摘要
生态环境健康评价对于促进生态保护、引导区域经济发展战略、调整和衡量生态文明建设结果具有重要意义。综合指标体系模型是现今国内外主流的评价方法,然而,如何构建不同地区通用、普适性强的指标体系,如何从众多繁杂的指标通过客观、科学的方法自动筛选出能表征研究区特点的重要指标是目前所面临的难点。本文集成压力-状态-响应模型(PressureState-Response,PSR)和生态层次网络模型(Ecological Hierarchy Network,EHN),并考虑部分指标所存在的信息重叠,建立了目标层-准则层-要素层-指标层-同类指标层的5层网状指标体系,提出了基于优劣解距离法(Technique for Order Preference by Similarity to Ideal Solution,TOPSIS)的同类指标层约简和基于目标优化理论的指标层约简相结合的两段式自适应指标约简模式。结合两者完成面向生态环境健康评价的自适应指标约简模型的构建,并在地理大数据的支持下,应用于云南、福建、京津冀、陕西、湖北、新疆和吉林7个生态环境迥异区域的2001—2021年生态环境健康评价。研究结果表明:(1)利用两段式自适应指标约简模型所筛选出的中选指标可以较好地体现不同地区生态系统特点,中选指标中权重靠前的指标被较多文献应用于各地区指标体系构建,说明所构建的指标体系和两段式自适应指标约简模型具有较好的普适性和合理性,有效避免了人为指标体系构建的主观性;(2)7个地区生态环境健康状况的空间分布和时间变化趋势符合实际情况,并且能与现有的文献、资料进行互相印证,从侧面证实了本文所提出模型的有效性。本文所提出的模型可为其他领域指标体系构建和筛选提供参考,也为大范围不同区域的生态环境健康评价提供方法支撑。
A healthy ecological environment forms a crucial foundation for the sustainable development of both nations and humanity.In the domain of ecological environment assessment,the comprehensive indicator system model represents the mainstream evaluation approach,both domestically and internationally.The extensive application of big geodata within this context offers significant potential for addressing ecological problems characterized by vast scales,intricate processes,and a variety of influencing factors.However,as the acquisition of big geodata becomes increasingly accessible,the coverage of the index system has significantly expanded,raising the pivotal issue of objectively and scientifically selecting crucial indicators capable of representing the distinctive characteristics of the study area.This challenge is particularly critical in today’s ecological health assessment.The Pressure-State-Response(PSR)model offers a causal perspective,comprehensively considering the systemic relationships between the ecological environment and human socioeconomic activities.The Ecological Hierarchy Network(EHN)model is capable of reflecting the overlap and interconnections between upper and lower-layer indicators.In this study,by integrating the frameworks of PSR and EHN and taking into account the potential information overlap from multiple available parameters,we established a five-layer networked indicator system consisting of the Target Layer,Criteria Layer,Element Layer,Indicator Layer,and Homogeneous Indicator Layer.We also proposed a two-stage adaptive indicator reduction model that combines Homogeneous Indicator Layer reduction using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)and Indicator Layer reduction based on target optimization theory.Combining both approaches,we developed an adaptive indicator reduction model tailored for ecological environmental health assessment.Leveraging big geodata comprising remote sensing thematic products,topography,meteorology,soil,and population information,we applied the proposed model to assess the ecological health of seven ecologically diverse regions in China,including Yunnan,Fujian,Beijing-Tianjin-Hebei,Shaanxi,Hubei,Xinjiang,and Jilin during the period 2001—2021.The results show that:(1)The selected indicators obtained through the two-stage indicator adaptive reduction model effectively reflected the distinct characteristics of ecosystems in different regions.Furthermore,indicators with higher weights among the selected ones have been widely employed in constructing indicator systems across various regions.These findings highlighted the universality and rationality of both the constructed indicator system and the two-stage indicator adaptive reduction model,effectively mitigating the subjectivity associated with manual indicator system construction;(2)The spatial distribution and temporal trends of the ecological environment health of the seven regions aligned with real-world conditions and were corroborated by existing literature and data,which indicated the effectiveness of the model proposed in this study.The proposed models presented in this paper can serve as a reference for constructing indicator systems and selecting indicators in other domains and provide methodological support for ecological environment health assessment across diverse regions on a large scale.
作者
陈建辉
汪小钦
孔令凤
CHEN Jianhui;WANG Xiaoqin;KONG Lingfeng(Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education,Fuzhou University,The Academy of Digital China(Fujian),Fuzhou 350108,China)
出处
《地球信息科学学报》
EI
CSCD
北大核心
2024年第5期1193-1211,共19页
Journal of Geo-information Science
基金
中国科学院战略性先导科技专项(XDA23100500)
国家重点研发计划项目(2017YFB0504203)。
关键词
指标约简
网状指标体系
优劣解距离法
目标优化模型
最大化偏差模型
CRITIC法
熵权法
生态层次网络模型
压力-状态-响应模型
indicators reduction
network index system
superior and inferior solution distance method
target optimization model
maximizing deviation model
Criteria Importance Through Intercriteria Correlation(CRITIC)method
entropy method
ecological hierarchy framework
Pressure-State-Response(PSR)framework
作者简介
陈建辉(1998-),男,福建平潭人,硕士生,主要从事资源环境遥感。E-mail:774248895@qq.com;通信作者:汪小钦(1972-),女,福建古田人,博士,研究员,主要从事遥感信息模型与方法、环境与资源遥感GIS应用建模等研究。E-mail:wangxq@fzu.edu.cn。
