Fishing Ground Prediction Using a Knowledge-based Expert System Geographical Information System Model in the South and Central Sulawesi Coastal Waters of Indonesia

International Journal of Remote Sensing, Volume 30, Issue 24 2009 , pages 6429 – 6440

M. Sadly;  N. Hendiarti;  S. I. Sachoemar; Y. Faisal

“A knowledge-based expert system model working on the basis of a geographical information system (GIS) was applied to predict fishing ground spots in the coastal waters of South and Central Sulawesi. The model is designed by the integration of multisource data to answer ‘what?’, ‘where?’, and ‘why?’ questions of the fishing ground location. Despite the fact that GIS is a powerful tool for dealing with the first two questions, GIS is inferior for answering the ‘why?’ question in geo-studies. One of the possible ways of overcoming the inferiority of GIS for answering the ‘why?’ question of geo-studies is by integrating an expert system in a GIS to form a knowledge-based expert system GIS model. In this study, we used a series of sea surface temperature (SST) satellite data, sea surface chlorophyll-a (SSC) and turbidity derived from MODIS Aqua in the period 2003-2005 as input data, to understand the temporal and seasonal variability of the marine environment of the study area, and identified the oceanographic phenomena, i.e. upwelling, front or eddy. A spatial configuration map of the predicted fishing ground spots was then developed and integrated using a knowledge-based expert system GIS model generated by the Erdas Macro Language (EML) of Erdas Imagine 9.0 software. To verify this result, a series of in situ fishing ground spot data of the study area were collected for similar periods, and they were then analysed using a simple statistical method. The result shows that the predicted fishing ground spots generated by the knowledge-based expert system GIS model corresponded well with in situ data with a high accuracy level of 85%. This result has demonstrated that the knowledge-based expert system GIS model can be applied to predict, localize and determine fishing ground spots in which their accuracy level will be determined by the completeness of spatial knowledge of the domain expertise and the sophistication level of the programming utilities being used.”

Disease Causing Steep Decline of Bats

Mapping the Spread of White-Nose Syndrome with GIS

…from the Winter 2009/2010 issue of ArcNews

Caves have become graveyards for what biologists estimate as more than one million bats, the victims of a devastating disease known as white-nose syndrome (WNS).

Building on the work of its partners, Bat Conservation International (BCI) is using GIS to create a geodatabase of critical hibernation sites and map the probable spread of the disease. Understanding how WNS has spread and is expected to spread in the future will help biologists respond to the disease using surveillance and conservation actions.

The first case of WNS in hibernating bats was discovered in a cave near Albany, New York, in February 2006. Since then, the disease is known to have killed bats in Connecticut, Massachusetts, New Hampshire, New Jersey, Pennsylvania, Vermont, Virginia, and West Virginia.