Sidescan sonar meets airborne and satellite remote sensing: challenges of a multi-device seafloor classification in extreme shallow water intertidal environments

Author:

Capperucci R. M.ORCID,Kubicki A.,Holler P.,Bartholomä A.

Abstract

AbstractTidal ecosystems like the Wadden Sea are particularly valuable for their ecological and economic importance. Here, the natural dynamics of the abiotic and biotic processes is threatened by the human pressure, and great efforts are made on mapping and monitoring programs. Remote sensing techniques (e.g., satellite and airborne sources) are commonly used on land and intertidal areas, whereas hydroacoustic devices are deployed in the subtidal zones. The overlap of hydroacoustics (sidescan sonar) and airborne Lidar data in such sensitive transitional zone (inter- to subtidal) is rather uncommon. In order to test the limitations of both techniques in extremely shallow waters (0.7 m min, water depth) and to find the most efficient methods for the spatial classification of intertidal areas, a portion of the backbarrier tidal flat of Norderney was investigated. Lidar bathymetric data were used for extracting high resolution morphological information. Sidescan sonar mosaics were collected in two following years under contrasting weather conditions. An expert classification based on sidescan sonar backscatter intensity, seafloor texture, morphology, and surface sediment data subdivided the research area into 10 classes. The outcomes were compared with an existing RapidEye-based classification. The tested methods showed both advantages and limitations, which were discussed based on statistical analyses. Satellite and Lidar approaches were most suitable for mapping biogenic features (e.g., shellfish beds) over large areas, whereas sidescan sonar was superior for detail detection and discrimination of morpho-sedimentary regions. As an outlook, it is postulated to perform ground-truthed hydroacoustic mapping on small testing areas, and to use the obtained classification for training satellite-based classification algorithms.

Funder

Senckenberg Gesellschaft für Naturforschung (SGN)

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Geotechnical Engineering and Engineering Geology,Environmental Science (miscellaneous),Oceanography

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