Forest Fire Hazards Vulnerability and Risk Assessment in Sirmaur District Forest of Himachal Pradesh (India): A Geospatial Approach

Abstract

The Himachal Pradesh district’s biggest natural disaster is the forest fire. Forest fire threat evaluation, model construction, and forest management using geographic information system techniques will be important in this proposed report. A simulation was conducted to evaluate the driving forces of fires and their movement, and a hybrid strategy for wildfire control and geostatistics was developed to evaluate the impact on forests. The various methods we included herein are those based on information, such as knowledge-based AHP-crisp for figuring out forest-fire risk, using such variables as forest type, topography, land-use and land cover, geology, geomorphology, settlement, drainage, and road. The models for forest-fire ignition, progression, and action are built on various spatial scales, which are three-dimensional layers. To create a forest fire risk model using three different methods, a study was made to find out how much could be lost in a certain amount of time using three samples. Precedent fire mapping validation was used to produce the risk maps, and ground truths were used to verify them. The accuracy was highest in the form of using “knowledge base” methods, and the predictive value was lowest in the use of an analytic hierarchy process or AHP (crisp). Half of the area, about 53.92%, was in the low-risk to no-risk zones. Very-high- to high-risk zones cover about 24.66% of the area of the Sirmaur district. The middle to northwest regions are in very-high- to high-risk zones for forest fires. These effects have been studied for forest fire suppression and management. Management, planning, and abatement steps for the future were offered as suitable solutions.

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

“…In the northern hemisphere, south-oriented slopes receive more sunlight, and thus the vegetation loses humidity faster and becomes more flammable [28]. Moreover, because of the difference in sunlight distribution among the different orientations of slope, the southern aspects usually have more dense vegetation.…”

“…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].…”

“…After an exhaustive review of the bibliography, we proposed seven factors to be used as criteria in AHP to estimate fire risk: the land cover (LC), the elevation, the aspect, the slope, the TWI, and the distance from roads (DfR) and settlements (DfS). Out of those seven factors, studies have shown that land cover is the most important for estimating fire risk [24,27], especially in cases where the land cover indicates the type of vegetation that covers the area [28,30]. It is evident that having a detailed and updated depiction of land cover is critical for estimating fire risk.…”

Assessment of the Impact of Urbanization on Geoenvironmental Settings Using Geospatial Techniques: A Study of Panchkula District, Haryana

“…Each major Indian city faces the same growing urban expansion problems, such as limited housing, poor transport, poor drainage, unreliable electricity, and insufficient water supply [11]. More and more lorries, buses, vehicles, three-wheelers, and motorbikes spray uncontrolled fumes, all of them contending on urban roads with wandering pedestrians, rickshaws, and cattle [12]. The main causes of the fast urban economic growth and urbanization have, in addition to increasing living standards, been the growth of dense and unplanned residential areas, environmental pollution, the lack of services and amenities, the production of solid waste, and the growth of slums [13,14].…”

Importance of Official Geodata in Disaster Risk Management—Case Study of Croatia

“…A study of the earthquakes of magnitude 3.0 or above occurring during the period between 1980 and 2019 in California and Nevada [5] provides a new perspective for the exploration of the temporal and spatial evolution trends of earthquakes and also for understanding the earthquake risk in this area. Tomar et al [6] recently conducted research that supports the notion that community development and advanced preparation strategies will help to deal effectively with wildfire incidents. They show how an adapted geospatial model was effectively employed in the development of management strategies for forest fire hazard in the Sirmaur district of the Himalayan provinces of India.…”