Forest Fire Susceptibility Mapping Research Articles

Regional forest-fire susceptibility analysis in central Portugal using a probabilistic ratings procedure and artificial neural network weights assignment

Geographic information system analysis and artificial neural network modelling were combined to evaluate forest-fire susceptibility in the Central Portugal administrative area. Data on forest fire events, indicated by burnt areas during the years from 1990 to 2007, were identified from official records. Topographic, supporting infrastructures, vegetation cover, climatic, demographic and satellite-image data were collected, processed and integrated into a spatial database using geographic information system techniques. Eight fire-related factors were extracted from the collected data, including topographic slope and aspect, road density, viewsheds from fire watchtowers, land cover, Landsat Normalised Difference Vegetation Index, precipitation and population density. Ratings were calculated for the classes or categories of each factor using a frequency-probabilistic procedure. The thematic layers (burnt areas and fire-related factors) were analysed using an advanced artificial neural network model to calculate the relative weight of each factor in explaining the distribution of burnt areas. A forest-fire susceptibility index was calculated using the trained back-propagation artificial neural network weights and the frequency-probabilistic ratings, and then a general forest-fire susceptibility index map was constructed in geographic information system. Burnt areas were used to evaluate the forest-fire susceptibility index map, and the results showed an agreement of 78%. This forest-fire susceptibility map can be used in strategic and operational forest-fire management planning at the regional scale.

Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS)

PurposeIn a tropical country like Malaysia, forest fire is a very common natural and man‐made disaster that prevails in the whole South East Asian region throughout the year. Recently, the haze problem in Malaysia has created a lot of awareness among the government and eco‐tourism sectors. Therefore, detection of the hotspot is very important to delineate the forest fire susceptibility mapping. In this study, remote sensing and geographical information systems (GIS) have been used to evaluate forest fire susceptibility at Sungai Karang and Raja Muda Musa Forest Reserve, Selangor, Malaysia. Frequency ratio model has been applied for the delineation of forest fire mapping for the study area.Design/methodology/approachForest fire locations were identified in the study area from historical hotspots data from year 2000 to 2005 using AVHRR NOAA 12 and NOAA 16 satellite images. Various other supported data such as soil map, topographic data, and agro climate were collected and created using GIS. These data were constructed into a spatial database using GIS. The factors that influence fire occurrence, such as fuel type and Normalized Differential Vegetation Index (NDVI), were extracted from classified Landsat‐7 ETM imagery. Slope and aspect of topography were calculated from topographic database. Soil type was extracted from soil database and dry month code from agroclimate data. Forest fire susceptibility was analyzed using the forest fire occurrence factors by likelihood ratio method.FindingsA new statistical method has been applied for the forest fire susceptibility mapping. The results of the analysis were verified using forest fire location data with the help of a newly written programming code. The validation results show satisfactory agreement between the susceptibility map and the existing data on forest fire location. The GIS was used to analyze the vast amount efficiently, and statistical programs were used to maintain the specificity and accuracy. The result can be used for early warning, fire suppression resources planning and allocation.Originality/valueAll data used in this study are original. The forest fire susceptibility mapping has been done in this study area for the first time. A new program has been coded to cross‐verify the susceptibility map. The results were also verified with field data and other supporting weather data.