GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing

Abstract

Fire risk prediction is significant for fire prevention and fire resource allocation. Fire risk maps are effective methods for quantifying regional fire risk. Laoshan National Forest Park has many precious natural resources and tourist attractions, but there is no fire risk assessment model. This paper aims to construct the forest fire risk map for Nanjing Laoshan National Forest Park. The forest fire risk model is constructed by factors (altitude, aspect, topographic wetness index, slope, distance to roads and populated areas, normalized difference vegetation index, and temperature) which have a great influence on the probability of inducing fire in Laoshan. Since the importance of factors in different study areas is inconsistent, it is necessary to calculate the significance of each factor of Laoshan. After the significance calculation is completed, the fire risk model of Laoshan can be obtained. Then, the fire risk map can be plotted based on the model. This fire risk map can clarify the fire risk level of each part of the study area, with 16.97% extremely low risk, 48.32% low risk, 17.35% moderate risk, 12.74% high risk and 4.62% extremely high risk, and it is compared with the data of MODIS fire anomaly point. The result shows that the accuracy of the risk map is 76.65%.

Fire Risk Probability Mapping Using Machine Learning Tools and Multi-Criteria Decision Analysis in the GIS Environment: A Case Study in the National Park Forest Dadia-Lefkimi-Soufli, Greece

“…Less humidity and dense vegetation results in higher fire risk, and thus vegetation facing south is more flammable. The fire risk classification of the aspect appears in Table 3 [25,28]. The aspect derives from the DEM and the is presented with different colors depending on the orientations.…”

“…Both of these factors make areas with steeper slopes have a higher risk of fire. We derived the fire risk classification of slope as shown in Table 4 [25]. In order to calculate the slope raster, we used the DEM and chose to present the results in percentage.…”

“…Multiple levels of spatial and nonspatial data and information related to fire risk, such as meteorological data, land cover, vegetation features, and topography, in the form of historical information is combined and evaluated to create detailed fire risk maps [24][25][26]. The information to be used by the fire risk model has to be reliable, the most recent, easy to obtain, and processable with reasonable H/W and S/W resources in order to produce an update fire risk map.…”

“…The key for the analysis is the determination and assignment of the proper weights between all these pieces of information. Many studies, in particular, have used multi-criteria decision analysis (MCDA) in conjunction with the analytic hierarchy process (AHP), which assigns weights to the influencing parameters, so as to successfully develop fire risk maps [14,[24][25][26][27][28]. In the AHP framework, a decision is broken down into a hierarchy of criteria or alternatives, and subsequently one can evaluate the significance of each criterion to the final decision, given the relevant weights between each pair of criteria [29].…”

Deriving Major Fire Risk Evaluation Items Utilizing Spatial Information Convergence Technology in Dense Areas of Small Obsolete Buildings

“…These spatial information convergence technologies and their evaluation items need to secure the suitability of attribute information for its own evaluation target, purpose, and region, and it is important that the evaluation information collected by various methods should be indexed and matched with valid location information [31,32].…”