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Rocchini, Duccio ; Santos, Maria J. ; Ustin, Susan L. ; Féret, Jean‐Baptiste ; Asner, Gregory P. ; Beierkuhnlein, Carl ; Dalponte, Michele ; Feilhauer, Hannes ; Foody, Giles M. ; Geller, Gary N. ; Gillespie, Thomas W. ; He, Kate S. ; Kleijn, David ; Leitão, Pedro J. ; Malavasi, Marco ; Moudrý, Vítězslav ; Müllerová, Jana ; Nagendra, Harini ; Normand, Signe ; Ricotta, Carlo ; Schaepman, Michael E. ; Schmidtlein, Sebastian ; Skidmore, Andrew K. ; Šímová, Petra ; Torresani, Michele ; Townsend, Philip A. ; Turner, Woody ; Vihervaara, Petteri ; Wegmann, Martin ; Lenoir, Jonathan:
The Spectral Species Concept in Living Color.
In: Journal of Geophysical Research: Biogeosciences.
Bd. 127
(2022)
Heft 9
.
ISSN 2169-8961
DOI: https://doi.org/10.1029/2022JG007026
Abstract
Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the ‘spectral species’ concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.