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 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.

Remote sensing is a widely used method for studying shallow marine and fresh water benthic environments. The method can provide information about the benthic habitats and bathymetry provided that the water is optically shallow. Optically shallow waters occur wherever the reflectance of the bottom can be detected from above the water’s surface and are determined by the water depth, optical properties of the water column, and underlying benthos.¹

Photosynthetic pigments may indicate the health and productivity of vegetation and thereby are among the most important targets of the remote-sensing science. We studied the relationship between macroalgae pigment concentration measured in situ and spectral reflectance, to develop predictive remote-sensing methods for macroalgal pigments. The measurements of spectral reflectance of macroalgae were made using both a field portable spectrometer Ramses built by TriOS GmbH (Germany) and a laboratory hyperspectral imaging device HySpex built by Norsk Elektro Optikk (Norway). Our results showed that differences in total chlorophyll (Chl-a + b) concentrations resulted in the consistent change of spectral reflectance for studied brown (Fucus vesiculosus) and green (Cladophora glomerata, Ulva intestinalis) macroalgae species. Charophytes (Chara aspera, Chara horrida) were also studied, and the relationship was much weaker for this taxon. If spectral indices predicted relatively well the concentration of Chl-a + b (R² = 0.64–0.73) and the carotenoid to total chlorophyll ratio (Car:Chl-a + b, R² = 0.80) across the five studied macroalgae species, then the concentration of chlorophyll a (Chl-a), chlorophyll b (Chl-b), and carotenoids (Car) were more difficult to model (R² = 0.004–0.51). The HySpex imaging system yielded systematically better results in predicting pigment concentrations compared to the Ramses spectroradiometer. By using traditional assessment of pigment concentration along with the Hyspex imaging device, we were able to build models with a capability to predict the spatial patterns of pigment concentration for Baltic Sea macroalgae.

Maps of submerged aquatic vegetation (SAV) are of primary importance for the sustainable management of coastal areas and serve as a basis for fundamental ecological studies. Various water column correction (WCC) models are successfully applied in clear Case-1 waters to compensate for the variable water depth effect. The performance of the WCC in less clear Case-2 waters is rarely assessed. In this study, the performance and applicability of model-based WCC algorithms in the complex Baltic Sea were analyzed. The bottom reflectance was retrieved from the Compact Airborne Spectrographic Imager (CASI) water surface reflectance by applying the Maritorena and Lee WCC algorithms. The Maritorena model retrieved bottom spectra that showed large variations in reflectance magnitudes. The Lee model was more successful in retrieving reasonable spectral magnitudes, although only in a rather narrow wavelength region (550–600 nm). Shorter and longer spectral regions were significantly overcorrected, resulting in unrealistic spectral shapes. Sensitivity analysis indicated that slight under- or overestimation of water depth and water column constituents affect retrieval of correct bottom spectra in Case-2 waters. To assess the performance of WCC models in improving the SAV quantification, the surface reflectance, as well as the retrieved bottom reflectance, were correlated with the corresponding in situ estimated SAV percent cover (%SAV). Although the quality of the Lee WCC model was not considered high, the spectral region least affected by the input parameters variations (550–600 nm) can be used for the SAV quantification. Application of the Lee model provided better results in %SAV assessment than not performing the WCC correction.

Coastal macroalgae worldwide provide multiple ecological functions and support vital ecosystem services. Thereby, it is important to monitor changes in the extent of benthic macroalgal cover. However, as in situ sampling is costly and time-consuming, areal estimates of macroalgal species cover are often based only on a limited number of samples. This low sampling effort likely yields very biased estimates, as macroalgal communities are often characterized by large spatial variability at multiple spatial scales. Moreover, ecological time series are often short-term, making it impossible to assess changes in algal communities over decades and relate this to different human pressures and/or climate change. The Landsat series satellites have operated for 40 years. In the current study, we tested if the Landsat sensors could be used for mapping the cover of shallow water benthic macroalgae. This study was carried out at two sites in the West Estonian Archipelago, in the northeastern Baltic Sea. Our results show that the Landsat imagery accurately reflected both spatial and temporal variability in benthic algal cover. To conclude, the current methodology can be used to improve the existing assessments of areal macroalgal cover, or to estimate the cover values, in areas and times lacking ecological observations.

It is known that the structure of benthic macrophyte and invertebrate habitats indicate the quality of coastal water. Thus, a large-scale analysis of the spatial patterns of coastal marine habitats makes it possible to adequately estimate the status of valuable coastal marine habitats, provide better evidence for environmental changes, and describe the processes behind the changes. Knowing the spatial distribution of benthic habitats is also important from the coastal management point of view. Our previous results clearly demonstrated that remote sensing methods can be used to map water depth and distribution of taxonomic groups of benthic algae (e.g., red, green, and brown algae) in the optically complex coastal waters of the Baltic Sea. We have as well shown that benthic habitat mapping should be done at high spatial resolution owing to the small-scale heterogeneity of such habitats in Estonian coastal waters. Here we tested the capability of high spatial resolution hyperspectral airborne image in its application for mapping benthic habitats. A big challenge is to define appropriate mapping classes that are also meaningful from the ecological point of view. In this study two benthic habitat classification schemes—broader level and finer level—were defined for the study area. The broader level classes were relatively well classified, but discrimination among the units of the finer classification scheme posed a considerable challenge and required a careful approach. Benthic habitat classification provided the highest accuracy in the case of the Spectral Angle Mapper classification method applied to a radiometrically corrected image. Further processing levels, such as spatial filtering and glint correction, decreased the classification accuracy.