Correlations between spectral variability at different spatial scales.

<p>All correlations were statistically significant at p<0.001.</p

General flowchart of processing of the remotely sensed data.

<p>General flowchart of processing of the remotely sensed data.</p

Correlations between the CASI bands.

<p>All correlations were statistically significant at p<0.05.</p

Relating Remotely Sensed Optical Variability to Marine Benthic Biodiversity

<div><p>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.</p> </div

Correlations between spectral variability (SV) at different spatial scales and environmental variables.

<p>Statistically significant correlations at p<0.05 are shown in boldface.</p

Study area.

<p>Filled circles and triangles indicate the location of sampling stations. Raster is the PCA image (3 first components) of CASI bands 5 to 16. PCA components 1, 2, and 3 are superimposed as red, green, and blue composite raster bands, respectively. PCA – principal component analysis; CASI – Compact Airborne Spectrographic Imager.</p

Pearson correlation coefficients between SV and biological variables at different spatial scales (m).

<p>Statistically significant correlations at p<0.05 are shown in boldface. S – species richness.</p

Central wavelengths of the CASI bands.

<p>Bands in boldface were used in the data analysis.</p

Predicting Species Cover of Marine Macrophyte and Invertebrate Species Combining Hyperspectral Remote Sensing, Machine Learning and Regression Techniques

<div><p>In order to understand biotic patterns and their changes in nature there is an obvious need for high-quality seamless measurements of such patterns. If remote sensing methods have been applied with reasonable success in terrestrial environment, their use in aquatic ecosystems still remained challenging. In the present study we combined hyperspectral remote sensing and boosted regression tree modelling (BTR), an ensemble method for statistical techniques and machine learning, in order to test their applicability in predicting macrophyte and invertebrate species cover in the optically complex seawater of the Baltic Sea. The BRT technique combined with remote sensing and traditional spatial modelling succeeded in identifying, constructing and testing functionality of abiotic environmental predictors on the coverage of benthic macrophyte and invertebrate species. Our models easily predicted a large quantity of macrophyte and invertebrate species cover and recaptured multitude of interactions between environment and biota indicating a strong potential of the method in the modelling of aquatic species in the large variety of ecosystems.</p></div

Particulate backscattering <i>b</i>_<i>b</i>p(532).

<p>Plotted for 4 depths < 20 m per station, as a function of (A) Chla, (B) POC, (C) TSM, and (D) the proportion of ISM in TSM. Drawn and dotted lines are the linear regression fit to the data for spring (green), summer (blue) and all plotted stations, respectively. Eight observations from stations with a stronger coastal influence (marked as crosses) were excluded from the linear fit for spring.</p