Forest fires are the major cause of degradation of forest. Forest fires have caused substantial damage in the state of Karnataka in terms of economic, social, environmental impacts on humans and also loss of biodiversity. Fire risk indices are important tools for the management of forest fires. They are developed based on static and/or dynamic factors influencing the occurrence of fire and propagation of fire. The objective of the present study was to develop a new static fire risk index based on parameters influencing forest fire such as fuel type, elevation, slope, aspect, terrain ruggedness, proximity to a road, proximity to water bodies and proximity to settlements. MODIS Land cover type yearly L3 global 500m SIN grid(MCD12Q1) was used to compute fuel type index based on historical fire data, SRTM DEM was used to compute slope index, aspect index, elevation index, and terrain ruggedness index. Road index, settlement index, and water body index were developed from the proximity maps generated. A geographic information system (GIS) was utilized adequately to join diverse forest fire causing factors for demarcating static fire risk index. The evaluated exactness was around 87%, i.e., the developed GIS-based static fire risk index of the examination zone was observed to be in solid concurrence with actual fire affected regions. The study area exhibited 32.38% prone to fire risk.
[1]
Nilton Cesar Fiedler,et al.
Applying GIS to develop a model for forest fire risk: A case study in Espírito Santo, Brazil.
,
2016,
Journal of environmental management.
[2]
R. Rothermel,et al.
How to Predict the Spread and Intensity of Forest and Range Fires
,
2017
.
[3]
Arijit Roy,et al.
Developing the static fire danger index using geospatial technology
,
2016,
2016 2nd International Conference on Contemporary Computing and Informatics (IC3I).
[4]
E. Chuvieco,et al.
Application of remote sensing and geographic information systems to forest fire hazard mapping.
,
1989
.
[5]
S Ramachandran,et al.
Application of Remote Sensing and Gis
,
2022
.
[6]
Pedro A. Hernandez-Leal,et al.
Synergy of GIS and Remote Sensing Data in Forest Fire Danger Modeling
,
2008,
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[7]
S. Mukherjee,et al.
Forest fire risk zone mapping from satellite imagery and GIS
,
2002
.
[8]
Mahendra Singh Nathawat,et al.
Geospatial Approach for Forest Fire Risk Modeling: a Case Study of Taradevi Range of Shimla Forest Division in Himachal Pradesh, India
,
2011
.