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Enhancing generic ecological model for short-term prediction of Southern North Sea algal dynamics with remote sensing images
Li, H.; Arias, M.; Blauw, A.; Los, H.; Mynett, A.E.; Peters, S. (2010). Enhancing generic ecological model for short-term prediction of Southern North Sea algal dynamics with remote sensing images. Ecol. Model. 221(20): 2435-2446. hdl.handle.net/10.1016/j.ecolmodel.2010.06.020
In: Ecological Modelling. Elsevier: Amsterdam; Lausanne; New York; Oxford; Shannon; Tokyo. ISSN 0304-3800, more
Peer reviewed article  

Available in Authors 

Keyword
    Marine
Author keywords
    Generic ecological model; MERIS data; Algal dynamics modelling; Southern North Sea; Total suspended matter; Self-learning cellular automata

Authors  Top 
  • Li, H.
  • Arias, M.
  • Blauw, A.
  • Los, H.
  • Mynett, A.E.
  • Peters, S.

Abstract
    Physically based numerical modelling follows from the basic understanding of the underlying mechanisms and is often represented by a set of (partial differential) equations. It is one of the main approaches in population dynamics modelling. The emphasis of the model introduced in this paper is on the simulation of short-term spatial and temporal dynamics of harmful algal bloom (HAB) events. Total suspended matter (TSM) concentration is one of the dominant factors for harmful algal bloom (HAB) prediction in North Sea. However, the modelling of suspended matter contains a high degree of uncertainty in this area. Therefore, this research aims to achieve a better estimation for the short-term prediction of harmful algal bloom development in both space and time by using spatially distributed TSM retrieved from remotely sensed images as physically based model inputs. In order to supply complete spatially covered datasets for the physically based model instrument: generic ecological model (GEM), this research retrieves TSM information from MERIS images by means of proper estimation techniques including biharmonic splines and self-learning cellular automata. A better estimation of HAB spatial pattern development is achieved by adding spatially distributed TSM data as inputs to original GEM model, and it proved that chlorophyll-a concentration in this area is very sensitive to TSM concentration.

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