Volume 41 Issue 6
Nov.  2024
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HUANG Xiaojie, DING Jinhua, WANG Daqing. Spatiotemporal evolution and regulation strategies of ecological risks in green space landscape in the water network area of southern Jiangsu[J]. Journal of Zhejiang A&F University, 2024, 41(6): 1283-1292. doi: 10.11833/j.issn.2095-0756.20240169
Citation: HUANG Xiaojie, DING Jinhua, WANG Daqing. Spatiotemporal evolution and regulation strategies of ecological risks in green space landscape in the water network area of southern Jiangsu[J]. Journal of Zhejiang A&F University, 2024, 41(6): 1283-1292. doi: 10.11833/j.issn.2095-0756.20240169

Spatiotemporal evolution and regulation strategies of ecological risks in green space landscape in the water network area of southern Jiangsu

doi: 10.11833/j.issn.2095-0756.20240169
  • Received Date: 2024-02-05
  • Accepted Date: 2024-06-24
  • Rev Recd Date: 2024-06-21
  • Available Online: 2024-08-14
  • Publish Date: 2024-11-20
  •   Objective  This study aims to discuss the ecological risks caused by landscape fragmentation and habitat quality decline in green space in the water network area of southern Jiangsu Province under the background of rapid urbanization.   Method  Taking Kunshan City as an example, the land use data from 2000, 2010 and 2020 were selected, and the landscape pattern index was used to construct a landscape ecological risk assessment model. The spatiotemporal evolution characteristics of landscape ecological risks in green space of Kunshan City from 2000 to 2020 were analyzed, and green space control zones were defined based on the characteristics of landscape ecological risk level transfer and change.  Result  (1) From 2000 to 2020, the total area of green space in Kunshan City showed a continuous reduction trend, with a significant decrease in cultivated land, a total reduction of 20 203.11 hm2, accounting for 21.70%. The water area first slightly increased and then continued to decrease, with an overall decrease of 3813.66 hm2. The proportion of forest land and grassland was relatively small and stable. The area transfer matrix between land types of green space mainly showed a shift from arable land to construction land, reflecting the increasing interference degree of green space by artificial construction. (2) There were certain changes in the distribution of landscape ecological risks, mainly manifested as a shift from low risk level to higher risk level. The proportion of the highest and high risk areas increased by 8.10% and 6.61%, respectively, while the area of low and the lowest risk areas decreased by 8.25% and 9.73%, respectively. (3) Based on the characteristics of landscape ecological risk level transfer and change, the study area was divided into three types of control zones: key restoration zone, coordinated buffer zone and optimal utilization zone.  Conclusion  The landscape ecological risk of green space in Kunshan City shows an upward trend. There is a correlation between the transformation of green space land use type and landscape ecological risk, reflecting the increasing ecological pressure of green space under human construction activities. Green space zoning regulation strategies based on the spatiotemporal evolution characteristics of risks are proposed in this study. [Ch, 4 fig. 5 tab. 28 ref.]
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  • [1]
    TRINDER J, LIU Qingxiang. Assessing environmental impacts of urban growth using remote sensing [J]. Geo-Spatial Information Science, 2020, 23(1): 20 − 39.
    [2]
    LI Feng, WANG Rusong. Research advance in ecosystem service of urban green space [J]. Chinese Journal of Applied Ecology, 2004, 15(3): 527 − 531.
    [3]
    NGOM R, GOSSELIN P, BLAIS C. Reduction of disparities in access to green spaces: their geographic insertion and recreational functions matter [J]. Applied Geography, 2016, 66: 35 − 51.
    [4]
    CHENG Chaonan, HU Yang, ZHAO Ming. Progress and prospect of the spatiotemporal change and ecosystem services evaluation of urban green space pattern [J]. Progress in Geography, 2020, 39(10): 1770 − 1782.
    [5]
    LIU Hui, CAO Shuo, CHEN Yu. Composition and morphological elements of habitat quadrat in urban green space: a case study of Xi’an [J]. Landscape Architecture, 2021, 28(6): 70 − 75.
    [6]
    ZHAO Haixia, WANG Shufen, MENG Fei, et al. Green space pattern changes and its driving mechanism: a case study of Nanjing metropolitan area [J]. Acta Ecologica Sinica, 2020, 40(21): 7861 − 7872.
    [7]
    SIDDIQUE S, UDDIN M M. Green space dynamics in response to rapid urbanization: patterns, transformations and topographic influence in Chattogram city, Bangladesh [J/OL]. Land Use Policy, 2022, 114 : 105974[2024-01-05]. doi: 10.1016/j.landusepol.2022.105974.
    [8]
    CHEN Yanhong, CAI Yuanbin. The differences of thermal environment effect of urban green space evolution: a case study of Fuzhou [J]. Chinese Journal of Ecology, 2019, 38(7): 2149 − 2158.
    [9]
    ZHANG Qian, ZHOU Dian, XU Duo, et al. Correlation between cooling effect of green space and surrounding urban spatial form: evidence from 36 urban green spaces [J/OL]. Building and Environment, 2022, 222 : 109375[2024-01-05]. doi: 10.1016/j.buildenv.2022.109375.
    [10]
    PENG Jian, DANG Weixiong, LIU Yanxu, et al. Review on landscape ecological risk assessment [J]. Acta Geographica Sinica, 2015, 70(4): 664 − 677.
    [11]
    XU Fengjiao, LU Xiao. Ecological risk pattern based on land use changes in Jiangsu coastal areas [J]. Acta Ecologica Sinica, 2018, 38(20): 7312 − 7325.
    [12]
    HOU Rui, LI Hongbo, GAO Yanli. Ecological risk assessment of land use in Jiangxia District of Wuhan based on landscape pattern [J]. Research of Soil and Water Conservation, 2021, 28(1): 323 − 330, 403.
    [13]
    GUO Hongjiang, CAI Yanpeng, LI Bowen, et al. An integrated modeling approach for ecological risks assessment under multiple scenarios in Guangzhou, China [J/OL]. Ecological Indicators, 2022, 142 : 109270[2024-01-05]. doi: 10.1016/j.ecolind.2022.109270.
    [14]
    WANG Ling, WANG Miao. Chengdu-chongqing urban landscape ecological risk evolution analysis [J]. Resources and Environment in the Yangtze Basin, 2023, 32(3): 626 − 637.
    [15]
    GUO Hongjiang, CAI Yanpeng, LI Bowen, et al. An improved approach for evaluating landscape ecological risks and exploring its coupling coordination with ecosystem services [J/OL]. Journal of Environmental Management, 2023, 348 : 119277[2024-01-05]. doi: 10.1016/j.jenvman.2023.119277.
    [16]
    WANG Tao, XIAO Caixia, LIU Jiao, et al. Dynamic evolution and landscape ecological risks assessment of Qilu Lake in Yunnan Plateau [J]. Journal of Zhejiang A&F University, 2020, 37(1): 9 − 17.
    [17]
    QIAN Yao, DONG Zheng, YAN Yan, et al. Ecological risk assessment models for simulating impacts of land use and landscape pattern on ecosystem services [J/OL]. Science of the Total Environment, 2022, 833 : 155218[2024-01-05]. doi:10.1016/j.scitotenv.2022.155218.
    [18]
    WU Jianguo. Landscape Ecology Pattern, Process, Scale and Hierarchy [M]. Beijing: Higher Education Press, 2007: 99 − 100.
    [19]
    ZHAO Yue, LUO Zhijun, LI Yating, et al. Study of the spatial-temporal variation of landscape ecological risk in the upper reaches of the Ganjiang River Basin based on the “production-living-ecological space” [J]. Acta Ecologica Sinica, 2019, 39(13): 4676 − 4686.
    [20]
    LIU Chunyan, ZHANG Ke, LIU Jiping. A long-term site study for the ecological risk migration of landscapes and its driving forces in the Sanjiang Plain from 1976 to 2013 [J]. Acta Ecologica Sinica, 2018, 38(11): 3729 − 3740.
    [21]
    ZHAO Weiquan, YANG Zhenhua, SU Weici, et al. Ecological risk assessment and management of watershed based on landscape pattern change: a case study of the Chishui River Basin in Guizhou [J]. Resources and Environment in the Yangtze Basin, 2017, 26(8): 1218 − 1227.
    [22]
    LI Chen, GAO Binpin, WU Yingmei, et al. Dynamic simulation of landscape ecological risk in mountain towns based on PLUS model [J]. Journal of Zhejiang A&F University, 2022, 39(1): 84 − 94.
    [23]
    SUN Caizhi, YAN Xiaolu, ZHONG Jingqiu. Evaluation of the landscape patterns vulnerability and analysis of spatial correlation patterns in the lower reaches of Liaohe River Plain [J]. Acta Ecologica Sinica, 2014, 34(2): 247 − 257.
    [24]
    FU Yangjun, SHI Xueyi, HE Juan. Characteristics of spatial and temporal differentiation of landscape pattern vulnerability in Fenhe River Basin [J]. Research of Soil and Water Conservation, 2020, 27(3): 197 − 202.
    [25]
    YANG Qingyuan, ZHANG Haozhe, TANG Qiang. Ecological restoration zoning of territorial space in Chongqing City based on adaptive cycle model [J]. Acta Geographica Sinica, 2022, 77(10): 2583 − 2598.
    [26]
    YU Shuhui, KANG Yuanyuan, DENG Wei, et al. Analysis of “Production-Living-Ecological” land trans formation and landscape ecological risk in the eastern counties of the Taihang Mountain: a case study in Pingshan County [J]. Chinese Journal of Eco-Agriculture, 2022, 30(7): 1113 − 1122
    [27]
    CHEN Bin, XU Shangzhao, ZHOU Yangyang, et al. Assessment of landscape ecological risk in Jianghan Plain area based on land use change [J]. Research of Soil and Water Conservation, 2022, 29(5): 228 − 234, 243.
    [28]
    HE Zhaoquan, SHANG Xue, ZHANG Tonghui, et al. Spatiotemporal variations of landscape ecological risk and its cold-hot spot pattern in the loess hills of northern Shaanxi over the past 20 years [J]. Chinese Journal of Ecology, 2023, 42(10): 2514 − 2525.
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Spatiotemporal evolution and regulation strategies of ecological risks in green space landscape in the water network area of southern Jiangsu

doi: 10.11833/j.issn.2095-0756.20240169

Abstract:   Objective  This study aims to discuss the ecological risks caused by landscape fragmentation and habitat quality decline in green space in the water network area of southern Jiangsu Province under the background of rapid urbanization.   Method  Taking Kunshan City as an example, the land use data from 2000, 2010 and 2020 were selected, and the landscape pattern index was used to construct a landscape ecological risk assessment model. The spatiotemporal evolution characteristics of landscape ecological risks in green space of Kunshan City from 2000 to 2020 were analyzed, and green space control zones were defined based on the characteristics of landscape ecological risk level transfer and change.  Result  (1) From 2000 to 2020, the total area of green space in Kunshan City showed a continuous reduction trend, with a significant decrease in cultivated land, a total reduction of 20 203.11 hm2, accounting for 21.70%. The water area first slightly increased and then continued to decrease, with an overall decrease of 3813.66 hm2. The proportion of forest land and grassland was relatively small and stable. The area transfer matrix between land types of green space mainly showed a shift from arable land to construction land, reflecting the increasing interference degree of green space by artificial construction. (2) There were certain changes in the distribution of landscape ecological risks, mainly manifested as a shift from low risk level to higher risk level. The proportion of the highest and high risk areas increased by 8.10% and 6.61%, respectively, while the area of low and the lowest risk areas decreased by 8.25% and 9.73%, respectively. (3) Based on the characteristics of landscape ecological risk level transfer and change, the study area was divided into three types of control zones: key restoration zone, coordinated buffer zone and optimal utilization zone.  Conclusion  The landscape ecological risk of green space in Kunshan City shows an upward trend. There is a correlation between the transformation of green space land use type and landscape ecological risk, reflecting the increasing ecological pressure of green space under human construction activities. Green space zoning regulation strategies based on the spatiotemporal evolution characteristics of risks are proposed in this study. [Ch, 4 fig. 5 tab. 28 ref.]

HUANG Xiaojie, DING Jinhua, WANG Daqing. Spatiotemporal evolution and regulation strategies of ecological risks in green space landscape in the water network area of southern Jiangsu[J]. Journal of Zhejiang A&F University, 2024, 41(6): 1283-1292. doi: 10.11833/j.issn.2095-0756.20240169
Citation: HUANG Xiaojie, DING Jinhua, WANG Daqing. Spatiotemporal evolution and regulation strategies of ecological risks in green space landscape in the water network area of southern Jiangsu[J]. Journal of Zhejiang A&F University, 2024, 41(6): 1283-1292. doi: 10.11833/j.issn.2095-0756.20240169
  • 在城市化快速发展的背景下,城镇建设用地的扩张导致生态空间衰减、系统结构失衡、生态功能下降等问题凸显[1],生态环境面临多重压力和干扰,引起的景观生态风险值得关注。绿色空间是城镇地域范围内对于改善区域生态环境、维持生态系统物质能量循环具有重要作用的生态空间,是由耕地、林地、草地、水域等不同土地单元镶嵌而成的复合生态系统[24]。当前,国内外学者对绿色空间的研究主要集中在绿色空间结构与功能[5]、景观格局动态演化[67]及生态环境效益[89]等方面。景观生态风险评价用于评估自然或人为因素干扰对生态系统及其组分产生不利影响的可能性及损失[10],基于景观格局指数构建景观生态风险评价模型能够定量揭示生态环境健康程度及风险压力的时空分布特征[11]。现有研究主要集中于景观生态风险的静态分析,对时空动态分析视角下景观生态风险演变特征的分析相对薄弱,且研究尺度集中在城市[1213]、城市群[1415]、流域[1617]等典型地区,对具有特殊地域特征的苏南水网地区的研究相对较少。

    苏南水网地区位于经济发达、人口密集的长江三角洲,河流、湖荡众多,水系纵横交错,形成了独特的地域生态空间特征。随着城镇建设用地的迅速扩张,苏南水网地区绿色空间日趋破碎化,生态系统稳定性下降。本研究以苏南水网地区江苏省昆山市为研究对象,利用2000、2010、2020年土地利用数据,定量测度其绿色空间景观格局变化引起的景观生态风险,并探究景观生态风险时空演变特征,依据风险等级转移变化特征划定绿色空间管控分区,提出分区调控策略,为优化水网地区空间景观布局,保护地区生态安全,合理开发绿色空间资源提供理论依据,也为地区景观生态风险管理提供决策支持。

    • 昆山市位于长江三角洲地区江苏省苏州市东部,31°06′~31°32′N,120°48′~121°09′E,全市下辖周庄镇、锦溪镇、淀山湖镇等10个镇,总面积为931 km2。根据《昆山市统计年鉴》,2000—2020年昆山市户籍总人数增加47.3万人,城镇化率由57.31%提升至78.95%,国内生产总值(GDP)增长4 075.96亿元,经济建设水平居于全国经济百强县首位。昆山市境内地势平坦,属北亚热带季风性湿润气候,四季分明,雨量充沛。境内河港纵横交错,湖荡星罗棋布,水域面积占16.4%,包含白莲湖、傀儡湖、明镜荡等湖荡,水网地区风貌特征明显。

    • 采用2000、2010、2020年3期 Landsat TM/OLI 遥感影像,数据集来源于地理空间数据云平台(http://www.gscloud.cn/),空间分辨率为30 m×30 m。利用ENVI 5.3软件对各期遥感影像数据进行校准、图像拼接裁剪等处理。参考中国科学院土地利用/土地覆盖分类系统及GB/T 21010—2017《土地利用现状分类》相关标准,结合苏南水网地区地域特点,将研究区划分为耕地、林地、草地、水域、建设用地和未利用地等6类土地利用类型,其中耕地、林地、草地和水域为绿色空间,建设用地和未利用地为非绿色空间。对解译后的土地利用类型数据进行精度验证,Kappa系数均>0.85,符合解译精度要求。

    • 为了便于景观生态风险指数的空间化表达,本研究基于ArcGIS的渔网分析功能划分景观生态风险小区。依据研究区面积大小及数据精度,采用等间距采样的方法将研究区划分为1.5 km×1.5 km正方形格网,共划分景观生态风险样本小区489个(图1),利用 Fragstats 4.2软件计算各个样本小区内的生态风险指数,作为每个风险小区中心点的景观生态风险值。

      Figure 1.  Schematic diagram of ecological risk area division

    • 景观格局指数是反映景观结构组成和空间配置特征的定量指标[18]。基于景观格局指数的生态风险评价方法能够有效评估生态系统受到外部干扰的强弱和内部抵抗力的大小[19]。根据相关研究成果[2021],依据景观格局与生态风险之间的关联,选取景观干扰度指数($ {E}_{i} $)、景观脆弱度指数($ {V}_{i} $)和景观损失度指数($ {R}_{i} $)来构建景观生态风险评价模型。

      各景观格局指数计算方法及生态学含义详见表1

      指数名称 计算方法 生态学含义
      土地利用生态风险指数
       (IERk)
      ${I_{{\text{ER}}k}} = \displaystyle \sum \limits_{i = 1}^N \dfrac{{{A_{ki}}}}{{{A_k}}} \times {R_i} $ Aki为第k个风险小区内土地利用类型i的面积;Ak为第k个风险小区的面积;Ri为第i类景观的景观损失度指数
      景观损失度指数(Ri) Ri=Ei×Vi Ei为景观干扰度指数,Vi为景观脆弱度指数
      景观干扰度指数($ {E}_{i} $) $ {E}_{i}={aC}_{i}+{bN}_{i}+{cD}_{i} $ 表示不同类型景观生态系统所受外界干扰的程度,主要与人类的开发活动有关。其中:$ a、b、c $分别为$ {C}_{i} $、$ {N}_{i}{\mathrm{、}D}_{i} $的权重,且$ a+b+c= $1,参考前人研究[11, 22],将$ a、b、c $分别赋值为0.5、0.3和0.2
      景观破碎度指数($ {C}_{i} $) $ {C}_{i}=\dfrac{{n}_{i}}{{A}_{i}} $ 表示景观被分割的破碎化程度,值越大表明景观破碎程度越高
      景观分离度指数($ {N}_{i} $) $ {N}_{i}=\dfrac{A}{2{A}_{i}}\sqrt{\dfrac{{n}_{i}}{A}} $ 表示某一景观类型中不同斑块间的分离程度,值越大表明景观空间分布越离散,景观结构稳定性越低。$ {n}_{i} $为景观类型$ i $的斑块个数;$ {A}_{i} $为景观类型$ i $的面积;$ A $为景观总面积
      景观优势度指数($ {D}_{i} $) $ {D}_{i}=\dfrac{\left(\dfrac{{n}_{i}}{N}+\dfrac{{q}_{i}}{Q}\right)}{4}+\dfrac{{A}_{i}}{2A} $ 表示斑块在景观中的地位,值越大代表斑块对景观格局演变影响越大。$ {q}_{i} $为景观类型$ i $斑块出现的样方数;$ Q $为样方总数;$ N $为斑块总数
      景观脆弱度指数($ {V}_{i} $) $ {V}_{i}={I}_{{\mathrm{LS}}}\times \left(1-{I}_{{\mathrm{LA}}}\right) $ 表示不同景观类型抵抗外界干扰的敏感程度。其中:ILS为景观敏感度指数,可通过景观干扰度指数和景观易损度指数相乘而得,景观易损度指数根据前人研究成果[2324],结合研究区实际情况赋以权重:未利用地为6,水域为5,耕地为4,草地为3,林地为2,建设用地为1;ILA为景观适应度指数,由斑块丰富密度指数、香农多样性指数、香农均匀度指数相乘而得。3种指数均由Fragstats软件计算而得

      Table 1.  Landscape pattern index and their calculation methods

    • 通过ArcGIS软件对遥感影像图进行分类处理,得到昆山市2000、2010和2020年3个时期土地利用类型图(图2),并统计得到各土地利用类型面积与占比(表2)。从分析结果来看:2000—2020年昆山市各绿色空间类型面积发生了较大变化,其中耕地面积下降明显,减少20 203.11 hm2,占比下降21.70%;水域面积在2000—2010年小幅增加,占比上升2.24%,2010—2020年水域面积明显下降,减少了5905.17 hm2,占比下降6.34%;林地面积共减少72.90 hm2,而草地面积则增加了143.64 hm2,两者在绿色空间中占比很小。总体而言,研究期间昆山市绿色空间总面积明显减少,反映了建设用地扩张不断侵占市域内的绿色空间,以耕地面积的缩减最为突出。

      Figure 2.  Land use type map of Kunshan City from 2000 to 2020

      土地利用类型 2000年 2010年 2020年
      面积/hm2 百分比/% 面积/hm2 百分比/% 面积/hm2 百分比/%
      绿色空间 耕地 68 884.11 73.98 51 240.51 55.03 48 681.00 52.28
      林地 122.85 0.13 112.59 0.12 49.95 0.05
      草地 36.36 0.04 78.66 0.08 180.00 0.19
      水域 15 156.36 16.28 17 247.87 18.52 11342.70 12.18
      合计 84 199.68 90.43 68 679.63 73.75 60253.65 64.70
      非绿色空间 建设用地 8 833.95 9.49 24 386.49 26.19 32828.04 35.26
      未利用地 81.36 0.09 48.96 0.05 33.39 0.05
      合计 8 915.31 9.58 24 435.45 26.24 32861.43 35.31

      Table 2.  Changes in the area of various land types in Kunshan City from 2000 to 2020

    • 为了进一步揭示昆山市绿色空间用地类型的时空演变规律,本研究采用土地利用转移矩阵对昆山市各用地类型之间的转移方向和转换数量进行分析,结果如表3所示。2000—2020年昆山市各绿色空间类型转移存在明显差异:耕地净转出量最大,总量达33 918.84 hm2,主要流向建设用地,转出面积达26 327.86 hm2,转出贡献率为77.62%,反映出建设用地侵占耕地现象普遍;水域面积整体呈现先小幅增加后逐渐减少的趋势,其中2000—2010年水域面积小幅增加了2 091.51 hm2,主要由耕地转入,2010—2020年,水域面积持续减少,主要向耕地和建设用地转出,转出总面积为7 150.64 hm2。总体来看,2000—2020年昆山市绿色空间类型转移以耕地和水域的转出为主,均主要转向建设用地。这反映出昆山市在经济社会快速发展下人为开发建设活动对绿色空间侵占现象较为明显,耕地和水域等绿色空间面临较大生态压力。

      时间段 土地利用类型 绿色空间/hm2 非绿色空间/hm2 转出合
      计/hm2
      面积变化
      合计/ hm2
      耕地 林地 草地 水域 建设用地 未利用地
      2000—2010 绿色空间 耕地 46 613.56 41.62 71.57 6 168.10 15 825.59 6.48 68 726.91 −17 577.88
      林地 46.23 41.28 0.07 29.54 5.62 0.00 122.74 −10.60
      草地 14.15 0.00 0.16 15.64 6.29 0.11 36.36 42.30
      水域 3 464.97 28.70 5.97 10 742.95 853.90 2.02 15 098.51 2 033.98
      非绿色空间 建设用地 975.40 0.53 0.90 174.01 7 672.25 0.20 8 823.28 15 544.60
      未利用地 34.72 0.00 0.00 2.25 4.23 40.15 81.36 −32.40
      转入合计 51 149.03 112.14 78.66 17 132.48 24 367.89 48.96 92 889.16
      时间段 土地利用类型 绿色空间/hm2 非绿色空间/hm2 转出合
      计/ hm2
      面积变化
      合计/ hm2
      耕地 林地 草地 水域 建设用地 未利用地
      2010—2020 绿色空间 耕地 39 356.15 8.54 127.00 1 161.04 10 502.27 6.64 51 161.64 −2 650.12
      林地 65.13 25.78 0.13 17.69 3.57 0.00 112.30 −62.47
      草地 2.17 0.00 1.46 0.00 74.73 0.30 78.66 101.34
      水域 6 482.98 14.97 37.18 9 906.54 667.66 6.89 17 116.24 −5 812.93
      非绿色空间 建设用地 2 592.59 0.53 14.22 217.65 21 538.87 1.19 24 365.05 8 439.75
      未利用地 12.50 0.00 0.00 0.38 17.71 18.37 48.96 −15.57
      转入合计 48 511.52 49.82 180.00 11 303.31 32 804.80 33.39 92 882.85
        说明:−表示无此项。

      Table 3.  Land class transfer matrix in Kunshan City from 2000 to 2020

    • 运用Fragstats软件计算得到昆山市2000、2010、2020年各绿色空间类型景观格局指数。统计结果表明:2000—2020年昆山市绿色空间景观格局发生了较大变化(表4)。①研究期间耕地破碎度和分离度指数显著上升,表明建设用地快速扩张,促使耕地空间分布趋于离散,破碎化程度加剧,景观优势度不断降低,受外界干扰程度增加。景观损失度逐年上升。②水域破碎度指数先下降后上升,总体呈上升趋势,景观优势度降低,且水域周边城镇较为密集,易受人为活动干扰,使景观脆弱程度不断增加,损失度上升。③林地破碎度、干扰度、脆弱度指数均先下降后上升,总体呈下降趋势,表明林地斑块分布逐渐聚集,景观结构稳定性提升。④草地破碎度指数先上升后下降,表明草地斑块在空间上趋于集聚与整合,抗外界干扰能力提高,景观脆弱度与损失度有所降低。

      土地利用类型 年份 斑块数量 斑块面积/hm2 破碎度 分离度 优势度 干扰度 脆弱度 损失度
      耕地 2000 1378 68 884.11 0.020 0.973 0.647 0.431 0.082 0.035
      2010 4401 51 240.51 0.086 0.987 0.602 0.459 0.087 0.040
      2020 4667 48 681.00 0.096 0.992 0.597 0.465 0.088 0.041
      林地 2000 494 122.85 4.021 1.000 0.092 2.329 0.222 0.516
      2010 355 112.59 3.153 1.000 0.076 1.892 0.180 0.340
      2020 172 49.95 3.443 1.000 0.046 2.031 0.193 0.392
      草地 2000 56 36.36 1.540 1.000 0.025 1.075 0.153 0.165
      2010 195 78.66 2.479 1.000 0.017 1.551 0.221 0.343
      2020 123 180.00 0.683 1.000 0.047 0.651 0.093 0.060
      水域 2000 4128 15 156.36 0.272 1.000 0.417 0.520 0.124 0.064
      2010 3566 17 247.87 0.207 1.000 0.399 0.483 0.115 0.056
      2020 3770 11 342.70 0.332 1.000 0.365 0.539 0.128 0.069

      Table 4.  Change of green space landscape pattern index in Kunshan City from 2000 to 2020

    • 基于景观生态风险评价指标计算结果,在ArcGIS 10.2中利用克里金插值法对昆山市生态风险值进行空间插值,得到昆山市绿色空间景观生态风险空间分布图,使用自然断点法将景观生态风险值(IERk)划分为5个等级:低生态风险(0<IERk≤0.026)、较低生态风险(0.026<IERk≤0.031)、中生态风险(0.031<IERk≤0.037)、较高生态风险(0.037<IERk≤0.041)和高生态风险(IERk>0.041),结果如图3,并统计得到不同景观生态风险等级的面积及占比(表5)。

      Figure 3.  Spatial distribution of ecological risks in green space landscape of Kunshan City from 2000 to 2020

      年份低风险区较低风险区中等风险区较高风险区高风险区
      面积/hm2比例/%面积/hm2比例/%面积/hm2比例/%面积/hm2比例/%面积/hm2比例/%
      20001 116.9025.661 830.8742.06918.0921.09345.157.93141.483.25
      2010642.7819.941 210.4137.55745.5623.13409.3212.70215.826.69
      2020452.7015.93961.0233.81692.6424.37455.5816.03280.269.86

      Table 5.  Area and proportion of landscape ecological risk level of green space in Kunshan City from 2000 to 2020

      2000—2020年昆山市绿色空间景观生态风险整体呈上升趋势,呈现“南北高,中间低”的空间分布特征。高、较高风险区面积明显上升,面积占比分别增加8.10%、6.61%,主要分布于淀山湖、白莲湖等湖荡密集地区,且不断向湖荡周围辐射扩张。该区域绿色空间类型以水域为主,由于围网养殖等人为活动对水域干扰程度加大,景观损失度逐年增加,使区域风险等级不断升高。中风险区面积小幅上升,面积占比增加3.28%,集中分布于渡头村、双洋潭等地区,并逐步沿较高风险区外围向四周扩散,区域内耕地、水域交错分布,受人为活动干扰较大,生态稳定性下降。较低、低风险区面积明显缩减,占比分别减少8.25%和9.73%,主要分布于研究区中部白渔潭村、荣家厍及北部范潭村、横泾等地区,且分布逐渐变得零散破碎,人为开发建设活动频繁,绿色空间不断减少,抗干扰能力减弱,景观生态风险值有增强趋势。

    • 借助景观生态风险等级变化分布(图4)分析2000—2020年期间研究区各风险等级的变化情况。①风险等级升高区域的面积为21 503.12 hm2,占绿色空间总面积的36.69%,其中较低风险区域上升为中风险的区域面积最大,为6 413.09 hm2,其次为中风险区域上升为较高风险区域。主要分布在白莲湖、明镜荡、汪洋荡等地区,区域内湖荡、耕地镶嵌分布,城镇建设用地的扩张使生态斑块破碎化程度加剧,生态结构和功能受到损害,生态系统稳定性和恢复力下降。②风险等级基本不变区域的面积为31 026.25 hm2,占绿色空间总面积的52.61%,其中较低风险区域面积最大,为13 102.74 hm2。主要分布在白渔潭村、荣家厍、范潭村片区等。该区域生态环境相对较好,生态系统结构和整体格局较为完整,对外界干扰具备一定的抵御能力,可维持基本的生态功能。③风险等级降低区域的面积为5 241.88 hm2,占绿色空间总面积的10.70%,其中中风险区域下降为较低风险区域面积最大,为2 925.90 hm2,其次为较低风险区域下降为低风险区域。在空间上集中在大渔新村、朱家湾村、黄家埭等地区。区域内具有较好的生态基底,生态斑块间连续性较强且受经济建设活动干扰较小,生态系统稳定性提高,能够提供较好的生态服务效益。

      Figure 4.  Change of landscape ecological risk level of green space in Kunshan City from 2000 to 2020

    • 基于2000—2020年昆山市绿色空间景观生态风险等级变化特征,将风险等级升高、不变和降低的区域分别划定为重点修复区、协调缓冲区和优化利用区。依据《苏州市“十四五”生态环境保护规划》《昆山市生态环境保护“十四五”规划》《昆山市国土空间总体规划(2021—2035)》等规划政策,结合调控分区的景观生态风险水平,提出有针对性的空间分区调控策略。

    • 重点修复区为景观生态风险等级升高的区域,主要表现为较低风险向中风险、中风险向较高风险转移。片区内绿色空间破碎度增加,生态系统稳定性下降,景观生态风险水平不断上升。应加强生态保育与生态修复,对淀山湖、白莲湖等主要核心水域开展生态治理与修复工程,提升水域生态涵养功能;系统梳理、串通河网水系,在河网沿线严格管控开发强度大的建设活动;对破碎的绿色空间斑块进行整合,特别是南部长白荡、明镜荡等水域密集地区,着力提升水网景观的连通性和抗干扰能力,维护绿色空间的完整性与稳定性。

    • 协调缓冲区为景观生态风险等级基本不变的区域,片区内绿色空间生态稳定性较强,能够抵御一定程度的外界干扰,景观生态风险维持在稳定水平。这些区域可作为生态缓冲地,提升绿色空间抗风险能力。通过强化河流水系、滨水绿带等生态廊道结构连通性[25],串联湖荡、农田大型生态斑块,构建水陆联动的网络化生态空间格局;加强傀儡湖、阳澄湖等生境敏感区的缓冲区建设,构建区域生态安全屏障,维护生态保护网络边界,增强区域景观生态风险缓冲能力。

    • 优化利用区为景观生态风险等级降低的区域,主要表现为中风险向较低风险、较低风险向低风险转换。片区内绿色空间生态系统结构较为完整,对外界干扰具有较强的适应能力,景观生态风险水平有所下降。应依托片区内良好的生态优势,适度优化建设,提升水网空间活力,维护生态系统的稳定性。首先明确生态保护红线边界,保护绿色空间健康稳定发展;其次对绿色空间进行分级分类管控,加强对城市生态森林公园、夏驾河湿地公园等核心生态资源的保护与管理,定期监测与评估生态用地的环境状况;同时在生态保护基础上优化建设,结合黄家埭等地区独特的水网空间优势开展科普教育、休闲游憩等服务,提升绿色空间的生态效益。

    • 本研究表明:绿色空间用地类型转变与景观生态风险具有关联性。研究期间昆山市南部水域及周边地区由于城镇用地扩张,耕地、水域等绿色空间面积持续减少,生态系统结构稳定性下降,景观生态风险等级呈上升趋势。这与于淑会等[26]、陈斌等[27]的研究结论一致。水网地区以纵横交错的河流、湖荡为主体,水域面积较大,易受外界城镇建设用地扩张的干扰而破碎化,景观脆弱度高。本研究结果表明:水域范围内的景观生态风险指数普遍较高。这与何钊全等[28]对延安市的研究存在一定差异。延安市地处黄土丘陵区,林地和耕地是优势景观类型,受经济发展和建设用地扩张影响较大,林地、耕地破碎化程度加剧,抗干扰能力下降,景观损失度增加,使林地与耕地的景观生态风险值较高。

      本研究在快速城镇化背景下,基于景观生态风险评价,加强绿色空间分区规划调控,对提升区域生态安全水平,优化国土空间结构,促进区域可持续发展具有一定理论指导意义。但研究仍存在一定局限性:①研究侧重从景观空间结构变化视角来评价绿色空间景观生态风险状况,对社会、经济等层面影响因素研究不足,还需进一步完善景观生态风险影响因素和驱动机制研究。②生态过程具有复杂性和抽象性,其具体演变过程很难做到定量表述。需要对生态风险展开多尺度分析,深入探讨景观格局生态风险和生态过程的耦合关系,为区域风险管理提供更加科学的依据。

    • ①2000—2020年昆山市绿色空间总面积持续减少,其中耕地面积缩减最多;水域面积先小幅增加后持续减少,总体呈减少趋势;林地、草地面积占比较小,维持相对平稳。研究区用地类型转换主要表现为耕地和水域转向建设用地。②2000—2020年昆山市绿色空间景观格局变化特征明显,耕地空间分布在建设用地扩张影响下趋于分散,破碎化程度加大,损失度增加;水域破碎度指数先下降后上升,总体破碎度呈增大趋势,景观受外界干扰增加;林地破碎度、干扰度和脆弱度呈下降趋势,斑块分布呈集聚态势;草地破碎度指数先上升后下降,总体破碎度呈下降趋势,空间分布趋于集聚,景观损失度降低。③2000—2020年昆山市绿色空间景观生态风险等级总体呈上升趋势,其中高风险区、较高风险区面积显著扩大,占比分别增加8.10%、6.61%,空间分布上主要集中在南部淀山湖、白莲湖等水域密集地区,并有进一步向外围蔓延发展的趋势;较低风险区、低风险区面积缩减明显,占比分别下降8.25%和9.73%;景观生态风险以低风险等级向更高一级转变为主,绿色空间受人工建设干扰生态风险不断增强。④依据景观生态风险等级变化特征将研究区划分为重点修复区、协调缓冲区和优化利用区。

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