Browse > Article
http://dx.doi.org/10.7780/kjrs.2019.35.6.3.11

Development of Satellite-based Drought Indices for Assessing Wildfire Risk  

Park, Sumin (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Son, Bokyung (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Im, Jungho (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Lee, Jaese (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Lee, Byungdoo (Department of Forest Conservation, National Institute of Forest Science)
Kwon, ChunGeun (Department of Forest Conservation, National Institute of Forest Science)
Publication Information
Korean Journal of Remote Sensing / v.35, no.6_3, 2019 , pp. 1285-1298 More about this Journal
Abstract
Drought is one of the factors that can cause wildfires. Drought is related to not only the occurrence of wildfires but also their frequency, extent and severity. In South Korea, most wildfires occur in dry seasons (i.e. spring and autumn), which are highly correlated to drought events. In this study, we examined the relationship between wildfire occurrence and drought factors, and developed satellite-based new drought indices for assessing wildfire risk over South Korea. Drought factors used in this study were high-resolution downscaled soil moisture, Normalized Different Water Index (NDWI), Normalized Multi-band Drought Index (NMDI), Normalized Different Drought Index (NDDI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI) and Vegetation Condition Index (VCI). Drought indices were then proposed through weighted linear combination and one-class support vector machine (One-class SVM) using the drought factors. We found that most drought factors, in particular, soil moisture, NDWI, and PCI were linked well to wildfire occurrence. The validation results using wildfire cases in 2018 showed that all five linear combinations produced consistently good performance (> 88% in occurrence match). In particular, the combination of soil moisture and NDWI, and the combination of soil moisture, NDWI, and precipitation were found to be appropriate for representing wildfire risk.
Keywords
Wildfire; drought index; soil moisture downscaling; Random forest; one-class SVM;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 Andrews, P. L., D. O. Loftsgaarden, and L. S. Bradshaw, 2003. Evaluation of fire danger rating indexes using logistic regression and percentile analysis, International Journal of Wildland Fire, 12(2): 213-226.   DOI
2 Breiman, L., 2001. Random forests, Machine Learning, 45(1): 5-32.   DOI
3 Cho, E., M. Choi, and W. Wagner, 2015. An assessment of remotely sensed surface and root zone soil moisture through active and passive sensors in northeast Asia, Remote Sensing of Environment, 160: 166-179.   DOI
4 Dillon, G. K., Z. A. Holden, P. Morgan, M. A. Crimmins, E. K. Heyerdahl, and C. H. Luce, 2011. Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006, Ecosphere, 2(12): 1-33.
5 Du, L., Q. Tian, T. Yu, Q. Meng, T. Jancso, P. Udvardy, and Y. Huang, 2013. A comprehensive drought monitoring method integrating MODIS and TRMM data, International Journal of Applied Earth Observation and Geoinformation, 23: 245-253.   DOI
6 Engman, E. T. and N. Chauhan, 1995. Status of microwave soil moisture measurements with remote sensing, Remote Sensing of Environment, 51(1): 189-198.   DOI
7 Ganatsas, P., M. Antonis, and T. Marianthi, 2011. Development of an adapted empirical drought index to the Mediterranean conditions for use in forestry, Agricultural and Forest Meteorology, 151(2): 241-250.   DOI
8 Gao, B. C., 1996. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, 58(3): 257-266.   DOI
9 Gu, Y., J. F. Brown, J. P. Verdin, and B. Wardlow, 2007. A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States, Geophysical Research Letters, 34(6).
10 Gudmundsson, L., F. C. Rego, M. Rocha, and S. I. Seneviratne, 2014. Predicting above normal wildfire activity in southern Europe as a function of meteorological drought, Environmental Research Letters, 9(8): 084008.   DOI
11 Han, D., Y. J. Kim, J. Im, S. Lee, Y. Lee, and H.-C. Kim, 2018. The Estimation of Arctic Air Temperature in Summer Based on Machine Learning Approaches Using IABP Buoy and AMSR2 Satellite Data, Korean Journal of Remote Sensing, 34(6-2): 1261-1272 (in Korean with English abstract).   DOI
12 Ke, Y., J. Im, S. Park, and H. Gong, 2016. Downscaling of MODIS One kilometer evapotranspiration using Landsat-8 data and machine learning approaches, Remote Sensing, 8(3): 215.   DOI
13 Holden, Z. A., C. H. Luce, M. A. Crimmins, and P. Morgan, 2012. Wildfire extent and severity correlated with annual streamflow distribution and timing in the Pacific Northwest, USA (1984-2005), Ecohydrology, 5(5): 677-684.   DOI
14 Im, J., S. Park, J. Rhee, J. Baik, and M. Choi, 2016. Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches, Environmental Earth Sciences, 75(15): 1120.   DOI
15 Jing, W., Y. Yang, X. Yue, and X. Zhao, 2016. A comparison of different regression algorithms for downscaling monthly satellite-based precipitation over North China, Remote Sensing, 8(10): 835.   DOI
16 Keetch, J. J. and G. M. Byram, 1968. A drought index for forest fire control, Research Paper SE-38, U.S. Forest Service, Southeastern Forest Experiment Station, Asheville, NC, USA.
17 Kogan, F. N., 1995. Droughts of the late 1980s in the United States as derived from NOAA polarorbiting satellite data, Bulletin of the American Meteorological Society, 76(5): 655-668.   DOI
18 Kogan, F., 2002. World droughts in the new millennium from AVHRR-based vegetation health indices, Eos, Transactions American Geophysical Union, 83(48): 557-563.   DOI
19 Kong, I., K. Kim, and Y. Lee, 2017. Sensitivity Analysis of Meteorology-based Wildfire Risk Indices and Satellite-based Surface Dryness Indices against Wildfire Cases in South Korea, Journal of Cadastre & Land InformatiX, 47(2): 107-120 (in Korean with English abstract).   DOI
20 Korea Forest Service, 2019. 2018 Statistical Yearbook of Forest Fire, No. 11-1400000-000424-10, Korean Forest Service, Daejeon, Korea (in Korean).
21 Oshiro, T. M., P. S. Perez, and J. A. Baranauskas, 2012. How many trees in a random forest?, In: Perner P. (eds), Machine Learning and Data Mining in Pattern Recognition, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, Germany, vol. 7376, pp. 154-168.
22 Lee, S. Y., 2010. A review of forest fire occurrence during 1960-2009, Journal of the Korean Society of Hazard Mitigation, 10(3): 51-55 (in Korean).
23 Littell, J. S., D. L. Peterson, K. L. Riley, Y. Liu, and C. H. Luce, 2016. A review of the relationships between drought and forest fire in the United States, Global Change Biology, 22(7): 2353-2369.   DOI
24 Liu, Y., S. Goodrick, and W. Heilman, 2014. Wildland fire emissions, carbon, and climate: Wildfire-climate interactions, Forest Ecology and Management, 317: 80-96.   DOI
25 Park, S., S. Park, J. Im, J. Rhee, J. Shin, and J. Park, 2017. Downscaling gldas soil moisture data in east asia through fusion of multi-sensors by optimizing modified regression trees, Water, 9(5): 332.   DOI
26 Riley, K. L., J. T. Abatzoglou, I. C. Grenfell, A. E. Klene, and F. A. Heinsch, 2013. The relationship of large fire occurrence with drought and fire danger indices in the western USA, 1984?2008: the role of temporal scale, International Journal of Wildland Fire, 22(7): 894-909.   DOI
27 Scott, J. H. and R. E. Burgan, 2005. Standard fire behavior fuel models: a comprehensive set for use with Rothermel' s surface fire spread model, RMRS-GTR-153, U.S. Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USA.
28 Seong, N., M. Seo, K.-S. Lee, C. Lee, H. Kim, S. Choi, and K.-S. Han, 2015. A water stress evaluation over forest canopy using NDWI in Korean peninsula, Korean Journal of Remote Sensing, 31(2): 77-83 (in Korean with English abstract).   DOI
29 Taufik, M., B. I. Setiawan, and H. A. van Lanen, 2015. Modification of a fire drought index for tropical wetland ecosystems by including water table depth, Agricultural and Forest Meteorology, 203: 1-10.   DOI
30 Sung, M.-K., G.-H. Lim, E.-H. Choi, Y.-Y. Lee, M.-S. Won, and K.-S. Koo, 2010. Climate Change over Korea and Its Relation to the Forest Fire Occurrence, Atmosphere, 20(1): 27-35 (in Korean with English abstract).
31 Torgo, L., P. Branco, R. P. Ribeiro, and B. Pfahringer, 2015. Resampling strategies for regression, Expert Systems, 32(3): 465-476.   DOI
32 Wagner, W., G. Lemoine, and H. Rott, 1999. A method for estimating soil moisture from ERS scatterometer and soil data, Remote Sensing of Environment, 70(2): 191-207.   DOI
33 Wang, L. and J. J. Qu, 2007. NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing, Geophysical Research Letters, 34(20).
34 Wang, L., J. J. Qu, X. Hao, and Q. Zhu, 2008. Sensitivity studies of the moisture effects on MODIS SWIR reflectance and vegetation water indices, International Journal of Remote Sensing, 29(24): 7065-7075.   DOI
35 Won, M.-S., Y.-S. Kim, and K.-H. Kim, 2014. Estimation on Greenhouse Gases(GHGs) Emission of Large Forest Fire Area in 2013, Journal of the Korean Association of Geographic Information Studies, 17(3): 54-67 (in Korean with English abstract).   DOI
36 Xiao, J. and Q. Zhuang, 2007. Drought effects on large fire activity in Canadian and Alaskan forests, Environmental Research Letters, 2(4): 044003.   DOI
37 Yoo, C., J. Im, S. Park, and L. J. Quackenbush, 2018. Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data, ISPRS Journal of Photogrammetry and Remote Sensing, 137: 149-162.   DOI
38 Zhang, Y., N. Meratnia, and P. Havinga, 2009. Adaptive and online one-class support vector machinebased outlier detection techniques for wireless sensor networks, Proc. of 2009 International Conference on Advanced Information Networking and Applications Workshops, Bradford, UK, May 26-29, pp. 990-995.
39 Yoon, S.-H. and M.-S. Won, 2016. Correlation Analysis of Forest Fire Occurrences by Change of Standardized Precipitation Index, Journal of the Korean Association of Geographic Information Studies, 19(2): 14-26 (in Korean with English abstract).   DOI