Efficient Forest Fire Detection System: A Spatial Data Mining and Image Processing Based Approach

The drastic ascent in the volume of spatial data owes its growth to the technical advancements in technologies that aid in spatial data acquisition, mass storage and network interconnection. Thus the necessity for automated detection of spatial knowledge from voluminous spatial data arises. Fire plays a vital role in a majority of the forest ecosystems. Forest fires are serious ecological threats that result in deterioration of economy and environment apart from jeopardizing human lives. Thus forest fires need to be detected as early as possible in order to inhibit from being spread. This paper intends to detect forest fires from the forest spatial data. The approach makes use of spatial data mining, image processing and artificial intelligence techniques for the detection of fires. A fuzzy rule base is formed for the detection of fires, from the spatial data with the presence of fires. The digital images from the spatial data are converted to YCbCr color space and then segmented by employing anisotropic diffusion to identify fire regions. Subsequently, a fuzzy set is created with the color space values of the fire regions. Further, fuzzy rules are derived on basis of fuzzy logic reasoning. Extensive experimental assessment on publicly available spatial data illustrated that the proposed approach efficiently detects forest fires.