Observing Dry-Fallen Intertidal Flats in the German Bight Using ALOS PALSAR Together With Other Microwave and Optical Remote Sensing Sensors
Martin Gade(1) and Kerstin Stelzer(2)
(1) Universität Hamburg, Bundesstraße 53, 20146 Hamburg, Germany
(2) Brockmann Consult, Max-Planck-Str. 2, 21502 Geesthacht, Germany
The surveillance of coastal regions is regulated through national and international laws and directives that define the temporal and spatial resolution, at which certain parameters have to be monitored. The increasing requirements of coastal monitoring, to some extend, can be met deploying remote sensing techniques that allow for relatively cheap surveillance of large coastal areas. Optical sensors are already being used for sediment classification purposes in coastal areas, and promising results have been achieved through the classification of different sediment types, vegetation, and mussel beds. However, because of the strong dependence on daylight and cloud conditions, useful optical data (acquired at low tide) from the German North Sea coast are rare. A classification system based on spaceborne remote sensing data would therefore strongly benefit from the utilization of synthetic aperture radar (SAR) data.
The radar backscattering from (wet) intertidal flats depends on their surface roughness properties, which vary due to, e.g., current-induced ripple formation, which in turn depends on the grainsize composition of the sediment. Further, benthic fauna such as blue mussels or oysters cause an increase in surface roughness. Therefore, the use, and the combination, of SAR data acquired at different radar bands may yield information on sediment classes, as well as on mussel beds, seagrass, etc.
We have used ALOS PALSAR, in combination with data from other spaceborne SAR sensors to investigate the sensitivity of the radar backscattering to surface roughness variations on dry-fallen intertidal flats in the German Bight of the North Sea. The observed differences are explained with the help of in-situ observations and classification results based on optical data. We also show the influence of environmental conditions (wind speed and direction) and imaging geometry (incidence angle and look direction) on the observed radar contrast. We demonstrate that ALOS PALSAR, used together with C and X band SAR data acquired shortly before or after, have great potential of improving the routine surveillance and classification of dry-fallen intertidal flats.