Assessing Regional Ecosystem Conditions Using Geospatial Techniques-A Review

被引:6
作者
Zhang, Chunhua [1 ]
Wang, Kelin [2 ,3 ]
Yue, Yuemin [2 ,3 ]
Qi, Xiangkun [2 ,3 ]
Zhang, Mingyang [2 ,3 ]
机构
[1] Algoma Univ, Dept Biol, Marie, ON P6A 2G4, Canada
[2] Chinese Acad Sci, Inst Subtrop Agr, Key Lab Agroecol Proc Subtrop Reg, Changsha 410125, Peoples R China
[3] Chinese Acad Sci, Huanjiang Observat & Res Stn Karst Ecosyst, Huanjiang 547100, Peoples R China
基金
中国国家自然科学基金;
关键词
ecosystem conditions; regional assessment; landscape pattern; remote sensing; spatial big data; ENVIRONMENTAL VULNERABILITY ASSESSMENT; BIG DATA APPLICATIONS; ECOLOGICAL VULNERABILITY; HEALTH-ASSESSMENT; DECISION-ANALYSIS; SATELLITE IMAGERY; NIGHTTIME LIGHTS; SOCIAL MEDIA; SUSTAINABILITY; INDICATORS;
D O I
10.3390/s23084101
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Ecosystem conditions at the regional level are critical factors for environmental management, public awareness, and land use decision making. Regional ecosystem conditions may be examined from the perspectives of ecosystem health, vulnerability, and security, as well as other conceptual frameworks. Vigor, organization, and resilience (VOR) and pressure-stress-response (PSR) are two commonly adopted conceptual models for indicator selection and organization. The analytical hierarchy process (AHP) is primarily used to determine model weights and indicator combinations. Although there have been many successful efforts in assessing regional ecosystems, they remain affected by a lack of spatially explicit data, weak integration of natural and human dimensions, and uncertain data quality and analyses. In the future, regional ecosystem condition assessments may be advanced by incorporating recent improvements in spatial big data and machine learning to create more operative indicators based on Earth observations and social metrics. The collaboration between ecologists, remote sensing scientists, data analysts, and scientists in other relevant disciplines is critical for the success of future assessments.
引用
收藏
页数:15
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