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Forests represent the lung of our planet. Wildfires constitute one of the major problems facing the forest ecosystem in the world. In the last decades number of forest fires has been increased due to the increasing in the human activities and climate change. Fast alarm for forest fires is very crucial issue to prevent their spread. In this book we employed satellite images together with artificial neural networks to build a very fast alarm system for forest fire detection. The satellite NOAA-AVHRR (National Oceanic and Atmospheric Administration / Advanced Very High Resolution Radiometer) images are used here. The TAGged Adaptive Resonance Theory ART-TAG Artificial Neural Network ANN is employed. ART-TAG is a supervised form for Compact Fuzzy ART. Burned Area Mapping System (BAMS) and Fire Detection System (FDS) have been built. Integrated Fire Evolution Monitoring System (IFEMS) has been constructed by integrating BAMS and FDS. IFEMS has the ability to distinguish burned area, area in active fire, and area beneath flames. Moreover, it has the ability to detect Fires that occur between two consecutive images and tracing sub pixel fires as well.

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Kamal R. AL-Rawi is an Associate Professor of Computer Science at the University of Petra; Amman; Jordan. He has taught computer science for over 25 years. His professional interests are the theoretical development of artificial neural networks and their applications in analysis of satellite images. He is a member of the ACM.

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