International Journal of Remote Sensing
Forest fires regional distribution and numerical simulation based on universal kriging algorithm
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Efficient forest fire occurrence prediction for developing countries using two weather parameters
Engineering Applications of Artificial Intelligence
Adaptive estimation of visual smoke detection parameters based on spatial data and fire risk index
Computer Vision and Image Understanding
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A forest fire can be a real ecological disaster, regardless of whether it is caused by natural forces or human activity. It is impossible to control nature, but it is possible to map forest fire risk zones and thereby minimise the frequency of fire, avert damage, etc. Forest fire risk zones are locations where a fire is likely to start, and from where it can easily spread to other areas. Anticipation of factors influencing the occurrence of fire and understanding the dynamic behaviour of fire are critical aspects of fire management. A precise evaluation of forest fire problems and decisions on solution methods can only be satisfactorily made when a fire risk zone map is available. Satellite data plays a vital role in identifying and mapping forest fires and in recording the frequency at which different vegetation types/zones are affected. A geographic information system (GIS) can be used effectively to combine different forest-fire-causing factors for demarcating the forest fire risk zone map. Gorna Subwatershed, located in Madhya Pradesh, India, was selected for this study because it continually faces a forest fire problem. A colour composite image from the Indian Remote Sensing Satellite (IRS) 1D LISS III was used for vegetation mapping. Slope and other coverages (roads and settlements) were derived from topographic maps and field information. The thematic and topographic information was digitised and ARC/INFO GIS software was used for analysis. Forest fire risk zones were delineated by assigning subjective weights to the classes of all the layers according to their sensitivity to fire or their fire-inducing capability. Four categories of forest fire risk ranging from very high to low were derived automatically. Almost 30% of the study area was predicted to be under very high and high-risk zones. The evolved GIS-based forest fire risk model of the study area was found to be in strong agreement with actual fire-affected sites.