Identification and modelling of forest fire severity and risk zones in the Cross – Niger transition forest with remotely sensed satellite data
Mfoniso Asuquo Enoh, Uzoma Chinenye Okeke, Needam Yiinu Narinua
Abstract
Forest fires are a serious environmental hazard within the forest ecosystem, which can be studied with Remote Sensing and GIS. The aim of the study is to identify and model forest fires severity and risk zones within the Cross – Niger transition forest. To achieve this aim, remotely sensed data, such as Landsat – 8 OLI (2020) and ASTER DEM were used to produce land cover maps and topography parameters such as aspect, elevation and slope. The topographic maps and the Google Earth imagery were used to extract human settlements and road networks. The final forest fire risk zone (FFRZ) map was prepared by integrating the different parameters such as Land cover, aspect, elevation, slope, proximities to roads and settlements in the ArcGIS environment. The FFRZ was categorized into three categories as low, moderate and high risk zones, based on their fire susceptibility. The category of low, moderate and high FFRZ were represented as 2731.7 km2 (12.5%), 17997.69 km2 (82.59 %) and 1061.63 km2 (4.87%) respectively. The study shows that Remote Sensing and GIS are excellent tools for modelling forest fire risk zones, hence proving that fires are anthropogenic in origin.