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
Contact: HU Baoqing
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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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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