Priority list of biodiversity metrics to observe from space

生物多样性 地理空间分析 遥感 计算机科学 地理 领域(数学) 生态系统服务 环境资源管理 生态系统 生态学 环境科学 生物 数学 纯数学
作者
Andrew K. Skidmore,Nicholas C. Coops,Elnaz Neinavaz,Abebe Mohammed Ali,Michael E. Schaepman,Marc Paganini,W. Daniel Kissling,Petteri Vihervaara,Roshanak Darvishzadeh,Hannes Feilhauer,Miguel Fernández,Néstor Fernández,Noel Gorelick,Ilse R. Geijzendorffer,Uta Heiden,Marco Heurich,Donald Hobern,Stefanie Holzwarth,Frank Müller‐Karger,Ruben Van De Kerchove
出处
期刊:Nature Ecology and Evolution [Nature Portfolio]
卷期号:5 (7): 896-906 被引量:261
标识
DOI:10.1038/s41559-021-01451-x
摘要

Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales. Remote sensing of geospatial biodiversity patterns is an important complement to field observations. This priority list suggests how remote sensing observations can be better integrated into the essential biodiversity variables.
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