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3 October 2019 New perspectives in coastal monitoring
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Monitoring the near-shore environment is always a challenging task. A very detailed representation of the relief is necessary to understand and properly characterize many of coastal processes. Traditional in situ measurements with topographic equipment provide spatially sparse datasets and they could not map in detail the spatial variability in wave and current fields, the shallow water bathymetry and the beach morphology. As an alternative solution to the in situ measurements remote sensing data and particularly high resolution satellite data and airphotos have benn used in coastal monitoring in order to assess the morphological evolution of the coastline or to measure volume changes in the coastal area. More recently, the use of airborne LIDAR systems has been of high interest in coastal area mapping due to the very high accuracy that can be achieved but on the other hand, the very high campaign costs are dissuasive in the use of LIDAR over small study areas. As high-resolution digital surface models (DSMs) and orthophoto maps became a necessity in order to map with precision all the variations in coastal environments unmanned aerial vehicles (UAV) photogrammetry offers an alternative solution to the acquisition of high accuracy spatial data along the coastline. The UAVs present serious capabilities such as: almost real-time applicability, flexible survey planning, acquisition of high resolution data, low operational cost, and capability of data collection in difficult accessible environments. This paper presents the feasibility of the use of a small commercial UAV and an unmanned surface vehicle (USV) for coastal monitoring and shallow water mapping. Optical and acoustic remote sensing data were acquired and processed and the results are presented.
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Konstantinos G. Nikolakopoulos, Dimitris Sardelianos, Elias Fakiris, and George Papatheodorou "New perspectives in coastal monitoring", Proc. SPIE 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X, 1115602 (3 October 2019);


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