Print Email Facebook Twitter Permanent Laser Scanner and Synthetic Aperture Radar Data Title Permanent Laser Scanner and Synthetic Aperture Radar Data: Correlation Characterisation at a Sandy Beach Author di Biase, V. (TU Delft Mathematical Geodesy and Positioning) Kuschnerus, M. (TU Delft Optical and Laser Remote Sensing) Lindenbergh, R.C. (TU Delft Optical and Laser Remote Sensing) Date 2022 Abstract In recent years, our knowledge of coastal environments has been enriched by remotely sensed data. In this research, we co-analyse two sensor systems: Terrestrial Laser Scanning (TLS) and satellite-based Synthetic Aperture Radar (SAR). To successfully extract information from a combination of different sensors systems, it should be understood how these interact with the common environment. TLS provides high-spatiotemporal-resolution information, but it has high economic costs and limited field of view. SAR systems, despite their lower resolution, provide complete, repeated, and frequent coverage. Moreover, Sentinel-1 SAR images are freely available. In the present work, Permanent terrestrial Laser Scanning (PLS) data, collected in Noordwijk (The Netherlands), are compared with simultaneous Sentinel-1 SAR images to investigate their combined use on coastal environments: knowing the relationship between SAR and PLS data, the SAR dataset could be correlated to beach characteristics. Meteorological and surface roughness have also been taken into consideration in the evaluation of the correlation between PLS and SAR data. A generally positive linear correlation factor up to 0.5 exists between PLS and SAR data. This correlation occurs for low- or moderate-wind-speed conditions, whilst no particular correlation has been highlighted for high wind intensity. Furthermore, a dependence of the linear correlation on the wind direction has been detected. Subject terrestrial laser scannerSARcoastal environmentweather effectsurface roughness To reference this document use: http://resolver.tudelft.nl/uuid:842c79f4-1af5-44b0-a535-ced008342a40 DOI https://doi.org/10.3390/s22062311 ISSN 1424-8220 Source Sensors, 22 (6) Part of collection Institutional Repository Document type journal article Rights © 2022 V. di Biase, M. Kuschnerus, R.C. Lindenbergh Files PDF sensors_22_02311.pdf 3.68 MB Close viewer /islandora/object/uuid:842c79f4-1af5-44b0-a535-ced008342a40/datastream/OBJ/view