Modelação da vulnerabilidade ao risco de incêndio florestal com integração de sistemas de informação geográfica (análise multicritério) e detecção remota (análise multi-espectral)

Abstract

It is essential to know the factors that contribute to the occurrence of forest fires and to map areas susceptible to forest degradation, with a view to enabling the development of specific programs for their mitigation. This study addressed the risk of forest fires in the Matutuine District in Mozambique, with the aim of applying a model of susceptibility to forest fires using geoprocessing techniques for the year 2023, given that the District was devastated by this phenomenon. For this purpose, the multicriteria analysis method integrated with Geographic Information Systems (GIS) was used and then a comparative analysis of the conditioning factors was used using Hierarchical Process Analysis with data on precipitation, temperature, topographic humidity index, land use and cover, slope, digital elevation model, population density, access roads and aspect derived from Remote Sensing. When assigning weights using the AHP method, it was found that precipitation is the factor that contributes most to the occurrence of forest fires and the digital elevation model is the factor with the least impact. In a total of 5,403 km2 of the Matutuine District area, according to the model developed, approximately 30.20% of the area represents low susceptibility to fires and 36.99% of the area presents moderate susceptibility, presenting the largest spatial coverage of the District, while 32.81% of the area presents high susceptibility to the occurrence of forest fires. The high-risk areas are concentrated in the central regions of the District; these areas have denser vegetation and are highly susceptible to the ignition and spread of fires due to the characteristics of the soil and exposure to high temperatures. To validate the model, the Consistency Ratio (CR) method was used, which obtained a result of 0.035 and presented an acceptable consistency as it is below 10%(TRADUÇÃO NOSSA)