Novelty detection--recognition and evaluation of exceptional water reflectance spectra.

The aim of environmental surveillance is to monitor known phenomena as well as to detect exceptional situations. Synoptic monitoring of large areas in coastal waters can be performed by remote sensing using multispectral sensors onboard satellites. Many methods are in use which enable the detection and quantification of 'standard algae' or specific algae blooms using their known spectral response. The present study focusses on the detection of spectra outside the known range and which are referred to as exceptional spectra. In a first step observations from a one-year period were used to establish the parameterisation of what is defined as 'normal.' In a second step observations from a different period were used to test the novelty detection application, i.e. to look for features not occurring in the first period.

Automated classification of an environmental sensitivity index.

Environmental Sensitivity Indices (ESI) composed of many field-data are essential for monitoring and control systems. At the beginning of the last decade an ESI of the German Wadden Sea was developed for use by the relevant authorities. This ESI was derived by experts semi-manually analysing the extensive field data-set. An algorithm is presented here which emulates human expert-decisions on the classification of sensitivity classes. This will permit the necessary regular updates of ESI-determination when new field data become available using automated classifications procedures. After tuning the algorithm parameters it generates decisions identical to those of human experts in about 97% of all locations tested. In addition, the algorithm presented also enables erroneous or extremely seldom field data to be identified.