Browse > Article
http://dx.doi.org/10.5658/WOOD.2020.48.6.917

A Study on a Development of Automated Measurement Sensor for Forest Fire Surface Fuel Moistures  

YEOM, Chan-Ho (Professional Graduate School of Disaster Prevention, Kangwon National University)
LEE, Si-Young (Professional Graduate School of Disaster Prevention, Kangwon National University)
PARK, Houng-Sek (Graduate School of Forestry, Dongguk University)
WON, Myoung-Soo (Forest Resource Management and Restoration Division, National Institute of Forest Science)
Publication Information
Journal of the Korean Wood Science and Technology / v.48, no.6, 2020 , pp. 917-935 More about this Journal
Abstract
In this study, an automated sensor to measure forest fire surface fuel moistures was developed to predict changes in the moisture content and risk of forest fire surface fuel, which was indicators of forest fire occurrence and spread risk. This measurement sensor was a method of automatically calculating the moisture content of forest fire surface fuel by electric resistance. The proxy of forest fire surface fuel used in this sensor is pine (50 cm long, 1.5 cm in diameter), and the relationship between moisture content and electrical resistance, R(R:Electrical resistance)=2E(E:Exponent of 10)+13X(X:Moisture content)-9.705(R2=0.947) was developed. In addition, using this, the software and case of the automated measurement sensor for forest fire surface fuel moisture were designed to produce a prototype, and the suitability (R2=0.824) was confirmed by performing field monitoring verification in the forest. The results of this study would contribute to develop technologies that can predict the occurrence, spread and intensity of forest fires, and are expected to be used as basic data for advanced forest fire risk forecasting technologies.
Keywords
forest fires; forest fuel humidity; forest fire prediction; Surface fuel; electrical resistance;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 Pang, S.J., Jeong, G.Y. 2019. Effects of Density, Temperature, Size, Grain Angle of Wood, Materials on Nondestructive Moisture Meters, Journal of the Korean Wood Science and Technology 47(1): 40-50   DOI
2 Zahn, S., Henson, C. 2011. Fuel moisture Collection Methods: A Field Guide. U.S. Department of Agriculture. pp.1-11
3 Storey, T.G. 1965. Estimating the Fuel Moisture Content of Indicator Sticks from Selected Weather Variables. U. S. FOREST SERVICE RESEARCH PAPER PSW-26. pp.1-14
4 Van Wagner, C.E. 1975. Comparison of the Canadian and American forest fire danger rating systems. Petawawa forest experiment station Chalk river. Ontario Information report PS-X-59.
5 Han, Y.J., Eom, C.D., Lee, S.M., Park, Y.G. 2019. Moisture Content Change of Korean Red Pine Logs During Air Drying: I. Effective Air Drying Days in Major Regions in Korea, Journal of the Korean Wood Science and Technology 47(6): 721-731.   DOI
6 Yeom, C.H., Lee, S.Y., Park, H.S. 2016. A Comparative Analysis of Fuel Moisture Contents Variation by Pinus densiflora & Quercus variabilis Stick for Estimating Fuel Moisture Contents in Forest Fire Surface Fuel in the Spring. Crisisonomy 12(12): 59-67.
7 Yeom, C.H., Won, M.S., Lee, S.Y., Yoon, S.H., Park, H.S. 2018. A Study on Estimation of Forest Fire Surface Fuel Moisture using Electrical Resistance. Journal of the Korean Society of Hazard Mitigation 18(4): 175-181.   DOI
8 Anderson, H.E. 1983. Predicting wind-driven wild land fire size and shape. Research Paper INT-RP-305. Ogden, UT: USDA Forest Service, Intermountain Forest and Range Experiment Station. p.26.
9 Anderson, H.E. 1990. Moisture diffusivity and response time in fire forest fuels. Canadian Journal of Forest Research. pp.315-325.
10 Kang, C.W., Lim, H.M., Kang, H.Y. 2017. Estimation of Wood Oven-Dry Density by Using a Portable Dielectric Moisture Meter, Journal of the Korean Wood Science and. Technology 45(5): 629-639.
11 Keane, R.E. 2015. Wildland fuel fundamentals and applications. Springer International Publishing Switzerland. DoI 10.1007/978-3-319-09015-3_9. pp.153-154.
12 Lee, C.H. 2005. Actual- and predicted drying curve and moisture profile during air drying of softwood square. Master's thesis. Seoul National University.
13 Kwon, C.G., Lee, S.Y., Lee, H.P., Cha, J.Y. 2011. Comparison of Surface Fuel Moisture after Rainfall in Pine Tree Forests during Spring/Autumn Season in Young Dong Region (Case Study on Surface Fuel and Soil Layer Moisture). Journal of Korean Society of Hazard Mitigation 11(6): 157-166.   DOI
14 Kwon, C.G. 2009. A Study on the Forecast of Fuel Moisture Contents's Changes in the Pine Tree Forest after Rainfall. Master's thesis. Kangwon National University.
15 Kwon, C.G. 2014. A Study on Development of a Forest Fuel Moisture Content Change Prediction Model(Focus on Predicting Forest Fire Dangers). Ph.D. Dissertation. Kangwon National University.
16 Lee, S.Y. 1995. Estimation on forest Fire Danger rating and factors affecting burning behavior. Ph.D. thesis. Dongguk University.
17 Stock, B.J., Alexander, M.E., Lawson, B.D., Van Wanger, C.E. 1987. Canadian Forest Fire Danger Rating System. User' Guide. The Canadian Forest Service Fire Danger Group.
18 Lee, S.Y., Kwon, C.G., Lee, M.W., Lee, H.P. 2010. Development of Prediction Model of Fuel Moisture Changes after Precipitation in the Spring for the Pine Forest Located the Yeongdong Region : Focused on the Down Wood Material Diameter. Transactions of Korean Institute of Fire Science and Engineering. pp.18-26.
19 Lee, S.Y., Chae, H.M., Lee, M.W., Kwon, C.G., Yeom, C.H. 2007. Prediction of Fuel Moisture Contents after Precipitation in the Pine Tree Stand during Forest Fire Period in the East Sea Region. Transactions of Korean Institute of Fire Science and Engineering. pp.464-467.
20 Harrington, M.G. 1983. Climate Class Adjustments Imporve Accuracy of Predicted Fuel Moisture Stick Value. USDA Forest service Research Note RM-431.