    Water
    建设用地
    Construction land
    自然发展
    Business as usual
    耕地 Cultivated land111111
    林地 Forest010000
    草地 Grassland111111
    湿地 Wetland111111
    水体 Water000110
    建设用地 Construction land000001
    生态保护
    Ecological protection
    耕地 Cultivated land111000
    林地 Forest010000
    草地 Grassland011000
    湿地 Wetland000110
    水体 Water000110
    建设用地 Construction land111111
    城镇发展
    Town development
    耕地 Cultivated land111111
    林地 Forest111111
    草地 Grassland111111
    湿地 Wetland111111
    水体 Water111111
    建设用地 Construction land000001
    下载: 导出CSV

    表  4  研究区不同土地利用类型碳密度

    Table  4.   Carbon densities of different land use types in the study area

    kg∙m−2 
    土地利用类型
    Land-use
    type
    地上碳密度
    Aboveground
    carbon density
    地下碳密度
    Underground
    carbon density
    土壤碳密度
    Soil carbon
    density
    耕地 Cultivated land1.2217.2611.52
    林地 Forest9.0724.7916.87
    草地 Grassland7.5518.5010.61
    湿地 Wetland000
    水体 Water0.6400
    建设用地 Construction land0.5300
    下载: 导出CSV

    表  5  2000—2020年研究区各期不同土地利用类型面积及比例

    Table  5.   Areas and proportions of different land use types in each period from 2000 to 2020 in the study area

    土地利用类型
    Land-use type
    200020102020
    面积 Area (km2)比例 Proportion (%)面积 Area (km2)比例 Proportion (%)面积 Area (km2)比例 Proportion (%)
    耕地 Cultivated land4239.8541.953875.3238.343692.1136.53
    林地 Forest4770.9647.214777.3147.274752.2847.02
    草地 Grassland193.841.92185.621.84206.252.04
    湿地 Wetland7.050.071.140.010.930.01
    水体 Water34.380.3446.130.4654.080.54
    建设用地 Construction land860.568.511221.1212.081400.9913.86
    下载: 导出CSV

    表  6  2000—2020年西安市土地利用转移矩阵

    Table  6.   Conversion matrix of land use from 2000 to 2020 in the study area

    土地利用类型
    Land-use type
    转移面积 Transfer area (hm2)2020年面积
    Area in 2020 (km2)
    耕地
    Cultivated land
    林地
    Forest
    草地
    Grassland
    湿地
    Wetland
    水体
    Water
    建设用地
    Construction land
    耕地 Cultivated land3525.4017.136.280.7733.32656.964239.85
    林地 Forest20.534666.9878.710.013.101.634770.96
    草地 Grassland8.9564.47114.660.022.583.17193.84
    湿地 Wetland4.940.180.450.001.280.207.05
    水体 Water13.262.902.080.1313.422.5834.38
    建设用地 Construction land119.040.624.080.000.37736.45860.56
    2000年面积 Area in 2000 (km2)3692.114752.28206.250.9354.081400.9910 106.64
    下载: 导出CSV

    表  7  2030年不同发展情景下不同土地利用类型的面积(km2)及其与2020年相比的变化

    Table  7.   Area of each land use type under different development scenarios in 2030 and its change from 2020 in the study area

    年份
    Year
    发展情景
    Development scenario
    耕地
    Cultivated land
    林地
    Forest
    草地
    Grassland
    湿地
    Wetland
    水体
    Water
    建设用地
    Construction land
    面积
    Area (km2)
    20203692.114752.28206.250.9354.081400.99
    自然发展
    Business as usual
    3544.124793.67165.020.8056.031547.00
    2030生态保护
    Ecological protection
    3863.254776.72190.280.8054.231221.35
    城镇发展
    Town development
    3531.874714.75184.730.6344.611630.05
    2030年变化率
    (与2020年比较)
    Annual gradient
    (compare to 2020)
    (%)
    自然发展
    Business as usual
    −4.010.87−20.00−13.983.6110.42
    生态保护
    Ecological protection
    4.640.51−7.74−13.980.28−12.82
    城镇发展
    Town development
    −4.34−0.79−10.43−32.26−17.5116.35
    下载: 导出CSV

    表  8  2000年、2010年、2020年及2030年自然发展(Q1)、生态保护(Q2)和城镇发展(Q3)情景下西安市各行政区碳储量

    Table  8.   Carbon storage of each administrative region of the study area in 2000, 2010, and 2030 under development scenarios of business as usual (Q1), ecological protection (Q2) and town development (Q3)

    行政区
    Administrative region
    200020102020Q1Q2Q3
    ×103 t 
    周至县 Zhouzhi12.93012.88012.86012.83612.97612.752
    鄠邑区 Huyi5.0594.9644.9104.8634.9754.821
    长安区 Chang’an5.8575.6735.5205.4285.5775.365
    未央区 Weiyang0.4960.3560.2400.2230.2580.212
    莲湖区 Lianhu0.0020.0040.0000.0020.0020.002
    新城区 Xincheng0.0070.0050.0100.0060.0080.006
    碑林区 Beilin0.0030.0040.0000.0030.0040.003
    雁塔区 Yanta0.2460.1710.0600.0490.0660.050
    灞桥区 Baqiao0.8040.6790.6100.5630.6430.536
    高陵区 Gaoling0.6550.5940.5600.5270.6130.507
    阎良区 Yanliang0.6070.5340.5300.5140.5800.505
    临潼区 Lintong2.6162.4602.4502.3842.5712.349
    蓝田县 Lantian8.2748.1738.0708.1408.2038.069
    合计 Total37.55636.49735.82035.53836.47635.177
    下载: 导出CSV
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  • 收稿日期:  2022-07-06
  • 录用日期:  2022-10-25
  • 修回日期:  2022-10-25
  • 网络出版日期:  2022-11-07
  • 刊出日期:  2023-02-10

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