Abstract
Wildfires are among the most urgent environmental challenges affecting various parts of the globe. Uncontained wildfires interrupt transport, communication networks, supply of power and gas, and irrigation/water supply systems. They adversely affect air quality and cause property loss, crop destruction, and kill main animals as well as people. Climate change has caused various ecosystems to progressively dry out thereby increasing the risk of wildfires. Wildfires cause the emission of huge amounts of carbon and its oxides and fine particulate matter and worsen the factors that lead to further wildfires. The impact of wildfires can be diminished by early and accurate detection. There are many commercial fire detection sensor systems but most suffer from performance issues when used to monitor broad open tracts of land and forests—response time, maintenance requirements, high cost, etc. Our study estimates the probability of wildfire occurrence by retrieving and analysing the colours of forest images using image processing—specifically, the RGB values. Our focus is on estimating the risk of wildfire occurrence and highlighting spots that are most vulnerable thereby to achieve wildfire prevention.
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