Sand and mud traversed by tidal inlets and channels, which split in subtle branches, salt marshes at the coast, the tide, harsh weather conditions and a high diversity of fauna and flora characterize the ecosystem Wadden Sea. No other landscape on the Earth changes in such a dynamic manner. Therefore, land cover classification and monitoring of vulnerable ecosystems is one of the most important approaches in remote sensing and has drawn much attention in recent years. The Wadden Sea in the southeastern part of the North Sea is one such vulnerable ecosystem, which is highly dynamic and diverse. The tidal flats of the Wadden Sea are the zone of interaction between marine and terrestrial environments and are at risk due to climate change, pollution and anthropogenic pressure. Due to that, the European Union has implemented various directives, which formulate objectives such as achieving or maintaining a good environmental status respectively a favourable conservation status within a given time. In this context, a permanent observation for the estimation of the ecological condition is needed. Moreover, changes can be tracked or even foreseen and an appropriate response is possible. Therefore, it is important to distinguish between short-term changes, which are related to the dynamic manner of the ecosystem, and long-term changes, which are the result of extraneous influences. The accessibility both from sea and land is very poor, which makes monitoring and mapping of tidal flat environments from in situ measurements very difficult and cost-intensive. For the monitoring of big areas, time-saving applications are needed. In this context, remote sensing offers great possibilities, due to its provision of a large spatial coverage and non-intrusive measurements of the Earth’s surface. Previous studies in remote sensing have focused on the use of electro-optical and radar sensors for remote sensing of tidal flats, whereas microwave systems using synthetic aperture radar (SAR) can be a complementary tool for tidal flat observation, especially due to their high spatial resolution and all-weather imaging capability. Nevertheless, the repetitive tidal event and dynamic sedimentary processes make an integrated observation of tidal flats from multi-sourced datasets essential for mapping and monitoring. The main challenge for remote sensing of tidal flats is to isolate the sediment, vegetation or shellfish bed features in the spectral signature or backscatter intensity from interference by water, the atmosphere, fauna and flora. In addition, optically active materials, such as plankton, suspended matter and dissolved organics, affect the scattering and absorption of radiation. Tidal flats are spatially complex and temporally quite variable and thus mapping tidal land cover requires satellites or aircraft imagers with high spatial and temporal resolution and, in some cases, hyperspectral data. In this research, a hierarchical knowledge-based decision tree applied to multi-sensor remote sensing data is introduced and the results have been visually and numerically evaluated and subsequently analysed. The multi-sensor approach comprises electro-optical data from RapidEye, SAR data from TerraSAR-X and airborne LiDAR data in a decision tree. Moreover, spectrometric and ground truth data are implemented into the analysis. The aim is to develop an automatic or semi-automatic procedure for estimating the distribution of vegetation, shellfish beds and sediments south of the barrier island Norderney. The multi-sensor approach starts with a semi-automatic pre-processing procedure for the electro-optical data of RapidEye, LiDAR data, spectrometric data and ground truth data. The decision tree classification is based on a set of hierarchically structured algorithms that use object and texture features. In each decision, one satellite dataset is applied to estimate a specific class. This helps to overcome the drawbacks that arise from a combined usage of all remote sensing datasets for one class. This could be shown by the comparison of the decision tree results with a popular state-of-the-art supervised classification approach (random forest). Subsequent to the classification, a discrimination analysis of various sediment spectra, measured with a hyperspectral sensor, has been carried out. In this context, the spectral features of the tidal sediments were analysed and a feature selection method has been developed to estimate suitable wavelengths for discrimination with very high accuracy. The developed feature selection method ‘JMDFS’ (Jeffries-Matusita distance feature selection) is a filter-based supervised band elimination technique and is based on the local Euclidean distance and the Jeffries-Matusita distance. An iterative process is used to subsequently eliminate wavelengths and calculate a separability measure at the end of each iteration. If distinctive thresholds are achieved, the process stops and the remaining wavelengths are applied in the further analysis. The results have been compared with a standard feature selection method (ReliefF). The JMDFS method obtains similar results and runs 216 times faster. Both approaches are quantitatively and qualitatively evaluated using reference data and standard methodologies for comparison. The results show that the proposed approaches are able to estimate the land cover of the tidal flats and to discriminate the tidal sediments with moderate to very high accuracy. The accuracies of each land cover class vary according to the dataset used. Furthermore, it is shown that specific reflection features can be identified that help in discriminating tidal sediments and which should be used in further applications in tidal flats.

The Wadden Sea off the coast of the southern North Sea is the largest coherent area of tidal flats worldwide. As a highly productive ecosystem it is of global importance, e.g. as nursery for fish and as a feeding and resting area for 10 – 12 million migratory birds following the East Atlantic Flyway. The outstanding ecological significance of this region corresponds to a high level of protection by EU directives and national law and by inscription as UNESCO World Heritage Site, all of which requires regular monitoring and assessment. Apart from the ecological aspects, the Wadden Sea is also of great importance for coastal protection. To survey the extensive, often inaccessible tidal area, remote sensing is essential and while mainly airborne techniques have been carried out for decades, now high-resolution satellite-borne sensors open up new possibilities relevant for monitoring and long-term ecological research. Especially satellite synthetic aperture radar (SAR) sensors offer a high availability of acquisitions as they operate largely independently of daylight and weather. The aim of the studies presented here was to explore the use of data from the TerraSAR-X satellite to record geomorphological structures and habitats for Wadden Sea Monitoring. More than 100 TerraSAR-X acquisitions from 2009 to 2016 were analyzed to distinguish various and variable surface types continuously influenced by tidal dynamics in the main study area, the tidal flats near the island of Norderney. Visual image interpretation supported by extensive in-situ verification proved to be a suitable and unsophisticated approach which is unspecific enough to identify mussel beds, fields of shell-detritus, gully structures, mud fields, and intertidal bedforms in the upper flats of the East Frisian Islands. The method proved to be robust against changes in geometry of acquisition and environmental influences. Several time series of TerraSAR-X data enabled to follow inter-annual and seasonal dynamics as well as event effects (Adolph et al. 2018). The high-frequency TerraSAR-X data revealed novel evidence of an intertidal bedform shift in an easterly direction during the study period. To this aim, an unsupervised ISODATA classification of textural parameters was developed to vectorize and compare the bedforms positions in a GIS (Adolph et al. 2017a). The same intertidal bedform area was chosen as test-site for comparison of different remote sensing methods, namely airborne lidar, satellite-based radar (TerraSAR-X) and electro-optical sensors (RapidEye) (Adolph et al. 2017b). High-resolution SAR data offer a relevant component for Wadden Sea Monitoring and Research, as they provide reliable, regular data with a high repetition rate. In particular, habitats with noticeable surface roughness, specific structures and textures are well reflected. Geomorphic Structures and their dynamics can be observed indirectly via detection of residual water trapped within. A comprehensive concept for Wadden Sea Monitoring however, requires automatized classifications and an integrative, multi-sensor approach (SAR, LIDAR, multi-spectral data, drones) in which different and complementary information, coverage and resolutions (spatial and temporal) contribute to an overall picture. The studies were carried out as part of the joint research project “Scientific monitoring concepts for the German Bight” (WIMO), jointly funded by the Ministry of Environment, Energy and Climate Protection (NMU) and the Ministry of Science and Culture (NMWK) of the Federal State of Lower Saxony. The findings have been published in Geo-Marine Letters 37/2 (2017) and in Remote Sensing 10/7 (2018).