Monitoring Aquaculture Structures by SAR Imagery

Monitoring Aquaculture Structures by SAR Imagery


G. Profeti
Remote sensing
[email protected]

C. Travaglia
FAO Remote Sensing Officer (retired)

Inventory and monitoring inland and offshore fisheries structures provide the necessary baseline data for decision-making on aquaculture and mariculture development, including regulatory laws, environmental protection and revenue collection.

Mapping fisheries structures can be performed with good accuracy and timely by satellite remote sensing, which allows observation of vast areas, often of difficult accessibility, at a fraction of the cost of traditional surveys.

Satellite imaging radar (SAR) data are unique for this task not only for their inherent all-weather capabilities, very important as aquaculture and mariculture activities mainly occur in tropical and subtropical areas, but essentially because the backscatter from the fisheries structures components allows for the structures identification and separation from other features.

In this paper, the Authors review two FAO pilot studies in which satellite SAR data were used to map aquaculture and mariculture structures with resulting high accuracy. Furthermore, the results of the data processing and interpretation can be directly managed by means of a Geographic Information System, for monitoring the territory and implement sound models of environmental development.

1. Introduction
Aquaculture is the world’s fastest growing food production system, with an average worldwide production increase of 8.9 percent per year since 1970 (FAO, 2004). In 2002, Asian aquaculture farmers contributed the 91.2 percent of the world’s aquaculture production quantity, and in the same year 90.7 percent of total aquaculture yield was produced in low-income food-deficit countries. For these populations, aquaculture has great potential for the production of food, alleviation of poverty and generation of income. However, significant problems can be associated with coastal aquaculture development. These include the vulnerability of aquaculture to poor water quality and pollution, caused by industrial, domestic, agricultural and aquacultural (i.e.: its own) wastes; and over-rapid development, where the undoubted successes of the sector have been tarnished by environmental (i.e.: mangroves destruction) and resource use issues, social problems, disease, and in some cases, marketing problems.

Although some of the social and environmental problems may be addressed at the individual farm level, most are cumulative – insignificant when an individual farm is considered, but potentially highly significant in relation to the whole sector. They are also additive – in the sense that they may add to the many other development pressures in the coastal zone. These cumulative and additive problems can only be addressed through better planning and management of the sector – by government, in collaboration with producer associations or industry organizations.

These objectives can be efficiently pursued by using Geographic Information Systems to store and analyze the geospatial data on aquaculture structures and aquaculture-related environmental parameters. Given the spreading of aquaculture practice, and the nature of the places in which these structures are located, the collection of such data by means of ground surveys is too costly. For these reasons the use of remote sensing imagery to locate and map aquaculture structures has been investigated by the Authors in the past years. In fact, compared with information acquired by traditional methods, these data offer a number of advantages:

  • They provide synoptic coverage and therefore give an exhaustive view of vast areas at the same time (e.g. from 3,600 to 34,000 km2 in one image, depending on the type of satellite).
  • They can be acquired for the same area of at a high rate of repetition – two to three times a month – thus permitting selection of the most appropriate seasonal data.
  • They can be obtained for any part of the world without encountering administrative restrictions.
  • In the case of radar imaging satellites with their inherent all-weather capabilities, data are available also for regions usually cloud-covered, a very important factor for mapping of tropical and subtropical areas.

A description of two case studies undertaken by the Authors on this subject is summarized in the following paragraphs.

2. The Sri Lanka study
In 1999, in the framework of the assistance provided to the FAO project TCP/SRL/6712 “Revitalization and Acceleration of Aquaculture Development” in its inventory and monitoring of shrimp farms in northwestern Sri Lanka (Fig. 1), a pilot study was conducted with a view to develop and field test adequate methodologies for future use in similar environments elsewhere.

Inventory and monitoring of shrimp farms are essential tools for decision-making on aquaculture development, including regulatory laws, environmental protection and revenue collection. The Sri Lanka Government required up to date information on the spatial distribution of shrimp farms in order to enforce development regulations and to ensure a productive environment for shrimp farming with the least impact on the other uses of land and water resources. The FAO project TCP/SRL/6712 provided an unique opportunity to test under operative conditions an innovative methodology for inventory and monitoring of shrimp farms and the support of a field team for the ground verification of the results and, thus, of the methodology’s accuracy.

It was immediately evident to the authors that satellite imaging radar was the only tool available for achieving good results. Synthetic Aperture Radar (SAR) data are unique for mapping shrimp farms, not only for their inherent all-weather capabilities, very important as shrimp farms occur in tropical and subtropical areas, but mainly because the backscatter from the dykes surrounding the ponds allows for recognition and separation of shrimp ponds from all other water covered surfaces. This is not possible with satellite data such as Landsat or SPOT, operating in the visible and near/mid infrared portion of the electromagnetic spectrum, because of the frequent clouds coverage and of the difficulty of discriminating the artisanal shrimp farms, with their small area and irregular shape, from other water covered surfaces, such as flooded rice paddies and flooded areas.