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Prediction of reef fish spawning aggregations using remote sensing: a review

Rosli, Mohamad Rahimi and Ibrahim, Ab. Latif and Masron, Tarmiji (2014) Prediction of reef fish spawning aggregations using remote sensing: a review. 8TH INTERNATIONAL SYMPOSIUM OF THE DIGITAL EARTH (ISDE8), 18 (1). ISSN 1755-1315

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Official URL: http://dx.doi.org/10.1088/1755-1315/18/1/012115

Abstract

Spawning aggregation is a very important occurrence to particular reef fish species as they use this opportunity to reproduce. However, due to their predictable nature, these aggregations have always been vulnerable to overexploitation. This problem leads to the importance of identifying the exact time and location for reef fish spawning aggregation. Thus, this paper review a little bit about spawning aggregation of reef fish as well as their characteristics, and problems regarding this phenomena. The use of remote sensing in marine applications is also described here in order to discuss how remote sensing can be utilize to predict reef fish spawning aggregation. Based on the unique geomorphological characteristics of the spawning aggregation, remote sensing seems to be a powerful tool to determine their exact times and locations. It has been proved that satellite imagery was able to delineate specific reef geomorphologies such as shelf edges and reef promontories. Despite of the widely use of remote sensing in marine applications, in fact there are still lack of studies had been carried out regarding spawning aggregations of reef fish due to the skeptical point-of-view by certain researchers over the capability of this technique. However, there is actually no doubt that the use of remote sensing will provide a better hand to the authorities in order to establish a more effective monitoring and conservation plan for these spawning aggregations.

Item Type:Article
Subjects:H Social Sciences > HD Industries. Land use. Labor
Divisions:Geoinformation and Real Estate
ID Code:54564
Deposited By: Haliza Zainal
Deposited On:05 Apr 2016 14:59
Last Modified:27 Jul 2017 11:18

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