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

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 18; p. 3704
• Main Authors: Zhao, Pengcheng, Zhang, Fuquan, Lin, Haifeng, Xu, Shuwen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2021
• Subjects: Accuracy; altitude; case studies; environmental preservation; Fire prevention; fire risk map; Forest & brush fires; forest fire; Forest fires; Forests; Geographic information systems; GIS; Heat; National forests; Natural resources; Normalized difference vegetative index; Precipitation; prediction; Probability; Remote sensing; Resource allocation; risk; Risk allocation; Risk assessment; Risk levels; Science education; temperature; Topography; Tourist attractions; tourists; Vegetation; Jiangsu China; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13183704

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%.