Integrated approach for the assessment of forest fire risk and burn severity mapping using GIS, AHP method, and Google Earth Engine in Western Algeria

Assessing Fire Risk in Wildland–Urban Interface Regions Using a Machine Learning Method and GIS data: The Example of Istanbul’s European Side

“…Aspect is the information about which direction the land parcels face. In the Northern Hemisphere, slopes facing the south are exposed to sunlight and radiation heat for longer and with greater strength compared to those facing the north [14,16,23,28,38]. Regarding aspect, south-facing slopes are considered to have the highest risk, while north-facing slopes are considered risk-free because they are not directly exposed to sunlight and are exposed to moist northern winds coming from the sea.…”

“…Subsequently, distance maps at 10 m intervals were obtained using the "Euclidean distance" tool. Natural areas have fire risk due to factors such as population density, accidents, negligence, various human activities, slash-and-burn agriculture, and clearing land for settlement, among others [15][16][17]19,26,29,39]. The closer to residential areas, the higher the risk; the risk decreases as the distance increases.…”

“…In studies of fires in WUI areas, some factors that are commonly considered for fires that occur in wildlands and forests are the distance to roads, vegetation, slope and position of houses on hills, construction materials used in homes, fire and support structures, and urban infrastructures, particularly power lines [13]. In the assessment of risks in rural, forest, and vegetation fires, the criteria generally selected include land slope, distance to roads, elevation, distance to water bodies, aspect, types of vegetation, land use, population, climate features, distance to settlements, and types of fuel [14][15][16][17]. Factors such as slope, elevation, aspect, distance to water bodies, and climate features are directly related to geography.…”

“…The factors commonly used for fire risk assessment in forest and rural fires are provided in Table 1 based on a review of modeling studies . The probability of a fire spreading and the damage it will cause are defined as fire risk [16,17]. Toward the end of the 20th century, remote-sensing infrared scanners began to be used to locate forest fires [40].…”

“…Today, thanks to the recurrence interval and widearea imaging capabilities of satellite data, it has become possible to obtain important information about various fires. Technologies such as remote sensing and geographic information systems (GISs) have made significant contributions to fire prevention by providing capabilities for data collection, analysis, and mapping [16]. To model fire risk, it is essential to identify factors that significantly influence the probability of a fire [41].…”