Integrating remote sensing processing and GIS to fire risk zone mapping: a case study for the Seih-Sou forest of Thessaloniki

Abstract

During the last years the forest management community has acknowledged the need of monitoring the vegetated and forest areas as well as mapping burned areas. The hazard of a forest fire is encountered in the Mediterranean territory quite often, mainly due to its climate. Notwithstanding the catastrophic effects to the entire ecosystem, for decades these countries have not taken proper precautionary measures. Remote sensing faces this challenge and emerges a necessity, for the accurate detection and control of the areas with a high fire risk. In order to map a fire risk zone and prevent future possible fires, an attempt is made to set up a risk model. The model deals with remote sensing data in a GIS environment. The model parameters that trigger the fire spreading are estimated by satellite imagery and topographical data. The vegetation type, the relief and the location of man-made features, such as tanks for reserving water, roads and fire belts are some of the model input data. In this study the image analysis of low and high resolution satellite imagery results to an effective study of the area. Image processing methods such as band ratios and vegetation indices offer the potential of exploring vegetated and non-vegetated areas, by creating new bands where the burned and un burned territory is highlighted. These bands in combination with the initial multispectral bands are inserted in an object based classification scheme designed to estimate the fire extent. The study area covers the suburban forest of Seih Sou situated around the city of Thessaloniki, Greece. Taking into account that only the 0.9 % of the city is vegetated, this forest is considered the main source of oxygen for the city which greatly affects the microclimate of the territory. A significant part of the forest was destroyed by a fire in July 1997 causing severe ecosystem damage.