Assessing Fire Risk Zones in Phrae Province, Northern Thailand, Using a MaxEnt Model

Torlarp Kamyo (), Punchaporn Kamyo, Kanyakorn Panthong, Itsaree Howpinjai, Ratchaneewan Kamton and Lamthai Asanok ()
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Torlarp Kamyo: Department of Agroforestry, Maejo University, Phrae Campus, Phrae 54140, Thailand
Punchaporn Kamyo: Department of Political Science, Maejo University, Phrae Campus, Phrae 54140, Thailand
Kanyakorn Panthong: Department of Forest Management, Maejo University, Phrae Campus, Phrae 54140, Thailand
Itsaree Howpinjai: Department of Forest Industry Technology, Maejo University, Phrae Campus, Phrae 54140, Thailand
Ratchaneewan Kamton: Department of Community Management Innovation, Maejo University, Phrae Campus, Phrae 54140, Thailand
Lamthai Asanok: Department of Agroforestry, Maejo University, Phrae Campus, Phrae 54140, Thailand

Geographies, 2025, vol. 5, issue 3, 1-14

Abstract: This study aimed to investigate the physical factors influencing the occurrence of forest fires and to create a fire risk map of Phrae Province. Remote sensing and geographic information system (GIS) technology were applied for the analysis, focusing on seven factors: the digital elevation model (DEM); slope; Normalized Difference Vegetation Index (NDVI); aspect; and distances from people, water, and roads. All of these geographical factors can affect forest fires. This resulted in a MaxEnt (Maximum Entropy) model with an AUC (area under the curve) of 0.849, indicating its great prediction ability. The findings revealed that the variables influencing forest fire incidence were the DEM, NDVI, slope, distance from roads, distance from water, distance from communities, and aspect, in that order. Subsequently, a fire risk map for wildfires was developed by reclassifying the data into five levels—very low risk, low risk, medium risk, high risk, and very high risk—accounting for 341,395.54, 88,132.64, 76,162.41, 81,157.55, and 57,384.10 hectares or 52.99, 13.68, 11.82, 12.60, and 8.91% of the total area, respectively. The areas classified as very high risk, high risk, medium risk, and low risk included the Song, Long, and Rong Kwang Districts. The area with the lowest risk was Nong Muang Khai District.

Keywords: assessing fire risk; Phrae Province; Northern Thailand; MaxEnt model (search for similar items in EconPapers)
JEL-codes: Q1 Q15 Q5 Q53 Q54 Q56 Q57 (search for similar items in EconPapers)
Date: 2025
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