Abstract
Biodiversity is important in maintaining ecosystem viability, and the availability of adequate biodiversity data is a prerequisite for the sustainable management of natural resources. As such, there is a clear need to map biodiversity at high spatial resolutions across large areas. Airborne and spaceborne optical remote sensing is a potential tool to provide such biodiversity data. The spectral variation hypothesis (SVH) predicts a positive correlation between spectral variability (SV) of a remotely sensed image and biodiversity. The SVH has only been tested on a few terrestrial plant communities. Our study is the first attempt to apply the SVH in the marine environment using hyperspectral imagery recorded by Compact Airborne Spectrographic Imager (CASI). All coverage-based diversity measures of benthic macrophytes and invertebrates showed low but statistically significant positive correlations with SV whereas the relationship between biomass-based diversity measures and SV were weak or lacking. The observed relationships did not vary with spatial scale. SV had the highest independent effect among predictor variables in the statistical models of coverage-derived total benthic species richness and Shannon index. Thus, the relevance of SVH in marine benthic habitats was proved and this forms a prerequisite for the future use of SV in benthic biodiversity assessments.
Highlights
Biodiversity plays an important role in maintaining ecosystem integrity and services over long periods of time [1,2,3,4,5]
The study demonstrated that all coveragebased benthic diversity measures had statistically significant positive correlations with spectral variability (SV) indicating the relevance of spectral variation hypothesis (SVH) in marine benthic habitats
The relevance of SVH in marine benthic habitat forms a prerequisite for the future use of SV in benthic biodiversity assessments
Summary
Biodiversity plays an important role in maintaining ecosystem integrity and services over long periods of time [1,2,3,4,5]. The cumulative effects of multiple human stressors such as resource extraction, pollution, habitat destruction, spread of non-indigenous species, and climate change have ever-increasing impacts on biological diversity. Mapping biodiversity at regional to global scales is increasingly important in order to sustainably manage natural resources and preserve biodiversity [6]. Time series of high resolution biodiversity maps can be used to detect change in natural systems and to assess the effects of management decisions on biodiversity patterns over space and time [7]. Mapping biodiversity by means of traditional sampling methods is expensive and time-consuming. Traditional sampling-point-wise field work is not suitable for covering extensive areas in high detail
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