Creation of Wildfire Opportunity Predictive Model from Climate Variability with Geo-informatics in Omkoi District, Chiang Mai Province
Abstract
This study aimed is 1) to study the difference of hotspot patterns from climate variability and 2) to study the relationship of physical factors involved in wildfire, leading to wildfire opportunity predictive model from climate variability in Omkoi District Study Area, Chiang Mai Province. The period of times of this study are El Niño year (2015 and 2019) normal year (2013 and 2017), and La Niña year (2011 and 2021). This study uses Landsat 8 and ALOS PALSAR satellite images to analyze physical factors affecting the wildfires. Landsat 8 were used to analyze physical factors which are drought, land surface temperature, forest type and above-ground carbon sequestration. And, ALOS PALSAR was used to analyze digital elevation model and slope. In addition, the hotspot from Terra and Aqua data, MODIS sensor, were used to analyze hotspot patterns conducted by kernel, quadrate, and nearest neighbor analysis to determine the density, dispersion and cluster of hotspots. Wildfire opportunity predictive model is create using linear regression analysis. In result of study found that El Niña year has the highest hotspot density, followed by normal and La Niña years, respectively. Moreover, physical factors influencing wildfires are drought factors and land surface temperature. And wildfire opportunity predictive model found that model in El Niño year has the highest precision followed by La Niña year, and normal year with the accuracy of 70.2, 54.6 and 50.7 percent, respectively. Keywords : wildfire predictive model ; physical factors ; climate variability ; hotspot patternsReferences
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Yan, L., Michael, J.H., Xiaoyang, Z., Zhuosen, W., Andrew, D., Richardson, K.H., Gianluca, F., Dennis D.S., Siyan, M., Joseph, V., & Crystal, B.S. (2017). agricultural of forest management. Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales. 49(11), 237-238. Retrieved March 2, 2021, from https://www.sciencedirect.com/science/article/pii/S0168192317300692.
Yotapakdee, T. (2014). Seconomic Evaluation Non-timber Forest Products Benefits Omkoi National Forest Reserve. Area Based Development Research Journal, 6(4), 23-41. (in Thai)
Guilherme, A.V.M., Maria, E., Siqueira, S., Gabriel, P., Francielle,D.S.C., Fernando, S.K., Gabriel, B., Julio, C.C., Raquel, C.R., & Viviane, V. (2018). Satellite observations for describing fire patterns and climate-related fire drivers in the Brazilian savannas. Nat.Hazards Earth Syst. Sci., 18(1), 125–144.
Rayanakorn, M. (2010). Smog and air pollution. IN Chiang Mai. Haze and Air Pollution in Chiang Mai 2553, (pp.2-10). Chiang Mai: Chiang Mai University School of Public Policy. (in Thai)
Rock, B.N.V., WILLIAMS, J.E, VOGELMANN, D.L., & HOSHISAKI, A.F.. (1986). BioScience. Remote detection of forest damage. 36(7), 439-445.
Thanadolmethaphorn, P., Chotiamonsak, C., & Dontre, S., (2018). Analysis of Impact of Climate Change on Forest Fire Potential in Chiang Mai by Using of Regression Model. Retrieved October 4, 2021, from http://dx.doi.org/10.14416/j.kmutnb.2018.09.004. (in Thai)
Yan, L., Michael, J.H., Xiaoyang, Z., Zhuosen, W., Andrew, D., Richardson, K.H., Gianluca, F., Dennis D.S., Siyan, M., Joseph, V., & Crystal, B.S. (2017). agricultural of forest management. Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales. 49(11), 237-238. Retrieved March 2, 2021, from https://www.sciencedirect.com/science/article/pii/S0168192317300692.
Yotapakdee, T. (2014). Seconomic Evaluation Non-timber Forest Products Benefits Omkoi National Forest Reserve. Area Based Development Research Journal, 6(4), 23-41. (in Thai)
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Published
2023-05-11
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