Mapping Baltic Sea shallow water environments with airborne remote sensing

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.     

Photo-library method for mapping seagrass biomass

The creation of seagrass biomass maps by diving/snorkelling is time-consuming and expensive. This paper presents a method for estimating seagrass dry weight using a photo-library of classes of differing seagrass biomass. Field data were collected at seagrass beds in Ngederrak Reef, Palau, in 2006. Photos of 25 × 25 cm quadrats were taken prior to the collection the above-ground biomass for determination of biomass dry weight. Fifteen classes of seagrass biomass and substrate type were identified. The dry weight for each class of seagrass was measured in laboratory. A photo-library was created for biomass classification where each in situ quadrat photo is accompanied with seagrass dry weight of the sample and a photo of the sorted sample taken in laboratory. The photo-library of quadrats was then used to estimate seagrass biomass on photos gathered along 100 m long transects at 2 m intervals. This procedure was conducted by three different observers. The seagrass dry weight estimates were consistent between interpreters even if one of the interpreters had no experience in seagrass research. This approach allows quick collection of seagrass dry weight data over large areas. The method can be used for creating seagrass biomass maps by snorkelling/diving and/or for calibrating and validating biomass maps created by remote sensing.