Remotely sensing the German Wadden Sea-a new approach to address national and international environmental legislation

Environ Monit Assess. 2016 Oct;188(10):595. doi: 10.1007/s10661-016-5591-x. Epub 2016 Sep 28.


The Wadden Sea along the North Sea coasts of Denmark, Germany, and the Netherlands is the largest unbroken system of intertidal sand and mud flats in the world. Its habitats are highly productive and harbour high standing stocks and densities of benthic species, well adapted to the demanding environmental conditions. Therefore, the Wadden Sea is one of the most important areas for migratory birds in the world and thus protected by national and international legislation, which amongst others requires extensive monitoring. Due to the inaccessibility of major areas of the Wadden Sea, a classification approach based on optical and radar remote sensing has been developed to support environmental monitoring programmes. In this study, the general classification framework as well as two specific monitoring cases, mussel beds and seagrass meadows, are presented. The classification of mussel beds profits highly from inclusion of radar data due to their rough surface and achieves agreements of up to 79 % with areal data from the regular monitoring programme. Classification of seagrass meadows reaches even higher agreements with monitoring data (up to 100 %) and furthermore captures seagrass densities as low as 10 %. The main classification results are information on area and location of individual habitats. These are needed to fulfil environmental legislation requirements. One of the major advantages of this approach is the large areal coverage with individual satellite images, allowing simultaneous assessment of both accessible and inaccessible areas and thus providing a more complete overall picture.

Keywords: Environmental EU directives; Monitoring; Mussel beds; Satellite remote sensing; Seagrass; Wadden Sea.

MeSH terms

  • Animals
  • Aquatic Organisms / growth & development*
  • Bivalvia*
  • Denmark
  • Ecosystem*
  • Environmental Monitoring / methods*
  • Germany
  • Netherlands
  • Oceans and Seas*
  • Plants*
  • Remote Sensing Technology*