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基于GWR模型的成渝城市群生态系统服务时空演变及驱动因素研究

邵明 董宇翔 林辰松

邵明, 董宇翔, 林辰松. 基于GWR模型的成渝城市群生态系统服务时空演变及驱动因素研究[J]. 北京林业大学学报, 2020, 42(11): 118-129. doi: 10.12171/j.1000-1522.20200217
引用本文: 邵明, 董宇翔, 林辰松. 基于GWR模型的成渝城市群生态系统服务时空演变及驱动因素研究[J]. 北京林业大学学报, 2020, 42(11): 118-129. doi: 10.12171/j.1000-1522.20200217
Shao Ming, Dong Yuxiang, Lin Chensong. Spatio-temporal evolution and driving factors of ecosystem services in Chengdu-Chongqing urban agglomeration of southwestern China based on GWR model[J]. Journal of Beijing Forestry University, 2020, 42(11): 118-129. doi: 10.12171/j.1000-1522.20200217
Citation: Shao Ming, Dong Yuxiang, Lin Chensong. Spatio-temporal evolution and driving factors of ecosystem services in Chengdu-Chongqing urban agglomeration of southwestern China based on GWR model[J]. Journal of Beijing Forestry University, 2020, 42(11): 118-129. doi: 10.12171/j.1000-1522.20200217

基于GWR模型的成渝城市群生态系统服务时空演变及驱动因素研究

doi: 10.12171/j.1000-1522.20200217
基金项目: 中央高校基本科研业务费专项(2017ZY11),北京市共建项目(2015BLUREE01),北京市重点研发计划项目(D171100007117003),国家自然科学基金项目(51908036)
详细信息
    作者简介:

    邵明,博士生。主要研究方向:风景园林规划设计与理论研究。 Email:shao_0701@foxmail.com 地址:100083北京市海淀区清华东路35号北京林业大学园林学院

    责任作者:

    林辰松,讲师。主要研究方向:风景园林规划设计与理论研究。 Email:7884231@qq.com 地址:同上

  • 中图分类号: X171.1;S718.5

Spatio-temporal evolution and driving factors of ecosystem services in Chengdu-Chongqing urban agglomeration of southwestern China based on GWR model

  • 摘要:   目的  对成渝城市群自然生态系统服务时空演变特征与驱动因素进行研究,有助于规划者从宏观尺度上掌握其自然资源存量情况与发展趋势,合理拟定成渝城市群规划建设策略,从而推动我国西部大开发平台建设。  方法  选择成渝城市群为研究对象,以144个市、县、区作为研究单元,通过当量因子法量化其生态系统服务,建立“自然—社会”驱动因子指标体系,在此基础上运用最小二乘法(OLS)模型筛选具有显著相关性的驱动因子,运用地理加权回归(GWR)构建回归模型,研究成渝城市群自然社会因素对生态系统服务的驱动作用。  结果  (1)成渝城市群生态系统服务总量劣化趋势显著,1995—2015年间(1995、2000、2005、2010、2015年,共5期)成渝城市群生态系统服务价值分别为3 470.53亿、3 464.08亿、3 452.43亿、3 438.02亿和3 423.92亿元,分别比前一期下降6.45亿、11.65亿、14.40亿和14.10亿元;(2)成渝城市群生态系统服务空间分布以成都、重庆两处核心城市中心区与缙云—中梁山脉中心区呈现显著区域化分布特征,全域超过90.38%的区域生态系统服务均处于下降趋势;(3)降雨量、温度、单位面积平均GDP、林业用地比例、人口密度5项驱动因子对成渝城市群生态系统服务具有显著影响。其中,降雨量、单位面积平均GDP呈现较明显的负相关驱动特征,林地覆盖比例呈现较明显的正相关驱动特征,降雨量与人口密度则呈现较明显的两极化趋势。  结论  本研究明确了在研究期限内成渝城市群生态系统服务逐渐劣化的发展状态,所构建的地理加权回归(GWR)模型在空间层面上量化降雨量、温度、单位面积平均GDP、林业用地比例、人口密度5项主要驱动因素对成渝城市群生态系统服务所呈现的不同驱动特征与驱动强度,为成渝城市群长远规划提供决策依据。

     

  • 图  1  成渝城市群生态系统服务评价

    Figure  1.  Ecosystem service evaluation of Chengdu-Chongqing urban agglomeration

    图  2  地理加权回归(GWR)模型拟合结果

    Figure  2.  Fitting result of geographically weighted regression (GWR) model

    图  3  成渝城市群温度与生态系统服务回归系数空间分布

    Figure  3.  Spatial distribution of regression coefficients between temperature and ecosystem service in Chengdu-Chongqing urban agglomeration

    图  7  成渝城市群人口密度与生态系统服务回归系数空间分布

    Figure  7.  Spatial distribution of regression coefficients between population density and ecosystem service in Chengdu-Chongqing urban agglomeration

    图  4  成渝城市群降雨量与生态系统服务回归系数空间分布

    Figure  4.  Spatial distribution of regression coefficients between rainfall and ecosystem service in Chengdu-Chongqing urban agglomeration

    图  5  成渝城市群林地覆盖比例与生态系统服务回归系数空间分布

    Figure  5.  Spatial distribution of regression coefficients between forest land proportion and ecosystem service in Chengdu-Chongqing urban agglomeration

    图  6  成渝城市群单位面积平均GDP与生态系统服务回归系数空间分布

    Figure  6.  Spatial distribution of regression coefficients between average GDP per unit area and ecosystem service in Chengdu-Chongqing urban agglomeration

    表  1  数据来源及精度表

    Table  1.   Data sources and accuracy table

    数据名称
    Data name
    数据描述
    Data description
    数据格式/精度
    Data format/accuracy
    数据来源
    Data source
    行政边界
    Administrative boundary
    正式政府规划边界
    Official government planning boundary
    Shp 《成渝城市群发展规划》
    Chengdu-Chongqing City Cluster Development Plan
    土地利用
    Land use
    土地利用
    Land use
    Raster/250 m 欧洲航天航空局(ESA)
    European Space Agency
    农产经济数据
    Agricultural economic data
    四川省粮食经济数据
    Grain economic data of Sichuan Province
    Excel 《中国农产品成本收益资料汇编》《中国统计年鉴》
    Data Collection on the Cost and Benefit of China’s Agricultural Products (China Statistical Yearbook)
    高程
    Elevation
    平均高程数据
    Data of average elevation
    Raster/30 m 地理空间数据云平台(http://www.gscloud.cn/)
    Geospatial data cloud platform (http://www.gscloud.cn/)
    植被覆盖指数
    Vegetation coverage index(NDVI)
    年均NDVI
    Average annual NDVI
    Raster/500 m 地理空间数据云平台(http://www.gscloud.cn/)
    Geospatial data cloud platform (http://www.gscloud.cn/)
    降雨量
    Rainfall
    年均降雨量
    Average annual rainfall
    Raster/1 km 中国科学院资源环境科学数据中心(www.resdc.cn)
    Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (www.resdc.cn)
    温度
    Temperature
    年均地表温度
    Average annual land surface temperature
    Raster/1 km 中国科学院资源环境科学数据中心(www.resdc.cn)
    Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (www.resdc.cn)
    单位面积GDP
    GDP per unit area
    每平方公里平均GDP
    Average GDP per square kilometer
    Raster/1 km 中国科学院资源环境科学数据中心(www.resdc.cn)
    Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (www.resdc.cn)
    林地面积比例
    Forestland area ratio
    森林生态系统占行政单元总面积比例
    Forestland area ratio to the administrative units
    Shp 欧洲航天航空局(ESA)
    European Space Agency
    人口密度
    Population density
    每平方公里平均人口数量
    Average population per square kilometer
    Raster/1 km 全球人口动态统计分析数据库
    Global statistical analysis database of population dynamics
    已建成区面积比例
    Built-up area ratio
    已建成区占行政单元总面积比例
    Proportion of built up area to the administrative units
    Shp 清华大学地球系统科学数据库
    Tsinghua University Earth System Science Database
    土壤
    Soil
    土壤含沙量
    Soil sediment concentration
    Raster/1 km 世界土壤数据库
    Harmonized World Soil Database
    基础设施密度
    Infrastructure density
    基础设施在行政单元内比例
    Infrastructure ratio to the administrative units
    Shp 高德地图POI接口
    AutoNavi POI interface
    下载: 导出CSV

    表  2  驱动因子统计学分析表

    Table  2.   Statistical analysis on driving factors

    驱动因子
    Driving factor
    平均值
    Average value
    标准差
    Standard deviation
    最大值
    Maximum value
    最小值
    Minimum value
    自然环境因子
    Natural environmental
    factor
    高程
    Elevation/m
    608.477 5 391.038 9 2 466.168 1 243.272 3
    土壤(含沙量)
    Soil (sediment concentration)/%
    11.144 2 19.276 7 90.000 0 0.000 0
    植被覆盖指数
    NDVI
    0.761 0 0.085 3 0.879 5 0.404 3
    降雨量
    Rainfall/mm
    1 053.407 4 123.527 2 1 247.849 5 789.998 9
    温度
    Temperature/℃
    16.927 6 1.725 4 19.226 3 10.279 7
    社会经济因子
    Socioeconomic factor
    单位面积GDP/(元·km−2)
    GDP per unit area/(CNY·km−2)
    6 745.219 8 18 194.331 5 136 089.924 2 145.433 3
    人口密度/(人·km−2)
    Population density/(person·km−2)
    893.455 7 2 274.309 1 21 474.381 0 39.794 2
    已建成区面积比例
    Built-up area ratio/%
    0.071 5 0.152 6 0.863 5 0.000 0
    林地面积比例
    Forestland area ratio/%
    0.170 4 0.156 9 0.670 9 0.005 2
    基础设施密度/(个·km−2)
    Infrastructure density /(number·km−2)
    0.339 7 0.784 8 4.027 5 0.003 7
    下载: 导出CSV

    表  3  基于探索性回归的OLS模型参数因子筛选结果表

    Table  3.   Filtering results of parameter factor based on exploratory regression

    驱动因素
    Driving factor
    初始模型 Initial model 筛选后模型 Filtered model
    系数
    Coefficient
    标准差
    Standard
    deviation
    概率/稳健概率
    Probability/
    robust probability
    系数
    Coefficient
    标准差
    Standard
    deviation
    概率/稳健概率
    Probability/
    robust probability
    自然环境因子
    Natural environmental
    factor
    高程
    Elevation
    0.021 3 0.009 5 m */*
    土壤(含沙量)
    Soil (sediment concentration)
    0.054 2 0.049 3%
    植被覆盖指数
    NDVI
    43.740 9 31.353 2
    降雨量
    Rainfall
    0.027 0 0.010 0 mm **/** 0.064 4 0.009 8 mm ***/***
    温度
    Temperature
    1.164 0 2.284 4 ℃ −5.725 3 0.705 5 ℃ ***/***
    社会经济因子
    Socioeconomic
    factor
    单位面积GDP
    GDP per unit area
    0.000 0 0.000 1元/km2
    0.000 1 CNY/km2
    −0.000 6 0.000 1元/km2
    0.000 1 CNY/km2
    ***/***
    人口密度
    Population density
    0.000 8 0.000 7人/km2
    0.000 7 person/km2
    −0.003 1 0.000 6人/km2
    0.000 6 person/km2
    ***/**
    已建成区面积比例
    Built-up area ratio
    −138.790 0 34.630 7% ***/***
    林地面积比例
    Forestland area ratio
    272.574 3 6.440 8% ***/*** 282.986 2 7.336 9% ***/***
    基础设施密度
    Infrastructure density
    4.783 3 5.611 1个/km2
    5.611 1 number/km2
    注:*、**、***分别为P < 0.05、P < 0.01、P < 0.001;初始模型与筛选后模型均满足R2 > 0.6的回归拟合优度。Notes: *,**,*** mean P < 0.05, P < 0.01, P < 0.001; both the initial model and the screened model meet R2 > 0.6 goodness of regression fitting.
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-07-13
  • 修回日期:  2020-10-09
  • 网络出版日期:  2020-10-31
  • 刊出日期:  2020-12-14

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