Algal Bloom Forecasting using Remote Sensing with Spatially and Temporally Sparse Satellite Data

Bachelor thesis (2023)

Authors

E.A. de Gruyl Electrical Engineering, Mathematics and Computer Science

Contributors

J.C. van Gemert Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
A. Lengyel Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
R. Bruintjes Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
K.G. Langendoen Embedded Systems - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Einar de Gruyl
Forecasting Remote Sensing Chlorophyll-a
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Published Date

31-01-2023

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This research presents a method for forecasting algal blooms using remote sensing with spatially and temporally sparse satellite data. The method involves the use of multiple interpolation methods to interpolate the sparse input data. The approach is shown to be effective in predicting algal blooms in areas where data is sparse, and the results demonstrate the potential for using this method to improve the forecasting and management of harmful algal blooms.

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