DOI QR코드

DOI QR Code

Forest Fire Severity Classification Using Probability Density Function and KOMPSAT-3A

확률밀도함수와 KOMPSAT-3A를 활용한 산불피해강도 분류

  • Lee, Seung-Min (Department of Geoinformatics Engineering, Namseoul University) ;
  • Jeong, Jong-Chul (Department of Geoinformatics Engineering, Namseoul University)
  • 이승민 (남서울대학교 공간정보공학과) ;
  • 정종철 (남서울대학교 공간정보공학과)
  • Received : 2019.10.31
  • Accepted : 2019.11.26
  • Published : 2019.12.31

Abstract

This research deals with algorithm for forest fire severity classification using multi-temporal KOMPSAT-3A image to mapping forest fire areas. The recent satellite of the KOMPSAT series, KOMPSAT-3A, demonstrates high resolution and multi-spectral imagery with infrared and high resolution electro-optical bands. However, there is a lack of research to classify forest fire severity using KOMPSAT-3A. Therefore, the purpose of this study is to analyze forest fire severity using KOMPSAT-3A images. In addition, this research used pre-fire and post-fire Sentinel-2 with differenced Normalized Burn Ratio (dNBR) to taking for burn severity distribution map. To test the effectiveness of the proposed procedure on April 4, 2019, Gangneung wildfires were considered as a case study. This research used the probability density function for the classification of forest fire damage severity based on R software, a free software environment of statistical computing and graphics. The burn severities were estimated by changing NDVI before and after forest fire. Furthermore, standard deviation of probability density function was used to calculate the size of each class interval. A total of five distribution of forest fire severity were effectively classified.

Acknowledgement

Grant : 국토위성정보 수집 및 활용기술개발

Supported by : 국토교통부

References

  1. Choi, J.Y., 2015. Unsupervised Change Detection for Very High-spatial Resolution Satellite Imagery by Using Object-based IR-MAD Algorithm, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 33(4): 297-304 (in Korean with English abstract). https://doi.org/10.7848/ksgpc.2015.33.4.297
  2. Chuvieco, E., M. Martin, and A. Palacios-Orueta, 2002. Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination, International Journal of Remote Sensing, 23(23): 5103-5110. https://doi.org/10.1080/01431160210153129
  3. 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.
  4. Harris, S., S. Veraverbeke, and S. Hook, 2011. Evaluating Spectral Indices for Assessing Fire Severity in Chaparral Ecosystems (Southern California) Using MODIS/ASTER (MASTER) Airborne Simulator Data, Remote Sensing, 3(11): 2403-2419. https://doi.org/10.3390/rs3112403
  5. Jung, M.H., S.H. Lee, E.M. Chang, and S.W. Hong, 2012. Method of Monitoring Forest Vegetation Change based on Change of MODIS NDVI Time Series Pattern, Journal of Korea Spatial Information Society, 20(4): 47-55 (in Korean with English abstract).
  6. Katagis, T., I.Z. Gitas, and G. Mitri, 2014. An Object-Based Approach for Fire History Reconstruction by Using Three Generations of Landsat Sensors, Remote Sensing, 6(6): 5480-5496. https://doi.org/10.3390/rs6065480
  7. Key, C. H. and N. C. Benson, 2006. Landscape Assessment (LA) Sampling and Analysis Methods, In: Lutes, D. C., Keane, R. E., Caratti, J. F., Key, C. H., Benson, N. C., Nathan, C., Sutherland, S., Gangi, L. J. (Ed.), FIREMON: Fire effects monitoring and inventory system, US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USA, pp. LA-1-55, https://doi.org/10.2737/RMRS-GTR-164. https://doi.org/10.2737/RMRS-GTR-164
  8. Kim, T.H., K.H. Kim, G.B. Nam, J.H. Shim, W.J. Choi, and M.H. Cho, 2010. Development of Natural Disaster Damage Investigation System using High Resolution Spatial Images, Korea Spatial Information Society, 12(1): 57-65 (in Korean with English abstract).
  9. Lee, S.H., 2009. Adaptive Reconstruction of NDVI Image Time Series for Monitoring Vegetation Changes, The Korean Society of Remote Sensing, 25(2): 95-105 (in Korean with English abstract).
  10. Lentile, L.B., Z.A. Holden, A.M.S. Smith, M.J. Falkowski, H.D. Hudak, P. Morgan, S.A. Lewis, P.E. Gessler, and N.C. Benson, 2006. Remote sensing techniques to assess active fire characteristics and post-fire effects, International Journal of Wildland Fire, 15(3): 319-345. https://doi.org/10.1071/wf05097
  11. Meng, R., J. Wu, K. Schwager, F. Zhao, P. Dennison, B. Cook, K. Brewster, K. T. Green, and S. Serbin, 2017. Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem, Remote Sensing of Environment, 191: 95-109.
  12. Park, S.W., S.J. Lee, C.Y. Chung, S.R. Chung, I.C. Shin, W.C. Jung, H.S. Mo, S.I. Kim, and Y.W. Lee, 2019. Satellite-based Forest Withering Index for Detection of Fire Burn Area: Its Development and Application to 2019 Kangwon Wildfires, Korean Journal of Remote Sensing, 35(2): 343-346 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2019.35.2.13
  13. Sertel, E. and U. Alganci, 2015. Comparison of pixel and object-based classification for burned area mapping using SPOT-6 images, Geomatics, Natural Hazards and Risk, 7(4): 1198-1206.
  14. Tucker, C., 1979. Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment, 8(2): 127-150. https://doi.org/10.1016/0034-4257(79)90013-0
  15. Wessel, M., M. Brandmeier, and D. Tiede, 2018. Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data, Remote Sensing, 10(9): 1419.
  16. Won, M.S., K.H. Kim, and S.W. Lee, 2014. Analysis of Burn Severity in Large-fire Area Using SPOT5 Images and Field Survey Data, Korean Journal of Agricultural and Forest Meteorology, 16(2): 114-124 (in Korean with English abstract). https://doi.org/10.5532/KJAFM.2014.16.2.114
  17. Wu, Z., B. Middleton, R. Hetzler, J. Vogel, and D. Dye, 2015. Vegetation Burn Severity Mapping Using Landsat-8 and WorldView-2, Photogrammetric Engineering & Remote Sensing, 81(2): 143-154. https://doi.org/10.14358/PERS.81.2.143
  18. Yong, S.S., G.S. Kang, and H.P. Heo, 2016. Current Status and Future Prospects of Satellite Payloads Technology, Korean Society for Aeronautical and Space Sciences, 44(8): 710-717.