Catálogo de publicaciones - libros

The use of AFC pulse shape information does improve discrimination of microalgal taxa, and is likely to be even more useful when species that form chains are to be discriminated. The use of RBF ANNs was again shown to be a rapid and useful tool for analysing large sets of high dimensional data.

The software LAGRAMGE for computational assemblage and adaptation of ODE by using the expert knowledge and measured data has been applied for the simulation of chl- in Lake Kasumigaura. As a result two types of chl- models were discovered: (1) chl- equations without considering zooplankton grazing assembled and trained by data of consecutive years were data of the last year was used for testing, and (2) chl- equations considering zooplankton grazing assembled and trained by data of the years 1986 to 1989. The test results of the different models have demonstrated that LAGRAMGE can discover ODE that allow to simulate chl- in Lake Kasumigaura for a variety of years. However the generalisation of discovered equations for unseen data of consecutive years was unsatisfactory, and the accuracy of calculated trajectories with regards to timing and magnitudes of peak events was moderate. The results have highlighted the importance of nutrients as growth limiting factors, and the need for considering functional algae groups in order to appropriately represent their selective grazing by zooplankton.

Neural networks are powerful general purpose computing tools. They have become popular in the analysis of remotely sensed data, particularly for classification and regression-type problems in which they have often been demonstrated to extract information more accurately than conventional methods. Although not free from problems, it seems likely that neural networks will be used increasingly in ecological research using remote sensing. Moreover, as some of the problems encountered in use of neural networks arise from a tendency to focus upon the MLP only it is likely that there will be a greater use of other network types. In addition, it is expected that the range of applications of neural networks in remote sensing will broaden. Applications in which neural networks have already been used and increased usage may be expected include: image preprocessing (e.g. geometric, atmospheric and radiometric correction), stereo-matching imagery, image compression, feature extraction, map generalisation, multi-source data analysis, data fusion and image sharpening (e.g. Day, 1997; Foody, 1999a). Thus while neural networks have rapidly become established in remote sensing it is likely that they will be used increasingly and in a broader range of activities that will help exploit more fully the potential of remote sensing as a useful tool in ecological research.