Spectral variability hypothesis
The Spectral Variability Hypothesis (SVH) states that spatial variability in the reflectance of vegetated surfaces relates to plant species richness. It has been originally coined by Palmer et al. (2000) and states that "species richness will be positively related to any objective measure (e.g. standard deviation) of the variation in the spectral characteristics of a remotely sensed image".[1] The underlying assumption is that habitats differ in reflectance and if there are more habitats in an area, higher numbers of species are to be expected. The hypothesis has later also been termed the Spectral Variation Hypothesis.[2] With high spatial resolution, variability in reflectance may also be a direct expression of plant individuals belonging to different species.[3] The Spectral Variability Hypothesis was well received in the research community due to its apparent straightforwardness.[4]
Tests of the hypothesis showed considerable variation in the connectedness between spatial variation of reflectance and plant species richness.[5] This variation can be attributed to several reasons. A major problem already noted by Palmer (2002) is the fact that different habitats support different species numbers so the relationship between habitat heterogeneity and species numbers differs depending on which habitats are involved.[2] This means that spatial heterogeneity in reflectance does not show a generalizable link to species richness. Accordingly, the Spectral Variability Hypothesis has been rejected for coarse scale levels where high plant species richness can occur in areas of low spectral variability and vice versa.[5] Other factors impacting the relationship between spectral heterogeneity and species numbers are differences in extent and selection of the areas of investigation, spatial grain (pixel size of the applied remote sensing data), spectral resolution (including the width of the bands and the spectral region covered by the bands) and also the timing of an investigation[4]
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References[edit]
- ^ Palmer, Michael W.; Wohlgemuth, Thomas; Earls, Peter; Arévalo, José Ramón; Thompson, Steven (2000). "Opportunities for Long-Term Ecological Research at the Tallgrass Prairie Preserve, Oklahoma" (PDF). In Lajtha, Kate; Vanderbilt, Kristin (eds.). Cooperation in Long Term Ecological Research in Central and Eastern Europe. ILTER Regional Workshop, 22–25 June 1999, Budapest, Hungary. Corvallis, OR: Oregon State University. pp. 123–128. Retrieved 17 May 2021.
- ^ a b Palmer, Michael W.; Earls, Peter G.; Hoagland, Bruce W.; White, Peter S.; Wohlgemuth, Thomas (2002). "Quantitative tools for perfecting species lists". Environmetrics. 13 (2): 121–137. doi:10.1002/env.516.
- ^ Carlson, Kimberly M.; Asner, Gregory P.; Hughes, R. Flint; Ostertag, Rebecca; Martin, Roberta E. (2007). "Hyperspectral remote sensing of canopy biodiversity in Hawaiian lowland rainforests". Ecosystems. 10 (4): 536–549. doi:10.1007/s10021-007-9041-z. S2CID 11483599.
- ^ a b Rocchini, D.; Balkenhol, N.; Carter, G.A.; Foody, G.M.; Gillespie, T.W.; He, K.S.; Kark, S.; Levin, N.; Lucas, K.; Luoto, M.; Nagendra, H.; Oldeland, J.; Ricotta, C.; Southworth, J.; Neteler, M. (2010). "Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges". Ecological Informatics. 5 (5): 318–329. doi:10.1016/j.ecoinf.2010.06.001.
- ^ a b Schmidtlein, Sebastian; Faßnacht, Fabian E. (2017). "The spectral variability hypothesis does not hold across landscapes". Remote Sensing of Environment. 192: 114–125. Bibcode:2017RSEnv.192..114S. doi:10.1016/j.rse.2017.01.036.