International Journal of Aquaculture, 2016, Vol.6, No.19, 1
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sensing has a high potential to provide valuable information regarding the extent, status and management of
aquaculture at various spatial and temporal scales.
The term ‘remote sensing’ is broadly defined as the technique(s) for collecting images or other data about an
object from measurements made at a distance from the object, and can refer, for instance, to satellite imagery, to
aerial photographs or to ocean bathymetry explored from a ship using echo sounder data (Rajitha et al., 2007).
It is also defined as the acquisition of data using a remotely located sensing device, and the extraction of
information from the data. The increasing use of remote sensing in agriculture is visible in growing researches in
crops (production, crop types, harvest, crop yield), lands (land condition, parcel area, location), forest types,
quality of water bodies, types of irrigation systems etc. (Jovanović et al., 2014).
The capabilities of evolving GIS and remote sensing provide a powerful tool for the efficient and cost effective
management of sustainable aquaculture (Radiarta et al., 2008). GIS is useful for manipulating spatial aspects of
aquaculture planning due to the ability to bring together many diverse and complex factors to facilitate
development and administrative decisions (Ross et al., 2009; Silva et al., 2011). Remote sensing is a very useful
tool for understanding and developing flood management strategies. Satellite imagery can provide synoptic data
covering large areas and in combination with Geographic Information Systems (GIS) can give timely and cost
efficient analysis of aquaculture. Application of GIS will help in efficient storage, management, and analysis of
spatial and non-spatial data (Kapetsky et al., 1987; Aronoff, 1989; Burrough and McDonnell, 1998).
Even though, there are some reviews on the use of remote sensing and GIS in aquaculture and fisheries, these
studies are either species specific (Rajitha et al., 2007) or regional/country based review. The rationale for this
paper is to review the extent to which remote sensing and GIS has been employed in aquaculture extensively over
the past years taking into consideration all commercial species of importance that have been studied across the
globe and also to make recommendations for the near future. This paper also looks at constrains and opportunities
of using remote sensing and GIS in aquaculture. This paper is of importance because decision-makers and
aquaculture stakeholders need confidence that all technical estimates and data provided to them are true and
accurate.
2 Application of remote sensing and GIS aquaculture
2.1 Mapping and Monitoring Aquaculture
As stated in the introduction, in order to ensure aquaculture practices that are environmentally safe, technically
appropriate, economically viable and socially acceptable there is the need to ensure proper monitoring and
evaluation. This is possible through integrated planning and management within the framework of sustainable
aquaculture management using modern technologies such as remote sensing and GIS.
Advances have been made with respect to the use of remote sensing and GIS in aquaculture worldwide. Remote
sensing has been applied in mapping shrimp-farming and agricultural areas (Shahid et al., 1992) and tilapia
farming areas (Hossain et al., 2007). It has also been employed in shrimp-farming development of coastal zone
(Hossain et al., 2001), assessing the impact of aquaculture on mangroves (Pattanaik, and Prasad, 2011), assessing
the impact of aquaculture farms using remote sensing: an empirical neural network algorithm for Ildırı Bay,
Turkey (Bengil and Bizsel, 2014) as well as remote sensing monitoring and temporal variation analysis of coastal
aquaculture in Shandong Province (Xu et al., 2014).
Remote sensing data collected over 37d spanning from September 2009 to February 2011 were used to assess the
impact aquaculture has on Ildiri Bay, Turkey by Bengil and Bizsel, (2014). By this they improved the available
dataset by applying a local empirical neural network (NN) algorithm. They subsequently evaluated the impact in
terms of total suspended matter (TSM) and Secchi disk depth (SDD) as the effective variables showing changes in
underwater light fields in each defined sub-area.
