Posts Tagged ‘tiger’

Geospatial Technology to for Wildlife Management and Conservation

Monday, December 28th, 2009

From KT, Intro GIS.

Geospatial technology, especially GIS, is often viewed as an application for analyzing and understanding social distributions (e.g., literacy rate, birth rate, and death rate). Increasingly, geospatial technology is used to monitor wildlife migration to better understand and develop conservation and management techniques.

In the Achanakmar Wildlife Sanctuary in India researchers measured a variety of variables to determine suitable habitat space for tigers. To develop the model, GIS data layers for the area were created by digitizing topographic maps (i.e., contours, roads, and settlement patterns). Satellite information was used for forest type and forest density. Forest type information was derived using a false colour composite. To complete the data sets, researchers also collected field data on the ground truth of forest type, current habitat area and the habitat area of prey. They also performed a statistical analysis. The result was a map that illustrates habitat suitabile for tigers.

A similar study was undertaken in Florida to analyze suitable habitat areas for the highly endangered Florida panther. The method of this study however differed slightly from that of the tiger study. Here, researchers used GIS to overlay maps of many different parameters (i.e., land type, road structure, vegetation, and protected areas). They obtained shapefiles from government and private sources. Their conclusions mimicked what was seen in the tiger study: only small regions are suitable for long-term panther sustainability.

The GIS approach to these problems is particularly important because it is repeatable over time as variables such as land use and forest type change. It also gives researchers a large spatial context and ensures that maps and models only contain relevant information. I think these models are very useful, as they provide a way for a researcher or conservation official to easily look at many variables and how the variables overlay each other spatially.