Histogram Matching of Sentinel-2 Spectral Information to Enhance Planetscope Imagery for Effective Wildfire Damage Assessment

  • Kim, Minho (Department of Civil and Environmental Engineering, Seoul National University) ;
  • Jung, Minyoung (Department of Civil and Environmental Engineering, Seoul National University) ;
  • Kim, Yongil (Department of Civil and Environmental Engineering, Seoul National University)
  • Received : 2019.07.28
  • Accepted : 2019.08.16
  • Published : 2019.08.31


In abrupt fire disturbances, high quality images suitable for wildfire damage assessment can be difficult to acquire. Quantifying wildfire burn area and severity are essential measures for quick short-term disaster response and efficient long-term disaster restoration. Planetscope (PS) imagery offers 3 m spatial and daily temporal resolution, which can overcome the spatio-temporal resolution tradeoff of conventional satellites, albeit at the cost of spectral resolution. This study investigated the potential of augmenting PS imagery by integrating the spectral information from Sentinel-2 (S2) differenced Normalized Burn Ratio (dNBR) to PS differenced Normalized Difference Vegetation Index (dNDVI) using histogram matching,specifically for wildfire burn area and severity assessment of the Okgye wildfire which occurred on April 4th, 2019. Due to the difficulty in acquiring reference data, the results of the study were compared to the wildfire burn area reported by Ministry of the Interior and Safety. The burn area estimates from this study demonstrated that the histogram-matched (HM) PS dNDVI image produced more accurate burn area estimates and more descriptive burn severity intervals in contrast to conventional methods using S2. The HM PS dNDVI image returned an error of only 0.691% whereas the S2 dNDVI and dNBR images overestimated the wildfire burn area by 5.32% and 106%, respectively. These improvements using PS were largely due to the higher spatial resolution, allowing for the detection of sparsely distributed patches of land and narrow roads, which were indistinguishable using S2 dNBR. In addition, the integration of spectral information from S2 in the PS image resolved saturation effects in areas of low and high burn severity.


Supported by : Ministry of Interior and Safety (MOIS)


  1. Brewer, C. K., J. C. Winne, R. L. Redmond, D. W. Opitz, and M. V. Mangrich, 2005. Classifying and mapping wildfire severity, Photogrammetric Engineering and Remote Sensing, 71(11): 1311-1320.
  2. Cansler, C. A. and D. McKenzie, 2012. How robust are burn severity indices when applied in a new region? Evaluation of alternate field-based and remote-sensing methods, Remote Sensing, 4(2): 456-483.
  3. Chuvieco, E., D. Riano, F. M. Danson, and P. Martin, 2006. Use of a radiative transfer model to simulate the postfire spectral response to burn severity, Journal of Geophysical Research: Biogeosciences, 111(G4).
  4. Cocke, A. E., P. Z. Fule, and J. E. Crouse, 2005. Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data, International Journal of Wildland Fire, 14(2): 189-198.
  5. Cooley, S., L. Smith, L. Stepan, and J. Mascaro, 2017. Tracking dynamic northern surface water changes with high-frequency Planet CubeSat imagery, Remote Sensing, 9(12): 1306.
  6. Cooley, S. W., L. C. Smith, J. C. Ryan, L. H. Pitcher, and T. M. Pavelsky, 2019. Arctic-boreal lake dynamics revealed using CubeSat imagery, Geophysical Research Letters, 46(4): 2111-2120.
  7. Dragozi, E., I. Z. Gitas, S. Bajocco, and D. G. Stavrakoudis, 2016. Exploring the relationship between burn severity field data and very high resolution GeoEye images: the case of the 2011 Evros wildfire in Greece, Remote Sensing, 8(7): 566.
  8. Escuin, S., R. Navarro, and P. Fernandez, 2008. Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images, International Journal of Remote Sensing, 29(4): 1053-1073.
  9. Fernandez-Garcia, V., M. Santamarta, A. Fernandez-Manso, C. Quintano, E. Marcos, and L. Calvo, 2018. Burn severity metrics in fireprone pine ecosystems along a climatic gradient using Landsat imagery, Remote Sensing of Environment, 206: 205-217.
  10. Fernandez-Manso, O., C. Quintano, and A. Fernandez-Manso, 2009. Combining spectral mixture analysis and object-based classification for fire severity mapping, Forest Systems, 18(3): 296-313.
  11. Fernandez-Manso, A., O. Fernandez-Manso, and C. Quintano, 2016. SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity, International Journal of Applied Earth Observation and Geoinformation, 50: 170-175.
  12. Fornacca, D., G. Ren, and W. Xiao, 2018. Evaluating the best spectral indices for the detection of burn scars at several post-fire dates in a mountainous region of Northwest Yunnan, China, Remote Sensing, 10(8): 1196.
  13. Gonzalez, R. C. and R. E. Woods, 2002. Digital Image Processing, Prentice Hall Press, Upper Saddle River, NJ, USA.
  14. Henry, M. C., 2008. Comparison of single-and multi-date Landsat data for mapping wildfire scars in Ocala National Forest, Florida, Photogrammetric Engineering and Remote Sensing, 74(7): 881-891.
  15. Houborg, R. and M. McCabe, 2016. High-resolution NDVI from Planet's constellation of earth observing nano-satellites: a new data source for precision agriculture, Remote Sensing, 8(9): 768.
  16. Houborg, R. and M. McCabe, 2018a. A Cubesat enabled spatio-temporal enhancement method (CESTEM) utilizing Planet, Landsat and MODIS data, Remote Sensing of Environment, 209: 211-226.
  17. Houborg, R. and M. McCabe, 2018b. Daily retrieval of NDVI and LAI at 3 m resolution via the fusion of CubeSat, Landsat, and MODIS data, Remote Sensing, 10(6): 890.
  18. Jang, E., Y. Kang, J. Im, D. W. Lee, J. Yoon, and S. K. Kim, 2019. Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea, Remote Sensing, 11(3): 271.
  19. Key, C. H. and N. C. Benson, 2003. The Normalized Burn Ratio (NBR): A Landsat TM radiometric measure of burn severity, U.S. Department of the Interior, U.S. Geological Survey, Northern Rocky Mountain Science Center.
  20. Key, C. H. and N. C. Benson, 2006. Landscape Assessment (LA), 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, General Technical Report RMRS-GTR-164-CD, Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station, p. LA-1-55,
  21. Kontoes, C. C., H. Poilve, G. Florsch, I. Keramitsoglou, and S. Paralikidis, 2009. A comparative analysis of a fixed thresholding vs. a classification tree approach for operational burn scar detection and mapping, International Journal of Applied Earth Observation and Geoinformation, 11(5): 299-316.
  22. Lentile, L. B., Z. A. Holden, A. M. Smith, M. J. Falkowski, A. T. 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.
  23. Levin, N. and A. Heimowitz, 2012. Mapping spatial and temporal patterns of Mediterranean wildfires from MODIS, Remote Sensing of Environment, 126: 12-26.
  24. Louis, J., V. Debaecker, B. Pflug, M. Main-Knorn, J. Bieniarz, U. Mueller-Wilm, E. G. Cadau and F. Gascon, 2016. S2 SEN2COR: L2A processor for users, Proc. of the Living Planet Symposium, Prague, Czech Republic, May 9-13, pp. 9-13.
  25. McFeeters, S. K., 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, International Journal of Remote Sensing, 17(7): 1425-1432.
  26. Meng, R., J. Wu, K. L. Schwager, F. Zhao, P. E. Dennison, B. D. Cook, K. Brewster, T. M. Green, and S. O. 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.
  27. Michael, Y., I. Lensky, S. Brenner, A. Tchetchik, N. Tessler, and D. Helman, 2018. Economic assessment of fire damage to urban forest in the wildland-urban interface using Planet satellites constellation images, Remote Sensing, 10(9): 1479.
  28. Miller, J. D. and A. E. Thode, 2007. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR), Remote Sensing of Environment, 109(1): 66-80.
  29. Ministry of the Interior and Safety, 2019. 20190418 Gangwon East-Sea Irwon wildfire response - recovery report (11AM), 70191, Accessed on Jul. 22, 2019 (in Korean).
  30. Navarro, G., I. Caballero, G. Silva, P. C. Parra, A. Vazquez, and R. Caldeira, 2017. Evaluation of forest fire on Madeira Island using S2A MSI imagery, International Journal of Applied Earth Observation and Geoinformation, 58: 97-106.
  31. Quintano, C., A. Fernandez-Manso, and D. A. Roberts, 2013. Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries, Remote Sensing of Environment, 136: 76-88.
  32. Sadeh, Y., X. Zhu, K. Chenu, and D. Dunkerley, 2019. Sowing date detection at the field scale using CubeSats remote sensing, Computers and Electronics in Agriculture, 157: 568-580.
  33. Sobrino, J. A., R. Llorens, C. Fernandez, J. M. Fernandez-Alonso, and J. A. Vega, 2019. Relationship between soil burn severity in forest fires measured in situ and through spectral indices of remote detection, Forests, 10(5): 457.
  34. Viana-Soto, A., I. Aguado, and S. Martinez, 2017. Assessment of post-fire vegetation recovery using fire severity and geographical data in the Mediterranean region (Spain), Environments, 4(4): 90.
  35. Wicaksono, P. and W. Lazuardi, 2018. Assessment of PS images for benthic habitat and seagrass species mapping in a complex optically shallow water environment, International Journal of Remote Sensing, 39(17): 5739-5765.