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http://dx.doi.org/10.7780/kjrs.2019.35.6.2.7

Detection of Forest Fire and NBR Mis-classified Pixel Using Multi-temporal Sentinel-2A Images  

Youn, Hyoungjin (Department of Geoinformatics Engineering, Namseoul University)
Jeong, Jongchul (Department of Geoinformatics Engineering, Namseoul University)
Publication Information
Korean Journal of Remote Sensing / v.35, no.6_2, 2019 , pp. 1107-1115 More about this Journal
Abstract
Satellite data play a major role in supporting knowledge about forest fire by delivering rapid information to map areas damaged. This study, we used 7 Sentinel-2A images to detect change area in forests of Sokcho on April 4, 2019. The process of classify forest fire severity used 7 levels from Sentinel-2A dNBR(differenced Normalized Burn Ratio). In the process of classifying forest fire damage areas, the study selected three areas with high regrowth of vegetation level and conducted a detailed spatial analysis of the areas concerned. The results of dNBR analysis, regrowth of coniferous forest was greater than broad-leaf forest, but NDVI showed the lowest level of vegetation. This is the error of dNBR classification of dNBR. The results of dNBR time series, an area of forest fire damage decreased to a large extent between April 20th and May 3rd. This is an example of the regrowth by developing rare-plants and recovering broad-leaf plants vegetation. The results showed that change area was detected through the change detection of danage area by forest category and the classification errors of the coniferous forest were reached through the comparison of NDVI and dNBR. Therefore, the need to improve the precision Korean forest fire damage rating table accompanied by field investigations was suggested during the image classification process through dNBR.
Keywords
Forest Fire; Sentinel-2A; NBR; Forest Classification; Change Detection;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Filipponi, F., 2019. Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires, Remote Sensing, 11(6): 622.   DOI
2 Jin, Y.J. T. Randerson, S.J. Goetz, P.S.A. Beck, M.M. Loranty, and M.L. Goulden, 2012. The influence of burn severity on postfire vegetation recovery and albedo change during early succession in North American boreal forests, Journal of Geophysical Research, 117(G1): 1-15.
3 Karau, E.C. and R.E. Keane, 2010. Burn severity mapping using simulation modelling and satellite imagery, International Journal of Wildland Fire, 19(6): 710-724.   DOI
4 Kim, D.Y., 2014. Spatial Analysis for Forest Fire Using GIS, The Geographical Journal of Korea, 48(3): 325-336 (in Korean with English abstract).
5 Lee, B.D., J.E. Song, M.B. Lee, and J.S. Chung, 2008. The Relationship between Characteristics of Forest Fires and Spatial Patterns of Forest Types by the Ecoregions of South Korea, Journal of Korean Forest Society, 97(1): 1-9 (in Korean with English abstract).
6 Lee, H.P., S.Y. Lee and Y.J. Park, 2009. A Study on Combustion of Living Leaves for Various Coniferous Trees and Broadleaf Trees in Young dong Areas, Journal of the Korean Society of Safety, 24(4): 95-103 (in Korean with English abstract).
7 Lee, S.J., K.J. Kim, Y.H. Kim, J.W. Kim, and Y.W. Lee, 2017. Development of FBI(Fire Burn Index) for Sentinel-2 images and an experiment for detection of burned areas in Korea, Journal of the Association of Korean Photo-geographers, 27(4): 187-202 (in Korean with English abstract).   DOI
8 Lee, S.Y., S.Y. Han, S.H. An, J.S. Oh, and M.H. Jo, 2001. Regional Analysis of Forest Fire Occurrence Factors in Kangwon Province, Korean Journal of Agriculture and Forest Meteorology, 3(3): 135-142 (in Korean with English abstract).
9 Lutz, J.A., C.H. Key, C.A. Kolden, J.T. Kane, and J.W. Wagtendonk, 2011. Fire frequency, area burned, and severity: A quantitative approach to defining a normal fire year, Fire Ecology, 7(2): 51-65.   DOI
10 Navarro, G., I. Caballero, G. Silva, P.C. Parra, A. Vazquez, and R. Caldeira, 2017. Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery, International Journal of Applied Earth Observation and Geoinformation, 58: 97-106.   DOI
11 Won, M.S., K.S. Koo, and M.B. Lee, 2007. An Quantitative Analysis of Severity Classification and Burn Severity for the Large Forest Fire Areas using Normalized Burn Ratio of Landsat Imagery, Journal of the Korean Association of Geographic Information Studies, 10(3): 80-92 (in Korean with English abstract).
12 Roteta, E., A. Bastarrika, M. Padilla, T. Storm, and E. Chuvieco, 2019. Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa, Remote Sensing of Environment, 222: 1-17.   DOI
13 Schepers, L., B. Haest, S. Veraverbeke, T. Spanhove, J.V. Borre, and R. Goossens, 2014. Burned area detection and burn severity assessment of a heathland fire in Belgium using airborne imaging spectroscopy (APEX), Remote Sensing, 6(3): 1803-1826.   DOI