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The Baltic Sea represents an optically complex case 2 water type, where high concentrations of water column constituents limit acquisition of benthic information. Different preprocessing steps were applied to the hyperspectral compact airborne spectrographic imager (CASI) image to extract as much useful benthic information as possible. Atmospheric correction, minimum noise fraction transform, and sun-glint correction were performed to acquire water surface reflectance data. Additionally, a water column removal procedure was applied to acquire bottom reflectance data. Although retrieved CASI water surface reflectance spectra generally matched the magnitudes and shapes of in situ measured spectra, then the applied water column correction algorithm did not yield accurate bottom reflectance spectra. Therefore, both benthic habitat and bathymetry maps were retrieved from the CASI sea surface image data set. An image-based supervised classification method produced a good quality benthic habitat map from the shallow Pakri study area (depth < 3.0 m) with an overall accuracy of 80%. A site-specific algorithm was developed for the bathymetry retrieval utilizing a green-yellow CASI band ratio. Validation of the bathymetry map for depths shallower than 4.0 m revealed an R 2 value of 0.88 and a root-mean-square error of 0.32 m. The assessment of the benthic substrate detectability limits in the Baltic Sea revealed that, at the wavelength of deepest light penetration (near 570 nm), the depth restriction for CASI benthic substrate detection was 7.6 m for sand, 5.0 m for green macroalgae, 3.0 m for higher order vegetation, and 3.1 m for brown macroalgae. The depth limit to which bathymetric mapping is practical in our study site was estimated to be around 3.5 to 4.0 m. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Abstract:The structure of benthic macrophyte habitats is known to indicate the quality of coastal water. Thus, a large-scale analysis of the spatial patterns of coastal marine habitats enables us to adequately estimate the status of valuable coastal marine habitats, provide better evidence for environmental changes and describe processes that are behind the changes. Knowing the spatial distribution of benthic habitats is also important from the coastal management point of view.A big challenge in remote sensing mapping of benthic habitats is to define appropriate mapping classes that are also meaningful from the ecological point of view. In this study, the benthic habitat classification scheme was defined for the study areas in the relatively turbid north-eastern Baltic Sea coastal environment. Two different classification methods-image-based and the spectral library-method were used for image classification. The image-based classification method can provide benthic habitat maps from coastal areas, but requires extensive field studies. An alternative approach in image classification is to use measured and/or modelled spectral libraries. This method does not require fieldwork at the time of image collection if preliminary information about the potential benthic habitats and their spectral properties, as well as variability in optical water properties exists from earlier studies. A spectral library was generated through radiative transfer model HydroLight computations using measured reflectance spectra from representative benthic substrates and water quality measurements.Our previous results have shown that benthic habitat mapping should be done at high spatial resolution, owing to the small-scale heterogeneity of such habitats in the Estonian OPEN ACCESSRemote Sens. 2013, 5 2452 coastal waters. In this study, the capability of high spatial resolution hyperspectral airborne a Compact Airborne Spectrographic Imager (CASI) sensor and a high spatial resolution multispectral WorldView-2 satellite sensor were tested for mapping benthic habitats. Initial evaluations of habitat maps indicate that image-based classification provides higher quality benthic maps compared to the spectral library method.

Abstract. Many macroalgal species potentially have distinctive spectral signatures detectable using remote sensing. In order to map the spatial distribution of these species, their spectral properties have to be quantified and statistical differences between species need to be assessed. In the present study, we collected a spectral library of the key benthic macrophyte species in the Baltic Sea area and presented the methodology that allows quantifying statistical differences between their reflectance spectra. The results indicate that three broad groups of algaegreen, brown, and red algae-can be separated based on their optical signatures. In general, the between-species differences are too small to allow easy recognition of benthic algae based on their untransformed reflectance spectra. However, the distinctness of the studied species and taxa improves if standardized reflectance values are used. The best indicative spectral range was at 530 to 570 nm for the separation of species and of larger taxonomic units. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.