Mr. Stefan Riha Profile

Stefan Riha

at Deutsches Zentrum für Luft- und Raumfahrt eV

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Publications (2)

Proceedings Article | 8 October 2011 Paper

Development of a remote sensing algorithm for cyanobacterial phycocyanin pigment in the Baltic Sea using neural network approach

Stefan Riha, Harald Krawczyk

Proceedings Volume 8175, 817504 (2011) https://doi.org/10.1117/12.898081

KEYWORDS: Algorithm development, Absorption, Remote sensing, Neural networks, Water, Optical properties, Ocean optics, Satellites, Scattering, Evolutionary algorithms

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Water quality monitoring in the Baltic Sea is of high ecological importance for all its neighbouring countries. They are highly interested in a regular monitoring of water quality parameters of their regional zones. A special attention is paid to the occurrence and dissemination of algae blooms. Among the appearing blooms the possibly toxicological or harmful cyanobacteria cultures are a special case of investigation, due to their specific optical properties and due to the negative influence on the ecological state of the aquatic system. Satellite remote sensing, with its high temporal and spatial resolution opportunities, allows the frequent observations of large areas of the Baltic Sea with special focus on its two seasonal algae blooms. For a better monitoring of the cyanobacteria dominated summer blooms, adapted algorithms are needed which take into account the special optical properties of blue-green algae. Chlorophyll-a standard algorithms typically fail in a correct recognition of these occurrences. To significantly improve the opportunities of observation and propagation of the cyanobacteria blooms, the Marine Remote Sensing group of DLR has started the development of a model based inversion algorithm that includes a four component bio-optical water model for Case2 waters, which extends the commonly calculated parameter set chlorophyll, Suspended Matter and CDOM with an additional parameter for the estimation of phycocyanin absorption. It was necessary to carry out detailed optical laboratory measurements with different cyanobacteria cultures, occurring in the Baltic Sea, for the generation of a specific bio-optical model. The inversion of satellite remote sensing data is based on an artificial Neural Network technique. This is a model based multivariate non-linear inversion approach. The specifically designed Neural Network is trained with a comprehensive dataset of simulated reflectance values taking into account the laboratory obtained specific optical properties of the algae species, according to the wavelengths of MERIS VIS/NIR bands. The input to the inversion neural network are atmospheric corrected (Level2) MERIS bottom of atmosphere reflectances as well as viewing geometries of the sensor from which the output maps for chlorophyll concentration, Suspended Matter concentration, CDOM absorption and phycocyanin absorption are generated. The paper demonstrates the theoretical basis and development of the algorithm together with a number of example results obtained from MERIS scenes in the Baltic Sea. Furthermore it compares the phycocyanin-algorithm with the standard DLR PCI algorithm based on the related inversion technique "Principal Component Analysis" and discusses the different inversion approaches.

Proceedings Article | 10 September 2009 Paper

Multivariate interpretation algorithm for water quality in the Baltic Sea

Harald Krawczyk, Andreas Neumann, Stefan Riha

Proceedings Volume 7473, 747303 (2009) https://doi.org/10.1117/12.830400

KEYWORDS: Water, Remote sensing, Principal component analysis, Ocean optics, Radiative transfer, Absorption, Scattering, Transparency, Satellites, Algorithm development

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Subject of the paper is the presentation of the potential of use of multispectral remote sensing data for the investigation of water quality of large water basins on the example of the monitoring of the Baltic Sea with MERIS data. An interpretation and inversion scheme for optical satellite data over water has been developed to be used in several national and international projects to monitor different aspects of water quality. The resulting "Principal Component Interpretation" algorithm allows an optimized estimation of water constituents: chlorophyll pigment concentrations, suspended matter concentration and yellow substance concentration as well as optical properties of the water body. From these are derived secondary parameters like water transparency. In the frame of the international ESA MARCOAST project this interpretation scheme was developed for a regular (daily) monitoring of the Baltic Sea. Results are uniformly mapped images and concentration maps of the Baltic Sea area from which are additionally derived weekly, monthly and seasonal means. The Principal Component Interpretation belongs to the class of model based multivariate interpretation schemes and is closely related to Neural Networks techniques, but bases on a completely different training procedure. It makes use of an optimal information redistribution between the spectral bands and relates them to the water constituents. This kind of estimation allows an simultaneous estimation of expected global estimation accuracy. The regular monitoring is accompanied by the survey of in-situ ground measurements, which can be used for validation.The paper will present the bio-optical model which is used for the interpretation of Baltic Sea water. The basics of the interpretation scheme basing on principal component analysis will be explained and results of the monitoring of different products will be discussed on examples of a time series in 2008, showing the development and movement of algae blooms, together with other constituents. The obtained results are critically compared with available ground measurement.

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