Danish Environmental monitoring of COastal waters - DECO

This is a small description of the IMM aspects of the DECO project. Check the DECO project homepage at DHI, Water and Environment. Check also the research council description.

Project description

Remote sensing techniques have a large potential for supplementing traditional monitoring of the marine environment by providing a synoptic coverage which is unmatched by other existing techniques. The main objective of this project is to link the spectral fingerprint from the water surface to important water quality parameters such as algae and suspended matter content. The remote sensed data will be collected at three different levels (handheld, airborne and satellite measurements) which will provide accurate atmospheric correction. Intensive collection of sea truth data will lead to the establishment of algorithms relating the remotely sensed signal to parameters such as suspended matter concentration and size spectra, and concentration of different phytoplankton groups and yellow substances. Furthermore, different bottom types will be classified. Finally, the remotely sensed data will be used as input to hydrodynamic models which will potentially convert 2-dimensional into 3-dimensional information.

IMM Main Contributions

Development, implementation and application of mainly statistical methods in

  1. removal of systematic and random noise in space- and airborne scanner data,
  2. combination, integration and fusion of multi-source in situ measurements and remote sensing data,
  3. discrimination between relevant classes (algae, suspended sediments, dissolved organic matter, bottom types etc.) based on the restored data, and
  4. multi-temporal analysis such as change detection.

re 1)
The pre-processing will take the characteristics of the individual scanners into account. Methods include moment or histogram matching of subgroups of data, data dependent roll corrections, transformations of data into a noise-ordered feature space followed by (possibly Fourier based) noise removal and back transformation.

re 2)
In order to exploit the collected multi-source data fully it is tempting to resample coarse scale data to the grid of the fine scale data. This can be done by means of geostatistical methods as kriging which is based on the autocovariance or the semivariogram of the data or by means of techniques based on Markov random field (MRF) theory. As opposed to analyses of the individual groups of data statistical analyses of the joint multi-source data can reveal otherwise uncovered cross dependencies.

re 3)
Classification of the relevant signals can be done by means of various classical forms of discriminant analysis. Also, spatial or contextual extensions to the classical methods and non-parametric classifiers such as Owen-Hjort-Mohn discriminant analysis and classification and regression trees (CART), a binary decision tree based method, can be applied.

re 4)
Multi-temporal analysis in this context consists primarily of change detection methods. The methods can deal with i) simple differences (or ratios) of bi-temporal data, ii) orthogonal transformations of simple differences (or ratios) of bi-temporal data, and, iii) orthogonal transformations applied directly to bi- or truly multi-temporal data. Also the classifiers mentioned under heading 3) can benefit from the results of a multi-temporal analysis or the classifier itself can incorporate an autoregressive temporal part.

The results of the work will include better understanding of the nature of the physical and biological phenomena under study, and of the mathematical methods applied. In this context it is considered very important that a Ph.D. student is affiliated with both an application oriented institution and a mathematical methodology oriented institution. Hence the sharing of a Ph.D. student. Furthermore, the results will consist of healthy algorithms implemented as computer programs and the final end products from computer runs on the project data. These results can be used directly by the partners in the project, and the methods with examples from the project can be published in the international scientific literature.