Crown fires represent an extreme fire behavior that leads to high fire severity, and dryness plays a vital role in this behavior. Due to the lack of fire severity data in Tohoku, high fire severity was estimated using a satellite-based high-fire-severity index (HFSI). HFSI is the ratio of the identified area of high fire severity sensed using the Landsat-differenced normalized difference vegetation index (dNDVI) to the reported total burnt area. Using the HFSI, only six wildfires could be identified as having high fire severity areas from an evaluation of 55 wildfires with burnt areas greater than 0.1 km² reported in Tohoku from 1995 to 2017. The low HFSI values computed for these wildfires implied that fireline intensity was not high for crown fires to occur in Tohoku. Additionally, the soil moisture (SM) content for three layers, the surface, root, and recharge zones simulated using a land surface model (the Simple Biosphere Model including Urban Canopy (SiBUC) model), were used to assess the dryness. The highest HFSI value calculated among all wildfires was that of the largest wildfire that ever occurred in Japan in the period between 1995 and 2017, the 2017 Kamaishi wildfire. The conditions before this fire were among the driest of the six wildfires with HFSI values.

1. [1] A. C. Scott, D. M. J. S. Bowman, W. J. Bond, S. J. Pyne, and M. E. Alexander, “Fire on Earth: An Introduction,” Wiley-Blackwell, 2013.
2. [2] K. C. Ryan, “Techniques for assessing fire damage to trees,” Proc. of 1982 Joint Fire Council Meeting: Fire – Its Field Effects, pp. 1-11, 1982.
3. [3] M. G. Turner, W. H. Romme, and R. H. Gardner, “Prefire heterogeneity, fire severity, and early postfire plant reestablishment in subalpine forests of Yellowstone National Park, Wyoming,” Int. J. Wildl. Fire, Vol.9, No.1, pp. 21-36, doi: 10.1071/wf99003, 1999.
4. [4] C. E. Van Wagner, “Conditions for the start and spread of crown fire,” Can. J. For. Res., Vol.7, No.1, pp. 23-34, doi: 10.1139/x77-004, 1977.
5. [5] Y. Goto, K. Tamai, T. Miyama, and Y. Kominami, “Forest fire intensity in Japan: Estimation of byram’s fireline intensity using Rothermel’s fire spread model,” Nihon Ringakkai Shi/J. Japanese For. Soc., Vol.87, No.3, pp. 193-201, doi: 10.4005/jjfs.87.193, 2005 (in Japanese).
6. [6] R. C. Rothermel, “A mathematical model for predicting fire spread in wildland fuels,” USDA Forest Service Research Paper INT-115, 40pp., 1972, https://www.fs.usda.gov/treesearch/pubs/32533 [accessed September 24, 2020]
7. [7] L. B. Lentile et al., “Remote sensing techniques to assess active fire characteristics and post-fire effects,” Int. J. of Wildland Fire, Vol.15, No.3. pp. 319-345, doi: 10.1071/WF05097, 2006.
8. [8] J. W. van Wagtendonk, R. R. Root, and C. H. Key, “Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity,” Remote Sens. Environ, Vol.92, No.3, pp. 397-408, doi: 10.1016/J.RSE.2003.12.015, 2004.
9. [9] A. E. Cocke, P. Z. Fulé, and J. E. Crouse, “Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data,” Int. J. Wildl. Fire, Vol.14, No.2, pp. 189-198, doi: 10.1071/WF04010, 2005.
10. [10] J. Epting, D. Verbyla, and B. Sorbel, “Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+,” Remote Sens. Environ, Vol.96, Nos.3-4, pp. 328-339, doi: 10.1016/j.rse.2005.03.002, 2005.
11. [11] D. P. Roy, L. Boschetti, and S. N. Trigg, “Remote sensing of fire severity: assessing the performance of the normalized burn ratio,” IEEE Geosci. Remote Sens. Lett., Vol.3, No.1, pp. 112-116, doi: 10.1109/LGRS.2005.858485, 2006.
12. [12] D. M. El-Shikha et al., “Remote sensing of cotton nitrogen status using the Canopy Chlorophyll Content Index (CCCI),” Trans. ASABE, Vol.51, No.1, pp. 73-82, doi: 10.13031/2013.24228, 2008.
13. [13] J. A. Gamon et al., “Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types,” Ecol. Appl., Vol.5, No.1, pp. 28-41, doi: 10.2307/1942049, 1995.
14. [14] F. Maselli, S. Romanelli, L. Bottai, and G. Zipoli, “Use of NOAA-AVHRR NDVI images for the estimation of dynamic fire risk in Mediterranean areas,” Remote Sens. Environ., Vol.86, No.2, pp. 187-197, doi: 10.1016/S0034-4257(03)00099-3, 2003.
15. [15] J. E. Keeley, “Fire intensity, fire severity and burn severity: A brief review and suggested usage,” Int. J. Wildl. Fire, Vol.18, No.1, pp. 116-126, doi: 10.1071/WF07049, 2009.
16. [16] A. Dai, “Increasing drought under global warming in observations and models,” Nat. Clim. Chang., Vol.3, No.1, pp. 52-58, doi: 10.1038/nclimate1633, 2013.
17. [17] O. Pechony and D. T. Shindell, “Driving forces of global wildfires over the past millennium and the forthcoming century,” Proc. Natl. Acad. Sci. U.S.A., Vol.107, No.45, pp. 19167-19170, doi: 10.1073/pnas.1003669107, 2010.
18. [18] Core Writing Team, R. K. Pachauri, and L. A. Meyer (Eds.), “Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,” IPCC, 2014, http://www.ipcc.ch [accessed April 3, 2019]
19. [19] J. Lecina-Diaz, A. Alvarez, and J. Retana, “Extreme fire severity patterns in topographic, convective and wind-driven historical wildfires of Mediterranean pine forests,” PLOS ONE, Vol.9, No.1, Article No.e85127, doi: 10.1371/journal.pone.0085127, 2014.
20. [20] J. S. Crosby, “Vertical wind currents and fire behavior,” Fire Control Notes, Vol.10, No.2, pp. 12-15, 1949.
21. [21] P. A. Werth et al., “Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists,” USDA, doi: 10.2737/PNW-GTR-891, 2016.
22. [22] A. L. R. Westerling, “Increasing western US forest wildfire activity: Sensitivity to changes in the timing of spring,” Philos. Trans. R. Soc. B Biol. Sci., Vol.371, Issue 1696, Article No.20150178, doi: 10.1098/rstb.2015.0178, 2016.
23. [23] M. Moriondo et al., “Potential impact of climate change on fire risk in the Mediterranean area,” Clim. Res., Vol.31, No.1, pp. 85-95, doi: 10.3354/cr031085, 2006.
24. [24] K. Ruosteenoja, T. Markkanen, A. Venäläinen, P. Räisänen, and H. Peltola, “Seasonal soil moisture and drought occurrence in Europe in CMIP5 projections for the 21st century,” Clim. Dyn., Vol.50, Nos.3-4, pp. 1177-1192, doi: 10.1007/s00382-017-3671-4, 2018.
25. [25] Y. Liu, J. Stanturf, and S. Goodrick, “Trends in global wildfire potential in a changing climate,” For. Ecol. Manage., Vol.259, No.4, pp. 685-697, doi: 10.1016/j.foreco.2009.09.002, 2010.
26. [26] Vinodkumar and I. Dharssi, “Evaluation and calibration of a high-resolution soil moisture product for wildfire prediction and management,” Agric. For. Meteorol., Vol.264, pp. 27-39, doi: 10.1016/j.agrformet.2018.09.012, 2019.
27. [27] Fire and Disaster Management Agency, “Fire report 1995-2017.”
28. [28] Y. Touge, G. P. Emang, S. Kazama, Y. Takahashi, and K. Sasaki, “Introduction of the Tohoku Forest Fires on May 2017; case in Kamaishi city of Iwate Prefecture and Kurihara city of Miyagi Prefecture,” J. Jpn. Soc. Nat. Disaster Sci., Vol.36, No.4, pp. 361-370, doi: 10.24762/jndsj.36.4_361, 2018 (in Japanese).
29. [29] Vinodkumar et al., “Comparison of soil wetness from multiple models over Australia with observations,” Water Resour. Res., Vol.53, No.1, pp. 633-646, doi: 10.1002/2015WR017738, 2017.
30. [30] C. M. Holgate et al., “Comparison of remotely sensed and modelled soil moisture data sets across Australia,” Remote Sens. Environ., Vol.186, pp. 479-500, doi: 10.1016/j.rse.2016.09.015, 2016.
31. [31] V. Kumar and I. Dharssi, “Evaluation of daily soil moisture deficit used in Australian forest fire danger rating system,” Bureau Research Report No.22, Commonwealth of Australia, 2017.
32. [32] Japan Aerospace Exploration Agency (JAXA), “High-resolution land use and land cover map,” 2016, https://www.eorc.jaxa.jp/ALOS/en/lulc/lulc_index.htm [accessed July 8, 2018]
33. [33] J. G. Masek et al., “A landsat surface reflectance dataset for North America, 1990-2000,” IEEE Geosci. Remote Sens. Lett., Vol.3, No.1, pp. 68-72, doi: 10.1109/LGRS.2005.857030, 2006.
34. [34] E. Vermote, C. Justice, M. Claverie, and B. Franch, “Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product,” Remote Sens. Environ., Vol.185, pp. 46-56, doi: 10.1016/j.rse.2016.04.008, 2016.
35. [35] G. P. Emang, Y. Touge, and S. Kazama, “Evaluating trees crowns damage for the 2017 largest wildfire in Japan using Sentinel-2A NDMI,” Proc. of 2020 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS 2020), pp. 6794-6797, doi: 10.1109/IGARSS39084.2020.9323345, 2020.
36. [36] D. P. Roy et al., “Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity,” Remote Sens. Environ., Vol.185, pp. 57-70, doi: 10.1016/j.rse.2015.12.024, 2016.
37. [37] J. E. Vogelmann et al., “Effects of Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus radiometric and geometric calibrations and corrections on landscape characterization,” Remote Sens. Environ., Vol.78, Nos.1-2, pp. 55-70, doi: 10.1016/S0034-4257(01)00249-8, 2001.
38. [38] K. Tanaka, “Development of the new land surface scheme SiBUC commonly applicable to basin water management and numerical weather prediction model,” Ph.D. Thesis, Kyoto University, 2005.
39. [39] P. J. Sellers et al., “A revised land surface parameterization (SiB2) for atmospheric GCMS. Part I: Model formulation,” J. Clim, Vol.9, No.4, pp. 676-705, doi: 10.1175/1520-0442(1996)009<0676_ARLSPF>2.0.CO;2, 1996.
40. [40] S. Kobayashi et al., “The JRA-55 Reanalysis: General Specifications and Basic Characteristics,” J. Meteorol. Soc. Japan. Ser. II, Vol.93, No.1, pp. 5-48, doi: 10.2151/jmsj.2015-001, 2015.
41. [41] Y. Touge, G. P. Emang, and S. Kazama, “Evaluation of soil moisture dryness using land surface model in the case of forest fires in Tohoku 2017,” Proc. of the 38th IAHR World Congress, pp. 3822-3828, doi: 10.3850/38WC092019-1808, 2019.
42. [42] A. M. Sparks et al., “Spectral indices accurately quantify changes in seedling physiology following fire: Towards mechanistic assessments of post-fire carbon cycling,” Remote Sens., Vol.8, No.7, Article No.572, doi: 10.3390/rs8070572, 2016.
43. [43] S. Escuin, R. Navarro, and P. Fernández, “Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images,” Int. J. Remote Sens., Vol.29, No.4, pp. 1053-1073, doi: 10.1080/01431160701281072, 2008.
44. [44] J. D. White, K. C. Ryan, C. C. Key, and S. W. Running, “Remote sensing of forest fire severity and vegetation recovery,” Int. J. Wildl. Fire, Vol.6, No.3, pp. 125-136, doi: 10.1071/WF9960125, 1996.