Forest Fire Risk Zone Mapping in Mizoram Using RS and GIS

In recent years, India’s Northeastern territory has been plagued by wildfires. Mizoram, as per FSI, has been among the most affected regions, with approximately 20,744-recorded wildfires. Forest fire directly or indirectly affects human health, climate change, and the environment. The forest fire ri...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2023-04, Vol.1164 (1), p.12005
Main Authors: Pramanick, N, Kundu, B, Acharyya, R, Mukhopadhyay, A
Format: Article
Language:English
Subjects:
Accuracy
Climate change
DFI
Fire hazards
Fire risk zone
Forest fire
Forest Fire-Mizoram
Forest fires
Geographic information systems
HFI
Information systems
Mathematical models
MODIS
Parameters
Real time
Reliability analysis
Remote sensing
SFI
Social factors
Socioeconomic factors
Socioeconomics
Wildfires
Zonation
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Summary:In recent years, India’s Northeastern territory has been plagued by wildfires. Mizoram, as per FSI, has been among the most affected regions, with approximately 20,744-recorded wildfires. Forest fire directly or indirectly affects human health, climate change, and the environment. The forest fire risk zonation is generated utilizing remote sensing data, and Geographic Information System based on specified physical and socioeconomic factors. As part of the current study, a Geographical Information System is employed to determine forest fire risk based on predetermined physical and socioeconomic parameters. This study used three distinct models of fire risk zonation index namely FRI (Fire Risk Index), HFI (Hybrid Fire Index), and SFI (Structural Fire Index) to identify the zones in the study area under the risk of forest fires. The Indian state of Mizoram is categorized into five distinct hazard zones based on the probability of wildfire incidents. Fire alerts generated using risk models, and real-time hotspot datasets (forest fire spots) received from MODIS and USGS have been validated. According to the study’s findings, 18.84 km 2 of the study area is at low risk of forest fire, 11.072 km 2 is at moderate risk, and 5.38 km 2 is at high risk. The probability from each metric varies since the input and weightage of the different parameters vary from one another. SFI, therefore, therefore predicts a lower frequency of high-risk wildfire-prone zones than HFI and FRI. In this research, the coefficient of discrimination (R 2 ) is employed to assess the reliability of the projected fire indices being compared with real-time hot spots. Here, FRI possesses the highest accuracy (R 2 =0.892), HFI has moderate accuracy (R 2 =0.676), and FRI has the lowest accuracy (R 2 =0.629).
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/1164/1/012005