桂西南喀斯特-北部湾海岸带生态环境脆弱性时空分异与驱动机制研究

doi:10.12082/dqxxkx.2021.200278

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (3): 456-466.doi: 10.12082/dqxxkx.2021.200278

• 地理空间分析综合应用 • 上一篇下一篇

桂西南喀斯特-北部湾海岸带生态环境脆弱性时空分异与驱动机制研究

张泽¹^,²^,⁴(), 胡宝清¹^,²^,³^,^*(), 丘海红¹^,²^,⁴, 邓雁菲¹^,²^,⁴

1.北部湾环境演变与资源利用教育部重点实验室,南宁 530001
2.广西地表过程与智能模拟重点实验室,南宁 530001
3.南宁师范大学地理与海洋研究院南宁 530001
4.南宁师范大学地理科学与规划学院,南宁 530001

收稿日期:2020-06-03 修回日期:2020-08-30 出版日期:2021-03-25 发布日期:2021-05-25
通讯作者: *胡宝清（1966- ）,男,江西临川人,博士,研究方向为脆弱环境演变与整治。E-mail: hbq1230@gxtc.edu.cn
*胡宝清（1966- ）,男,江西临川人,博士,研究方向为脆弱环境演变与整治。E-mail: hbq1230@gxtc.edu.cn
作者简介:张泽（1996- ）,男,内蒙古乌兰浩特人,硕士生,研究方向为地理信息技术。E-mail: 3014930258@qq.com
基金资助:
国家自然科学基金项目(42071135);国家自然科学基金项目(41930537);广西自然科学基金项目(2016GXNSFGA380007);广西科技计划项目(AD19110142)

Spatio-temporal Differentiation and Driving Mechanism of Ecological Environment Vulnerability in Southwest Guangxi Karst-Beibu Gulf Coastal Zone

ZHANG Ze¹^,²^,⁴(), HU Baoqing¹^,²^,³^,^*(), QIU Haihong¹^,²^,⁴, DENG Yanfei¹^,²^,⁴

1. Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning 530001, China
2. Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning 530001, China
3 Institute of geography and oceanography, Nanning normal university, Nanning 530001, China
4. School of Geography and Planning, Nanning normal university, Nanning 530001, China

Received:2020-06-03 Revised:2020-08-30 Online:2021-03-25 Published:2021-05-25
Supported by:
National Natural Science Foundation of China(42071135);National Natural Science Foundation of China(41930537);Guangxi Natural Science Foundation project(2016GXNSFGA380007);Guangxi Science and Technology Project(AD19110142)

摘要/Abstract

摘要：

为揭示生态环境脆弱性的时空分异和驱动因子,本研究在山江海视角下,以桂西南喀斯特-北部湾海岸带为典型研究区,运用空间主成分分析法,地理探测器模型,结合生态环境脆弱性综合指数,系统分析桂西南喀斯特-北部湾海岸带生态环境脆弱性的时空分异特征及驱动机制。结果表明：① 研究区2008、2013、2018年脆弱性指数分别为0.54、0.61、0.69,多年平均值为0.61,整体处于中度脆弱,在空间上,由城市中心向四周逐渐降低的趋势;在时间上,生态环境脆弱等级呈微恶化趋势; ② 在单因子作用中6个驱动因子对生态环境脆弱性的解释力强度为汛期降雨量（0.457）>植被覆盖度（0.384）>高温季节温度（0.311）>废水入海量（0.248）>NPP（0.184）>人口密度（0.036）。在多因子交互中,只有汛期降水量和NPP, NPP和高温季节温度、废水入海量和NPP呈非线性增强,其余的交互作用均为双线性增强,而且汛期降水量和植被覆盖度的单因子影响较强,交互作用后影响也是最强（0.679）,说明了汛期降水量和植被覆盖度为该区域的主要驱动因子。

关键词: 山江海视角, 生态环境脆弱性, 时空分异, 空间主成分分析法, 生态环境脆弱性指数, 地理探测器, 驱动机制, 桂西南喀斯特-北部湾海岸带

Abstract:

Characterizing the spatiotemporal changes in ecological vulnerability and reveal its driving factors is important, especially for unique geographical environment that connects mountains, rivers, and sea together, In this study, we chose the Southwest Guangxi karst-Beibu gulf coastal zone as the typical study area. We combined the spatial principal component analysis and geo-detector model to quantify the ecological vulnerability index and further characterizes the spatiotemporal changes in ecological vulnerability and evaluates its driving mechanism. Our results show that: (1) the vulnerability index of the study area was 0.54, 0.61 and 0.69, respectively for 2008, 2013, and 2018, with a multi-year average of 0.61. The general ecological vulnerability level was moderately fragile with a slightly worse trend over time; and (2) the top six explanatory driving factors of ecological vulnerability were rainfall in flood season (0.457), vegetation coverage (0.384), temperature in hot seasons (0.311), waste water input (0.248), NPP (0.184), and population density (0.036). For the interaction between driving factors, only rainfall in flood season and NPP, NPP and temperature in hot seasons, waste water input and NPP showed positive nonlinear relationships, while the rest had linear relationships. Flood season rainfall and vegetation coverage had the strongest effect on ecological vulnerability and a strongest interaction (0.679) with each other. Our study illustrates that flood season rainfall and vegetation coverage are the main driving factors of ecological vulnerability in the study area.

Key words: view of mountains, rivers and seas, ecological vulnerability, spatial-temporal differentiation, spatial principal component analysis, ecological environment vulnerability index, geographical detector, driving mechanism, Southwest Guangxi karst - Beibu gulf coastal zone

张泽, 胡宝清, 丘海红, 邓雁菲. 桂西南喀斯特-北部湾海岸带生态环境脆弱性时空分异与驱动机制研究[J]. 地球信息科学学报, 2021, 23(3): 456-466.DOI:10.12082/dqxxkx.2021.200278

ZHANG Ze, HU Baoqing, QIU Haihong, DENG Yanfei. Spatio-temporal Differentiation and Driving Mechanism of Ecological Environment Vulnerability in Southwest Guangxi Karst-Beibu Gulf Coastal Zone[J]. Journal of Geo-information Science, 2021, 23(3): 456-466.DOI:10.12082/dqxxkx.2021.200278

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参考文献 45

[1]	Nandy S, Singh C, DAS K K, et al. Environmental vulnerability assessment of eco-development zone of Great HimalayanNational Park, Himachal Pradesh, India[J]. Ecological Indicators, 2015,57:182-195.
[2]	李永化, 范强, 王雪, 等. 基于SRP模型的自然灾害多发区生态环境脆弱性时空分异研究——以辽宁省朝阳县为例[J]. 地理科学, 2015,35(11):1452-1459.
	[ Li Y H, F Q, W X, et al. Spatial and temporal differentiation of ecological vulnerability in natural disaster prone areas based on SRP model: A case study of chaoyang county, liaoning province[J]. Geosciences, 2015,35(11):1452-1459. ]
[3]	杨强. 基于遥感的榆林地区生态环境脆弱性研究[D]. 南京:南京大学, 2012.
	[ Yang Q. Research on ecological vulnerability in yulin area based on remote sensing[D]. Nanjing: Doctoral Dissertation of Nanjing University, 2012. ]
[4]	李平星, 樊杰. 基于VSD模型的区域生态系统脆弱性评价——以广西西江经济带为例[J]. 自然资源学报, 2014,29(5):779-788.
	[ Li Q X, Fan J. Regional ecosystem vulnerability assessment based on VSD model: A case study of guangxi xijiang economic belt[J]. Journal of Natural Resources, 2014,29(5):779-788. ]
[5]	牛文元. 生态环境脆弱带ECOTONE的基础判定. 生态学报, 1989,9(2):97-105.
	[ Niu W Y. Basic determination of ECOTONE in fragile ecological environment[J]. Acta Biota Sinica, 1989,9(2):97-105. ]
[6]	王钰, 胡宝清. 西江流域生态环境脆弱性时空分异及其驱动机制研究[J]. 地球信息科学学报, 2018,20(7):947-956.
	[ Wang Y, Hu B Q. Spatiotemporal differentiation of ecological vulnerability in xijiang river basin and its driving mechanism[J]. Journal of Geosciences, 2018,20(7):947-956. ]
[7]	吴琼. 基于景观格局的辽宁海岸带生态环境脆弱性评价[D]. 大连:辽宁师范大学, 2014.
	[ Wu Q. Ecological vulnerability assessment of liaoning coastal zone based on landscape pattern[D]. Dalian: Liaoning Normal University, 2014. ]
[8]	廖炜, 李璐, 吴宜进, 等. 丹江口库区土地利用变化与生态环境脆弱性评价[J]. 自然资源学报, 2011,26(11):1879-1889.
	[ Liao W, l L, W Y J, et al. Land use change and ecological environment vulnerability assessment in danjiangkou reservoir area[J]. Chinese Journal of Natural Resources, 2011,26(11):1879-1889. ]
[9]	靳毅, 蒙吉军. 生态环境脆弱性评价与预测研究进展[J]. 生态学杂志, 2011,30(11):2646-2652.
	[ Jin Y, Meng J J. Research progress of ecological vulnerability assessment and prediction[J]. Journal of Ecology, 2011,30(11):2646-2652. ]
[10]	张圆圆, 毛爽, 张淑伟. 2000—2017年龙门山断裂带生态环境脆弱性演变研究[J]. 天津农业科学, 2020,26(2):22-28.
	[ Zhang Y Y Mao S Zhang S W, Study on ecological vulnerability evolution of longmen mountain fault zone from 2000 to 2017[J]. Tianjin Agricultural Sciences, 2020,26(2):22-28. ]
[11]	陈桃, 包安明, 郭浩, 等. 中亚跨境流域生态环境脆弱性评价及其时空特征分析——以阿姆河流域为例[J]. 自然资源学报, 2019,34(12):2643-2657.
	[ Chen T, Bao A M, Guo H, et al. Ecological vulnerability assessment and spatial-temporal analysis of transboundary river basins in central Asia: A case study of the amu darya basin[J]. Journal of Natural Resources, 2019,34(12):2643-2657. ]
[12]	马骏, 李昌晓, 魏虹, 等. 三峡库区生态环境脆弱性评价[J]. 生态学报, 2015,35(21):7117-7129.
	[ Ma J, li C X, Wei H, et al. Ecological vulnerability assessment of the three gorges reservoir area[J]. Acta Ecologica Sinica, 2015,35(21):7117-7129. ]
[13]	Hou K, Li X X, Zhang J. GIS analysis of changes in ecological vulnerability using a SPCA model in the loess plateau of northern Shaanxi, China[J]. International Journal of Environmental Research and Public Health, 2015,12(4):4292-4305. pmid: 25898407
[14]	陆海燕, 孙桂丽, 李路, 等. 基于VSD模型的新疆生态环境脆弱性评价[J]. 新疆农业科学, 2020,57(2):292-302.
	[ Lu H Y, Sun G L, Li L, et al. Evaluation of ecological vulnerability in xinjiang based on VSD model[J]. Xinjiang AgriCultural Sciences, 2020,57(2):292-302. ]
[15]	康永辉, 解建仓, 黄伟军, 等. 广西大石山区农业干旱成因分析及脆弱性评价[J]. 自然灾害学报, 2014,23(3):24-32.
	[ Kang Y H, Jie J C, Huang W J, et al. Genetic analysis and vulnerability assessment of agricultural drought in dashi mountain area of guangxi[J]. Journal of Natural Disasters, 2014,23(3):24-32. ]
[16]	袁烽迪, 张溪, 魏永强. 青藏高原生态屏障区生态环境脆弱性评价研究[J]. 地理空间信息, 2018,16(4):67-69.
	[ Yuan F D, Zhang X, Wei W Q. Assessment of ecological environment vulnerability in the ecological barrier area of the qinghai-tibet plateau[J]. Geospatial Information, 2018,16(4):67-69. ]
[17]	吴春生, 黄翀, 刘高焕, 等. 基于模糊层次分析法的黄河三角洲生态环境脆弱性评价[J]. 生态学报, 2018,38(13):584-4595.
	[ Wu C S, Huang C, Liu G H, et al. Qingsheng evaluation of ecological vulnerability in The Yellow River Delta based on fuzzy analytic hierarchy Process[J]. Acta Ecologica Sinica, 2008,38(13):4584-4595. ]
[18]	于伯华, 吕昌河. 青藏高原高寒区生态环境脆弱性评价[J]. 地理研究, 2011,30(12):2289-2295.
	[ Yu B H, Lu C H. Ecological vulnerability assessment in the cold region of the qinghai-tibet plateau[J]. Geography Research, 2011,30(12):2289-2295. ]
[19]	Cao C X, Yang B, Xu M, et al. Evaluation and analysis of post-seismic restoration of ecological security in Wenchuan using remote sensing and GIS[J]. Geomatics, Natural Hazards and Risk, 2016,7(6):1919-1936.
[20]	Martins V N, Silva D S, Cabral P. Social vulnerability assessment to seismic risk using multicriteria analysis: The case study of Vila Franca doCampo ( So Miguel Island, Azore, Portugal)[J]. Natural Hazards, 2012,62(2):385-404.
[21]	Eckert S, Jelinek R, Zeug G, et al. Remote sensing-based assessment of tsunami vulnerability and risk in Alexandria, Egypt[J]. Applied Geography, 2012,32(2):714-723.
[22]	吴健生, 罗宇航, 王小玉, 等. 城市滑坡灾害生态风险不确定性分析与风险管理——以深圳市为例[J/OL].生态学报, 2020(11):1-10.
	[ Wu J S, Luo Y H, Wang X Y, et al. Uncertainty analysis and Risk management of urban landslide ecological risk: A case study of Shenzhen[J/OL]. Acta Ecologica Sinica, 2020(11):1-10. ]
[23]	王铁军, 赵礼剑, 张溪. 青藏高原生态屏障区生态环境综合评价方法探讨[J]. 测绘通报, 2018(9):112-116.
	[ Wang T J, Zhao L J, Zhang X. A comprehensive evaluation method of the ecological environment in the ecological barrier area of the qinghai-tibet plateau[J]. Bulletin of Surveying and Mapping, 2018(9):112-116. ]
[24]	张行, 陈海, 史琴琴, 刘迪, 等. 陕西省景观生态环境脆弱性时空演变及其影响因素[J]. 干旱区研究, 2020,37(2):496-505.
	[ Zhang X, Chen H, Shi Q Q, et al. Temporal and spatial evolution of landscape ecological vulnerability in shaanxi province and its influencing factors[J]. Arid Region Research, 2020,37(2):496-505. ]
[25]	刘晶晶. 集中连片特困区生态环境脆弱性评价研究[D]. 华中师范大学, 2019.
	[ Liu J J. Study on ecological Environment Vulnerability Assessment in contiguous poverty-stricken areas[D]. Central China Normal University, 2019. ]
[26]	Song G D, Chen Y, Tian M R, et al. The ecological vulnerability evaluation in southwestern mountain region of Chinabased on GIS and AHP method[J]. Procedia Environmental Sciences, 2010,2:465-475.
[27]	杨俊, 关莹莹, 李雪铭, 等. 城市边缘区生态环境脆弱性时空演变———以大连市甘井子区为例[J]. 生态学报, 2018,38(3):778-787.
	[ Yang J, Guan Y Y, Li X M, et al. Spatial and temporal evolution of ecological vulnerability in urban fringe areas: A case study of ganjingzi district, dalian city[J]. Acta Ecologica Sinica, 2018,38(3):778-787. ]
[28]	范斐, 孙才志. 辽宁省海洋经济与陆域经济协同发展研究[J]. 地域研究与开发, 2011,30(2):59-63.
	[ Fan F, Sun C Z. Research on the coordinated development of Marine economy and land economy in liaoning province[J]. Regional Research and Development, 2011,30(2):59-63. ]
[29]	徐静, 王泽宇. 中国陆海统筹绩效时空分异及影响因素——基于脆弱性视角的分析[J]. 地域研究与开发, 2019,38(2):25-30.
	[ Xu J, Wang Z Y. Temporal and spatial differentiation and influencing factors of China's land-sea overall planning performance: Analysis from the perspective of vulnerability[J]. Regional Research and Development, 2019,38(2):25-30. ]
[30]	刘伯超, 唐凤德, 张连辉. 辽宁省农业生态环境脆弱性相关分析[J]. 沈阳农业大学学报(社会科学版), 2009,11(1):34-38.
	[ Liu B C, Tang F D, Zhang L H. Correlation analysis of agricultural ecological environment vulnerability in liaoning province[J]. Journal of Shenyang Agricultural University (Social Science Edition), 2009,11(1):34-38. ]
[31]	周振民. 黄河下游引黄灌区农业生态环境脆弱性评价及对策[J]. 云南农业大学学报:自然科学版, 2007,22(1):127-131.
	[ Zhou Z M. Evaluation and countermeasures of agricultural eco-environment frangibility in theirrigation districts of the downstream of Yellow River[J]. Journal of Yunnan Agricultural University: Natural Science, 2007,22(1):127-131. ]
[32]	姚昆, 余琳, 刘光辉, 等. 基于SRP模型的四川省生态环境脆弱性评价[J]. 物探化探计算技术, 2017,39(2):291-295.
	[ Yao K, Yu L, Liu G H, et al. Evaluation of ecological environment vulnerability in sichuan province based on SRP model[J]. Geophysical and Geochemical Computing Technology, 2017,39(2):291-295. ]
[33]	王雪梅, 席瑞. 基于GIS的渭干河流域生态环境脆弱性评价[J]. 生态科学, 2016,35(4):166-172.
	[ Wang X M, Xi R. Vulnerability assessment of ecological environment in weigan river basin based on GIS[J]. Ecological Science, 2016,35(4):166-172. ]
[34]	徐明德, 李艳春, 何娟, 等. 区域生态环境脆弱性的GIS“分解-合成”评价分析——以浑源县为例[J]. 地球信息科学学报, 2011,13(2):198-204.
	[ Xu M D, Li Y C, He J, et al. Decomposition and synjournal evaluation of regional ecological environment vulnerability in GIS: A case study of hunyuan county[J]. Journal of Earth Information Science, 2011,13(2):198-204. ]
[35]	梁丽清. 不同尺度区域特征表述的分析评价和整合重构[D]. 福州:福建师范大学, 2013.
	[ Liang L Q. Analysis, evaluation and integration reconstruction of regional feature expressions at different scales[D]. Fuzhou: Fujian Normal University, 2013. ]
[36]	陈燕丽, 莫伟华, 罗永明, 等. 基于气候信息的喀斯特地区植被EVI模拟[J]. 农业工程学报, 2015,31(9):187-194.
	[ Chen Y L, Mo W H, Luo Y M, et al. EVI simulation of vegetation in karst areas based on climate information[J]. Chinese journal of agricultural engineering, 2015,31(9):187-194. ]
[37]	穆少杰, 游永亮, 朱超, 等. 中国西北部草地植被降水利用效率的时空格局[J]. 生态学报, 2017,37(5):1458-1471.
	[ Mu S J, You T L, Zhu C, et al. Spatial and temporal pattern of precipitation utilization efficiency of grassland vegetation in northwest China[J]. Acta Ecologica Sinica, 2017,37(5):1458-1471. ]
[38]	熊繁. 农业发展转型区土地利用景观格局优化及评价[D]. 重庆:重庆师范大学, 2016.
	[ Xiong F. Land use landscape pattern optimization and evaluation in agricultural development transition zone[D]. Chongqing: Chongqing Normal University, 2016. ]
[39]	宋福春. 土地利用总体规划决策机制研究[D]. 长春:吉林大学, 2006.
	[ Song F C. A study on the decision-making mechanism of overall land use planning[D]. Changchun: Jilin University, 2006. ]
[40]	陈燕丽, 罗永明, 莫伟华, 等. MODIS NDVI与MODIS EVI对气候因子响应差异[J]. 自然资源学报, 2014,29(10):1802-1812.
	[ Chen Y L, Luo Y M, Mo W H, et al. Differences in response of MODIS NDVI and MODIS EVI to climate factors[J]. Chinese Journal of Natural Resources, 2014,29(10):1802-1812. ]
[41]	王钰, 胡宝清. 基于GIS的广西西江流域脆弱性评价及综合整治研究[J]. 长江科学院院报, 2016,35(9):48-53.
	[ Wang Y, Hu B Q. Gis-based vulnerability assessment and comprehensive improvement of Xijiang River basin in Guangxi[J]. Journal of Yangtze River Academy of Sciences, 2016,35(9):48-53. ]
[42]	周永娟, 仇江啸, 王姣, 等. 三峡库区消落带生态环境脆弱性评价[J]. 生态学报, 2010,30(24):6726-6733.
	[ Zhou Y J, Qiu J X, Wang J, et al. Evaluation of ecological environment vulnerability in the three gorges reservoir zone[J]. Acta ecologica sinica, 2010,30(24):6726-6733. ]
[43]	宁文怡. 基于GIS的喀斯特山区地貌与土地利用分形关系研究[D]. 南宁:广西师范学院, 2016.
	[ Ning W Y. Research on the Fractal Relationship between landform and Land use in karst mountainous areas based on GIS[D]. Nanning: Guangxi Normal University, 2016. ]
[44]	贾晶晶, 赵军, 王建邦, 等. 基于SRP模型的石羊河流域生态环境脆弱性评价[J]. 干旱区资源与环境, 2020,34(1):34-41.
	[ Jia J J, Zhao J, Wang J B, et al. Ecological vulnerability assessment of shiyang river basin based on SRP model[J]. Resources and Environment in Arid Regions, 2020,34(1):34-41. ]
[45]	王劲峰, 徐成东. 地理探测器:原理与展望[J]. 地理学报, 2017,72(1):116-134.
	[ Wang J F, Xu C H. Geographical detector: Principle and prospect[J]. Acta Geographica Sinica, 2017,72(1):116-134. ]

目标层	指标层	指标计算说明
生态环境脆弱性评价指标	高程	裁剪、数据转换、分区统计
	地形起伏度	高程数据模型得到地形起伏度、分区统计
	水土流失面积	统计数据
	植被覆盖度	最大合成法（MVC）合成为年数据、分区统计
	废水入海量	统计数据
	废水排放量	统计数据
	汛期降水量	反比距离权重法（IDW）进行插值
	高温季节温度	克里金法（Kriging）进行插值
	人口密度	区域人口数量/区域土地总面积
	人均海岸带面积	海岸带人口数量/海岸带土地总面积
	人均耕地面积	区域人口数量/区域耕地面积
	净初级生产力（NPP）	提取、图像镶嵌、裁剪、数据转换、投影转换
	经济密度	区域国民生产总值/区域土地总面积
	海岸带经济密度	海岸带国民经济生产总值/海岸带土地总面积

判断依据	交互作用
P(X1∩X2)<min(P(X1),P(X2))	非线性减弱
min(P(X1),P(X2))<P(X1∩X2)<max(P(X1),P(X2))	单线性减弱
P(X1∩X2)>max(P(X1),P(X2))	双线性增强
P(X1∩X2)= P(X1)+P(X2)	相互独立
P(X1∩X2)>P(X1)+P(X2)	非线性增强

年份	主成分系数	主成分
年份	主成分系数	PC1	PC2	PC3	PC4	PC5
2008	特征值λ	0.04263	0.02768	0.01454	0.00565	0.00129
	贡献率/%	41.95250	26.57840	12.21970	4.15090	4.01350
	累计贡献率/%	41.95250	68.53090	80.75030	84.90150	88.91500
2013	特征值λ	0.04629	0.02459	0.01359	0.00676	0.00193
	贡献率/%	44.43210	23.16610	11.34610	5.25850	1.53820
	累计贡献率/%	44.43210	67.59820	78.99430	84.25280	85.79100
2018	特征值λ	0.03954	0.02267	0.01428	0.00971	0.00565
	贡献率/%	38.36110	21.67230	13.88610	8.78620	6.99840
	累计贡献率/%	38.36110	60.03340	73.91950	82.70570	91.75730

脆弱性等级	生态环境脆弱性指数标准化值	脆弱程度	生态特征
Ⅰ	<0.2	潜在脆弱	生态系统结构完整,无生态异常出现,服务功能很好,承受生态压力小,对各类干扰敏感性弱
Ⅱ	0.2~0.4	微度脆弱	生态系统结构较为完善,存在潜在的生态异常,服务功能良好,承受生态压力较小,对各类干扰敏感性较弱
Ⅲ	0.4~0.6	轻度脆弱	生态系统结构尚可维持,出现少量生态异常,服务功能退化,承受生态压力接近阈值,对各类干扰敏感性中等
Ⅳ	0.6~0.8	中度脆弱	生态系统结构出现缺陷,生态异常较多,服务功能不全,承受生态压力较大,对各类干扰敏感性较强
Ⅴ	>0.8	重度脆弱	生态系统功能退化严重,生态异常集中连片出现,服务功能完全丧失,承受生态压力大,对各类干扰敏感性强

X1∩X2	P(X1∩X2)	判断	交互作用
汛期降水量∩植被覆盖度	0.679	P(X1∩X2)>max(P(X1),P(X2))	双线性增强
汛期降水量∩高温季节温度	0.607	P(X1∩X2)>max(P(X1),P(X2))	双线性增强
汛期降水量∩废水入海量	0.541	P(X1∩X2)>max(P(X1),P(X2))	双线性增强
汛期降水量∩NPP	0.648	P(X1∩X2)>P(X1)+P(X2)	非线性增强
汛期降水量∩人口密度	0.433	P(X1∩X2)>max(P(X1),P(X2))	双线性增强
植被覆盖度∩高温季节温度	0.442	P(X1∩X2)>max(P(X1),P(X2))	双线性增强
植被覆盖度∩废水入海量	0.429	P(X1∩X2)>max(P(X1),P(X2))	双线性增强
植被覆盖度∩NPP	0.406	P(X1∩X2)>max(P(X1),P(X2))	双线性增强
植被覆盖度∩人口密度	0.327	P(X1∩X2)>max(P(X1),P(X2))	双线性增强
高温季节温度∩废水入海量	0.453	P(X1∩X2)>max(P(X1),P(X2))	双线性增强
高温季节温度∩NPP	0.537	P(X1∩X2)>P(X1)+P(X2)	非线性增强
高温季节温度∩人口密度	0.233	P(X1∩X2)>max(P(X1),P(X2))	双线性增强
废水入海量∩NPP	0.479	P(X1∩X2)>P(X1)+P(X2)	非线性增强
废水入海量∩人口密度	0.247	P(X1∩X2)>max(P(X1),P(X2))	双线性增强
NPP∩人口密度	0.108	P(X1∩X2)>max(P(X1),P(X2))	双线性增强

桂西南喀斯特-北部湾海岸带生态环境脆弱性时空分异与驱动机制研究

Spatio-temporal Differentiation and Driving Mechanism of Ecological Environment Vulnerability in Southwest Guangxi Karst-Beibu Gulf Coastal Zone

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