Seagrass meadows play a vital role in coastal ecosystems health as constitute an important pillar of the coastal environment. So far, regional scale habitat mapping was implemented with the use of freely available medium scale satellite images (Sentinel-2 or Landsat-8). The Unmanned Aerial Systems (UAS) have increase the spatial resolution of the observation from meter to sub-decimeter. Using sub-decimeter imagery, seagrass can be mapped in great detail revealing significant habitat species and detect new habitat patterns. In the present study, we suggest a multi-scale image analysis methodology consisting of georeferencing, atmospheric and water column correction and Object- Based Image Analysis (OBIA). OBIA process is performed using nearest neighborhood and fuzzy rules as classifiers in three major classes, a) seagrass, b) shallow areas with soft bottom and c) shallow areas with hard bottom (reefs). UAS very high-resolution data treated as in situ observations and used for training the classifiers and for accuracy assessment. The methodology applied in two satellite images Sentinel-2 and Landsat-8 with 10m and 30m spatial resolution respectively, at Livadi beach, Folegandros Island, Greece. The results show better classification accuracies in Sentinel-2 data than in Landsat-8. There was a great difficulty in the detection of the reef habitat in satellite images because it covered a small area. Reef habitat was clearly detected only in the UAS data. In conclusion, the present study highlights the necessity of new high precision geospatial data for examining the habitat detection accuracies on satellite images of different resolutions.
The significance of coastal habitat mapping lies in the need to prevent from anthropogenic interventions and other factors. Until 2015, Landsat-8 (30m) imagery were used as medium spatial resolution satellite imagery. So far, Sentinel-2 satellite imagery is very useful for more detailed regional scale mapping. However, the use of high resolution orthophoto maps, which are determined from UAV data, is expected to improve the mapping accuracy. This is due to small spatial resolution of the orthophoto maps (30 cm). This paper outlines the integration of UAS for data acquisition and Structure from Motion (SfM) pipeline for the visualization of selected coastal areas in the Aegean Sea. Additionally, the produced orthophoto maps analyzed through an object-based image analysis (OBIA) and nearest-neighbor classification for mapping the coastal habitats. Classification classes included the main general habitat types, i.e. seagrass, soft bottom, and hard bottom The developed methodology applied at the Koumbara beach (Ios Island - Greece). Results showed that UAS’s data revealed the sub-bottom complexity in large shallow areas since they provide such information in the spatial resolution that permits the mapping of seagrass meadows with extreme detail. The produced habitat vectors are ideal as reference data for studies with satellite data of lower spatial resolution.
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