水利水电技术2017，Vol.48Issue(9)：1-9,23,10.DOI:10.13928/j.cnki.wrahe.2017.09.001
基于大数据的湿地生态系统服务价值评估
Evaluation of wetland ecosystem services based on big data
摘要
Abstract
As the wetland ecosystem service has significant spatial and temporal specificity and comprehensive characteristics,the value of it is quite difficult to be accurately quantified and assessed to the conventional assessment method.Aiming at the data analysis method of wetland ecosystem service value,both the advances and the challenges faced by the assessment of the wetland ecosystem service are analyzed and the big data wetland observation system based on the observation network and remote sensing technology is discussed herein,while the methods of the dimensional analysis,spatial analysis and attributive analysis of the big data for the assessment of the wetland ecosystem service value are described as well,thus a coupling analysis framework of the wetland dimensional-spatial-attributive big data for the assessment of the wetland ecosystem service is put forward.Furthermore,the future development trend of the study made on the assessment of the value of the wetland ecosystem service function in the big data era is prospected at last.The result shows that the wetland ecosystem observation network can be used to obtain the dynamic long time series,and remote sensing of earth observation can realize integrated observation of the earth and the sky.The combination of the two methods is helpful to construct the observation system of wetland ecological big data.The service value of coastal wetland ecosystem in China is 501.0 billion yuan.Taking the China coastal wetland as a case study for empirical research,the study result has a reference for both the protection and management of the wetland ecosystem.关键词
湿地/生态系统/服务功能/价值评估/大数据/生态价值Key words
wetland/ecosystem/service functions/value evaluation/big data/envionmental values分类
资源环境引用本文复制引用
高崟,崔丽娟,王发良,李伟,雷茵茹,孙宝娣..基于大数据的湿地生态系统服务价值评估[J].水利水电技术,2017,48(9):1-9,23,10.基金项目
滨海滩涂湿地生态恢复与功能提升技术项目 （）
林业公益性行业科研专项(201404305) （201404305）
国家自然科学基金项目(50809005) （50809005）
