Alena Schmidt

Wissenschaftliche Konzepte für ein Monitoring des ökologischen Zustands des deutschen Küstenmeeres

Aussagen zum Zustand und der Entwicklung von Küstenmeeren können nur durch langfristige Beobachtungen und Modellansätze erfolgen. Im Verbundprojekt WIMO (Wissenschaftliche Monitoringkonzepte für die Deutsche Bucht) werden Konzepte und Mess ver fahren entwickelt und bewertet, die sowohl grundsätzliche wissenschaftliche Frage stel lungen behandeln, als auch den Anforderungen des behördlichen Monitorings im Rahmen der europäischen Gesetzgebung genügen.

Water-Land-Classification in Coastal Areas with Full Waveform Lidar Data

flat, a high accuracy of the DTM is required for tasks such as hydrographic modelling. Airborne lidar (light detection and ranging) has become a standard method for DTM generation in coastal zones. The lidar technique has two main advantages compared to traditional aerial photogrammetry: Firstly, the active laser technique works independently from illumination from the sun, which allows mapping also during night-time. Secondly, the elevation model can be directly inferred from the two-way time-of-flight of the pulse reflected at the ground, whereas stereo techniques rely

Integration of TerraSAR-X, RapidEye and airborne lidar for remote sensing of intertidal bedforms on the upper flats of Norderney (German Wadden Sea)

The Wadden Sea is a large coastal transition area adjoining the southern North Sea uniting ecological key functions with an important role in coastal protection. The region is strictly protected by EU directives and national law and is a UNESCO World Heritage Site, requiring frequent quality assessments and regular monitoring. In 2014 an intertidal bedform area characterised by alternating crests and water-covered troughs on the tidal flats of the island of Norderney (German Wadden Sea sector) was chosen to test different remote sensing methods for habitat mapping: airborne lidar, satellite-based radar (TerraSAR-X) and electro-optical sensors (RapidEye). The results revealed that, although sensitive to different surface qualities, all sensors were able to image the bedforms. A digital terrain model generated from the lidar data shows crests and slopes of the bedforms with high geometric accuracy in the centimetre range, but high costs limit the operation area. TerraSAR-X data enabled identifying the positions of the bedforms reflecting the residual water in the troughs also with a high resolution of up to 1.1 m, but with larger footprints and much higher temporal availability. RapidEye data are sensitive to differences in sediment moisture employed to identify crest areas, slopes and troughs, with high spatial coverage but the lowest resolution (6.5 m). Monitoring concepts may differ in their remote sensing requirements regarding areal coverage, spatial and temporal resolution, sensitivity and geometric accuracy. Also financial budgets limit the selection of sensors. Thus, combining differing assets into an integrated concept of remote sensing contributes to solving these issues.

Detection of water surfaces in full-waveform laser scanning data

Airborne laser scanning has become a standard method for recording topographic data. A new generation of laser scanners digitises the complete waveform of the backscattered signal and thus offers the possibility of analysing the signal shape. As a product of the laser scanning, a digital surface model (DSM) or a digital terrain model (DTM) can be derived. In water regions, data acquisition by laser scanning is limited to the water surface because the near-infrared laser pulses hardly penetrate water. Therefore, a height model generated from laser scanner point clouds over water regions does not represent the actual terrain. The generation of a DTM thus requires the detection of water surfaces. In this study, a method for the detection and classification of water surfaces in airborne laser scanning data is proposed. The method works with both geometrical features (e.g. height or height variation) and characteristics of the pulses derived from the full waveform of the returned signal (e.g. intensity or pulse width). In our strategy, based on fuzzy logic, all classification parameters are derived automatically from training areas. According to their statistical distributions, the features are considered with individual weights. The aim of this paper is to analyse crucial features for classification and to investigate the potential of full waveform laser scanning data for this application. We present results from different areas with lakes and rivers, analysing the contribution of the individual groups of features for the detection of water surfaces.