Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study of Ecosystem Functioning and Conservation at Large Spatial Scales

Schrodt, Franziska; De La Barreda Bautista, Betsabe; Williams, Christopher; Boyd, Doreen S.; Schaepman-Strub, Gabriela; Santos, Maria J.

Abstract

© The Editor(s) (if applicable) and The Author(s) 2020. Assessing patterns and processes of plant functional, taxonomic, genetic, and structural biodiversity at large scales is essential across many disciplines, including ecosystem management, agriculture, ecosystem risk and service assessment, conservation science, and forestry. In situ data housed in databases necessary to perform such assessments over large parts of the world are growing steadily. Integrating these in situ data with remote sensing (RS) products helps not only to improve data completeness and quality but also to account for limitations and uncertainties associated with each data product. Here, we outline how auxiliary environmental and socioeconomic data might be integrated with biodiversity and RS data to expand our knowledge about ecosystem functioning and inform the conservation of biodiversity. We discuss concepts, data, and methods necessary to assess plant species and ecosystem properties across scales of space and time and provide a critical discussion of outstanding issues.

Citation

Schrodt, F., De La Barreda Bautista, B., Williams, C., Boyd, D. S., Schaepman-Strub, G., & Santos, M. J. (2020). Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study of Ecosystem Functioning and Conservation at Large Spatial Scales. In Remote Sensing of Plant Biodiversity (449-484). Springer. https://doi.org/10.1007/978-3-030-33157-3_17

• Online Publication Date: Jun 23, 2020
• Publication Date: Jun 23, 2020
• Deposit Date: Apr 1, 2023
• Publicly Available Date: Apr 5, 2023
• Publisher: Springer
• Pages: 449-484
• Book Title: Remote Sensing of Plant Biodiversity
• Chapter Number: 17
• ISBN: 9783030331566
• DOI: https://doi.org/10.1007/978-3-030-33157-3_17
• Keywords: Citizen science; data integration; Hypertemporal; Hyperspectral; In situ; Phenology; Pollination; Socioeconomic; Spectral information; Plant functional traits
• Public URL: https://nottingham-repository.worktribe.com/output/4847396
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-030-33157-3_17
• Additional Information: First Online: 23 June 2020