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Landsat-8 위성영상 분석을 통한 산불피해 심각도 판정 및 영향 인자 도출 - 강릉, 동해 산불을 사례로 -

Determination of Fire Severity and Deduction of Influence Factors Through Landsat-8 Satellite Image Analysis - A Case Study of Gangneung and Donghae Forest Fires -

  • 이수동 (경상국립대학교 조경학과) ;
  • 박경식 (경상국립대학교 대학원 조경학과) ;
  • 오충현 (경상국립대학교 대학원 도시시스템 공학과) ;
  • 조봉교 (경상국립대학교 대학원 도시시스템 공학과) ;
  • 유병혁 (국립공원공단 사회가치혁신실)
  • Soo-Dong Lee (Dept. of Landscape Architecture, Gyeongsang National University) ;
  • Gyoung-Sik Park (Dept. of Landscape Architecture, Graduate School, Gyeongsang National University) ;
  • Chung-Hyeon Oh (Dept. of Urban system Engineering, Graduate School, Gyeongsang National University) ;
  • Bong-Gyo Cho (Dept. of Urban system Engineering, Graduate School, Gyeongsang National University) ;
  • Byeong-Hyeok Yu (Social Value & Innovation Office, Korea National Park Service)
  • 투고 : 2023.10.23
  • 심사 : 2024.04.17
  • 발행 : 2024.06.30

초록

지형적인 이질성이 심한 강원도, 경상북도에 집중되고 있는 대형 산불을 관리하기 위해서는 위성 영상을 활용하여 효율적이고 신속한 피해 평가를 통한 의사 결정 과정이 필수적이다. 이에 본 연구는 2022년 3월 5일에 강원도 강릉 및 동해에서 발화하여 3월 8일 19시경 진화된 대형 산불을 대상으로, dNBR을 활용한 산불 심각도 산정과 등급에 영향을 미치는 환경요인을 도출하고자 하였다. 환경요인으로는 식생 또는 연료 유형을 대표하는 정규식생지수, 수종을 구분한 임상도, 수분함양을 나타내는 정규수분지수, 지형과 관련해서는 DEM 등을 수치화한 후 산불 심각도와의 상관관계를 분석하였다. 산불 심각도는 산불 피해 없음(Unbured)이 52.4%로 가장 넓었고, 심각도 낮음 42.9%, 심각도 보통-낮음 4.3%, 심각도 보통-높음 0.4% 순이었다. 환경요인의 경우 dNDVI, dNDWI와는 음의 상관관계를, 경사도와는 양의 상관관계를 나타내었다. 식생과 관련해서는 산불 심각도에 영향을 미치는 것으로 분석된 dNDVI, dNDWI, 경사도 모두에서 침엽수, 활엽수, 기타의 집단간 차이가 p-value < 2.2e-16로 유의미한 것으로 분석되었다. 특히, 침엽수와 활엽수의 차이가 명확하였는데, 강원도 지역에서 우점종인 소나무를 비롯하여 잣나무, 리기다소나무, 곰솔 등의 산불 심각도가 높아 침엽수가 활엽수에 비해 피해를 받는 것이 확인되었다.

In order to manage large-scale forest fires concentrated in Gangwon-do and Gyeongsangbuk-do with severe topographical heterogeneity, a decision-making process through efficient and rapid damage assessment using satellite images is essential. Accordingly, this study targets a large-scale forest fire that ignited in Gangneung and the Donghae, Gangwon-do on March 5, 2022, and was extinguished around 19:00 on March 8, to estimate the fire severity using dNBR and derive environmental factors that affect the grade. As environmental factors, we quantified the regular vegetation index representing vegetation or fuel type, the forest index that classifies tree species, the regular moisture index representing moisture content, and DEM in relation to topography, and then analyzed the correlation with the fire severity. In terms of fire severity, the widest range was 'Unbured' at 52.4%, followed by low severity at 42.9%, medium-low severity at 4.3%, and medium-high severity at 0.4%. Environmental factors showed a negative correlation with dNDVI and dNDWI, and a positive correlation with slope. Regarding vegetation, the differences between coniferous, broad-leaved, and other groups in dNDVI, dNIWI, and slope, which were analyzed to affect the fire severity, were analyzed to be significant with p-value < 2.2e-16. In particular, the difference between coniferous and broad-leaved forests was clear, and it was confirmed that coniferous forest suffered more damage than broad-leaved forest due to the higher fire severity in the Gangwon-do region, including Pinus densiflora, which are dominant species, as well as P. koraiensis, P. rigida and P. thunbergii.

키워드

참고문헌

  1. Abram, N.J., J.B. Henley, A.S. Gupta, T.J.R. Lippmann, H. Clarke, A.J. Dowdy, J.J. Sharples, R.H. Nolan, T. Zhang, M.J. Wooster, J.B. Wurtzel, K.J. Meissner, A.J. Pitman, A.M. Ukkola, B.P. Murphy, N.J. Tapper and M.M. Boer(2021) Connections of climate change and variability to large and extreme forest fires in southeast Australia. Commun Earth Environ 2: 8. https://doi.org/10.1038/s43247-020-00065-8
  2. Adagbas, G.E., S.A. Adelabu and T.W. Okello(2018) Spatiotemporal assessment of fire severity in a protected and mountainous ecosystem. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 6572-6575). IEEE. 10.1109/IGARSS.2018.8518268
  3. Afina, F.S., L. Syaufina and I. Sukaesih(2021) Sitanggang Forest and peatland fire severity assessment at Siak Regency, Riau Province using Sentinel-2 Imagery. Journal of Natural Resources and Environmental Management 11(4): 621-630. http://dx.doi.org/10.29244/jpsl.11.4.621-630
  4. Bistinas, I., S.P. Harrison, I.C. Prentice and J.M.C. Pereira(2014) Causal relationships versus emergent patterns in the global controls of fire frequency. Biogeosciences 11(3): 5087-5101. DOI:10.5194/bg-11-5087-2014
  5. Boby, L.A., E.A.G. Schuur, M.C. Mack, D. Verbyla and J.F. Johnstone(2010) Quantifying fire severity, carbon, and nitrogen emissions in Alaska's boreal forest. Ecological Applications 20(6): 1633-1647. https://doi.org/10.1890/08-2295.1
  6. Bright, B.C., A.T. Hudak, R.E. Kennedy, J.D. Braaten and A. Henareh Khalyani(2019) Examining post-fire vegetation recovery with Landsat time series analysis in three western North American forest types. Fire Ecology 15(1): 1-14. DOI:10.1186/s42408-018-0021-9
  7. Chae, H.M., G.J. Um and S.Y. Lee(2011) The vulnerability assessment of forest fire in Gangwon province using CCGIS. Journal of the Korean Society of Hazard Mitigation 11(4): 123-130. DOI:10.9798/kosham.2011.11.4.123
  8. Chung, M.K. and Y. Kim(2020) Analysis on topographic normalization methods for 2019 Gangneung-east sea wildfire area using planetscope imagery. Korean Journal of Remote Sensing 36(2): 179-197.
  9. Chung, M.K., Y.K. Han and Y. Kim(2020) A framework for unsupervised wildfire damage assessment using VHR satellite images with planetscope data. Remote Sensing 12(22): 3835. DOI:10.3390/rs12223835. (In Korean with English abstract)
  10. Chuvieco, E., A. De Santis, D. Riano and K. Halligan(2007) Simulation approaches for burn severity estimation using remotely sensed images. Fire Ecology Special Issue 3(1): 129-150. https://doi.org/10.4996/fireecology.0301129
  11. Chuvieco, E., I. Aguado, J. Salas, M. Garcia, M. Yebra and P. Oliva(2020) Satellite remote sensing contributions to wildland fire science and management. Current Forestry Reports 6: 81-96. DOI:10.1007/s40725-020-00116-5.
  12. 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. https://doi.org/10.1071/WF04010
  13. Collins, L., G. McCarthy, A. Mellor, G. Newell and L. Smith(2020) Training data requirements for fire severity mapping using Landsat imagery and random forest. Remote Sens Environ 245: 111839 https://doi.org/10.1016/j.rse.2020.111839
  14. Collins, L., P. Griffioen, G. Newell and A. Mellor(2018) The utility of random forests for wildfire severity mapping. Remote Sens. Environ 216: 374-384. https://doi.org/10.1016/j.rse.2018.07.005
  15. Coop, J., T. DeLory, W. Downing, S. Haire, M. Krawchuk, C. Miller, M. Parisien and R. Walker(2019) Contributions of fire refugia to resilient ponderosa pine and dry mixed-conifer forest landscapes. Ecosphere 10(7): e02809.
  16. Curtis, P.G., C.M. Slay, N.L. Harris, A. Tyukavina and M.C. Hansen(2018) Classifying drivers of global forest loss. Science 361: 1108-1111. 10.1126/science.aau3445.
  17. Dixon, D.J., J.N. Callow, J.M.A. Duncan, S.A. Setterfield and N. Pauli(2022) Regional-scale fire severity mapping of Eucalyptus forests with the Landsat archive. Remote Sensing of Environment 270: 112863 https://doi.org/10.1016/j.rse.2021.112863
  18. Doerr, S.H. and C. Santin(2016) Global trends in wildfire and its impacts: Perceptions versus realities in a changing world. Philosophical Transactions of the Royal Society B: Biological Sciences 371(1696): 20150345. http://dx.doi.org/10.1098/rstb.2015.0345
  19. Doerr, S.H., R.A. Shakesby, W.H. Blake, C.J. Chafer, G.S. Humphreys and P.J. Wallbrink(2006) Effects of differing wildfire severities on soil wettability and implications for hydrological response. Journal of Hydrology 319(1-4): 295-311. doi:10.1016/j.jhydrol.2005.06.038
  20. Driscoll, D.A., D.B. Lindenmayer, A.F. Bennett, M. Bode, R.A. Bradstock, G.J. Cary, M.F. Clarke, N. Dexter, R. Frensham, G. Friend, M. Gill, S. James, G. Kay, D.A. Keith, C. MacGregor, J. Russell-Smith, D. Salt, J.E.M. Watson, R.J. Williams and A. York(2010) Fire management for biodiversity conservation: Key research questions and our capacity to answer them. Biological Conservation 143: 1928-1939. https://doi.org/10.1016/j.biocon.2010.05.026
  21. Eidenshink, J., B. Schwind, B., Brewer, Z.L. Zhu, B. Quayle and S. Howard(2007) A project for monitoring trends in burn severity. Fire Ecology 3: 3-21 https://doi.org/10.4996/fireecology.0301003
  22. Forkel, M., N. Andela, S.P. Harrison, G. Lasslop, M. van Marle, E. Chuvieco, W. Dorigo, M. Forrest, S. Hantson, A. Heil, F. Li, J. Melton, S. Sitch, C. Yue and A. Arneth(2019) A: Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models. Biogeosciences 16: 57-76. https://doi.org/10.5194/bg-16-57-2019
  23. Fujiki, T. and Y. Yasuda(2004) Vegetation history during the Holocene from Lake Hyangho, northeastern Korea. Quaternary International 123: 63-69. https://doi.org/10.1016/j.quaint.2004.02.009
  24. Gao, B.C.(1996) NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58: 257-266. https://doi.org/10.1016/S0034-4257(96)00067-3
  25. Ghermandi, L., A. Lanorte, F. Oddi and R. Lasaponara(2019) Assessing fire severity in semiarid environments with the dNBR and RDNBR Indices. Global Journal of Science Frontier Research 19(1): 27-44.
  26. Gibson, R., T. Danaher, W. Hehir and L. Collins(2020) A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest. Remote Sens. Environ 240:111702.
  27. Giglio, L. and D.P. Roy(2020) On the outstanding need for a long-term, multi-decadal, validated and quality assessed record of global burned area: Caution in the use of Advanced Very High Resolution Radiometer data. Science of Remote Sensing 2(11): 100007.
  28. Glenn, E.P., A.R. Huete, P.L. Nagler and S.G. Nelson(2008) Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: Whatvegetation indices can and cannot tell us about the landscape. Sensors 8: 2136-2160. https://doi.org/10.3390/s8042136
  29. Hall, M.L., P. Samaniego, J.L. Le Pennec and J.B. Johnson(2008) Ecuadorian Andes volcanism: A review of Late Pliocene to present activity. Journal of Volcanology and Geothermal Research 176(1): 1-6 https://doi.org/10.1016/j.jvolgeores.2008.06.012.
  30. Han, D., X. Di, G. Yang, L. Sun and Y. Weng(2021) Quantifying fire severity: A brief review and recommendations for improvement. Ecosystem Health and Sustainability 7: 1. DOI: 10.1080/20964129.2021.1973346
  31. Han, S.S.(2000) Forest fire and forest ecosystems restoration. Journal of Forest and Environmental Science 16: 175-193. (In Korean with English abstract)
  32. Hawbaker, T.J., M.K. Vanderhoof, Y.J. Beal, J.D. Takacs, G.L. Schmidt, J.T. Falgout, B. Williams, N. M. Fairaux, M.K. Caldwell, J.J. Picotte, S.M. Howard, S. Stitt and J.L. Dwyer(2017) Mapping burned areas using dense time-series of Landsat data. Remote Sensing of Environment 198: 504-522. https://doi.org/10.1016/j.rse.2017.06.027
  33. Heward, H., A.M.S., Smith, D.P., Roy, W.T., Tinkham, C.M., Hoffman, P., Morgan and K.O. Lannom(2013) Is burn severity related to fire intensity? Observations from landscape scale remote sensing. Int. J. Wildland Fire 22: 910-918. https://doi.org/10.1071/WF12087
  34. Huang, D., Y. Tang and R. Qin(2022) An evaluation of PlanetScope images for 3D reconstruction and change detection-experimental validations with case studies. GIScience and Remote Sensing 59(1): 744-761. DOI: 10.1080/15481603.2022.2060595
  35. Jakubauskas, M.E., K.P. Lulla and P.W. Mausel(1990) Assessment of vegetation change in a fire-altered forest landscape. Photogrammetric Engineering and Remote Sensing 56(3): 371-377.
  36. Jee, M.J., J.A. Tyson, S. Hilbert, M.D. Schneider, S. Schmidt and D. Wittman(2015) Cosmic shear results from the deep lens survey - II: full cosmological parameter constraints from tomography. The Astrophysical Journal 824(2): 77. DOI: 10.3847/0004-637X/824/2/77
  37. Jones, M.W., J.T. Abatzoglou, S. Veraverbeke, N. Andela, G. Lasslop and M. Forkel(2022) Global and regional trends and drivers of fire under climate change. Reviews of Geophysics 60(3): e2020RG000726.
  38. Keeley, J.E.(2009) Fire intensity, fire severity and burn severity: A brief review and suggested usage. International Journal of Wildland Fire 18: 116-126. DOI: 10.1071/WF07049
  39. Kelley, D.I., I. Bistinas, R. Whitley, C. Burton, T.R. Marthews and N. Dong(2019) How contemporary bioclimatic and human controls change global fire regimes. Nature Climate Change 9(9): 690-696. https://doi.org/10.1038/s41558-019-0540-7.
  40. Key, C.H. and N.C. Benson(2006) Landscape Assessment (LA). FIREMON: Fire effects monitoring and inventory system. Gen.Tech. Rep. RMRS-GTR-164-CD. Fort Collins, CO:US Department of Agriculture, Forest Service, Rocky Mountain Research Station, LA-1-55.
  41. Kim, J.S. and J.S. Oh(2021) Recoverability analysis of forest fire area based on satellite imagery: Applications to DMZ in the Western Imjin stuary. Journal of the Korean Geomorphological Association 28(1): 83-99. (In Korean with English abstract) https://doi.org/10.16968/JKGA.28.1.83
  42. Kolden, C.A. and J. Rogan(2013) Mapping wildfire burn severity in the arctic tundra from downsampled MODIS data. Arctic, Antarctic, and Alpine Research 45(1): 64-76. https://doi.org/10.1657/1938-4246-45.1.64
  43. Korea Forest Service(2016) 2015 Forest fire statistics yearbook, Daejeon, Korea. (in Korean).
  44. Korea Forest Service(2019) 2018 Forest fire statistics yearbook, Daejeon, Korea. (in Korean).
  45. Korea Forest Service(2022) 2021 Forest fire statistics yearbook, Daejeon, Korea. (in Korean).
  46. Laurent, P., F. Mouillot, M.V. Moreno, C. Yue and P. Ciais(2019) Varying relationships between fire radiative power and fire size at a global scale. Biogeosciences 16: 275-288. DOI:10.5194/bg-16-275-2019.
  47. Lee, B.D, J.E. Song, M.B. Lee and J.S. Chung(2008b) The relationship between characteristics of forest fires and spatial patterns of forest types by the ecoregions of South Korea. Journal of Korean Society of Forest Science 97(1): 1-9. (In Korean with English abstract)
  48. Lee, B.D., M.S. Won, K.M. Jang and M.B. Lee(2008a) Analysis of the relationship between landform and forest fire severity. Journal of the Korean Association of Geographic Information Studies 38(11): 58-67. (In Korean with English abstract)
  49. Lee, H.P., S.Y. Lee and Y.J. Park(2009) Combustion characteristics of the 5 herb species in Youngdong areas. Journal of Korean Society of Forest Science 98(3): 290-296. (In Korean with English abstract)
  50. Lee, M.W., S. Y. Lee and J.H. Lee(2012) Study of the characteristics of forest fire based on statistics of forest fire in Korea. Journal of the Korean Society of Hazard Mitigation 12(5): 185-192 (In Korean with English abstract) https://doi.org/10.9798/KOSHAM.2012.12.5.185
  51. Lee, S.Y. and J.E. Kim(2011) A study on meteorological elements effecting on large-scale forest fire during spring time in Gangwon Young-dong Region. Koeran society of hazard mitigation 11(1) : 37-43. (In Korean with English abstract) https://doi.org/10.9798/KOSHAM.2011.11.1.037
  52. Lentile, L.B., Z.A. Holden, A.M.S. 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: 319-345. https://doi.org/10.1071/WF05097
  53. Lisa, M., S.A. Holsinger, L.B. Parks, R.A. Saperstein, E. Loehman, J. Whitman, Barnes and M.A. Parisien(2021) Improved fire severity mapping in the North American boreal forest using a hybrid composite method. Remote Sensing in Ecology and Conservation 8(2): 222-235. DOI: 10.1002/rse2.238
  54. Liu, S., L. Zhang and Y. Long(2019) Urban vitality area identification and pattern analysis from the perspective of time and space fusion. Sustainability 11(15): 4032.
  55. Malamud, B.D., G. Morein and D.L. Turcotte(1998) Forest fires: An example of self-organized criticality. Science 281: 1840-1842. https://doi.org/10.1126/science.281.5384.1840
  56. Molla, I., E. Velizarova and M. Zaharinova(2017) Fire severity assessment using ndvi derived from Landsat TM/ETM images and terrain data. Ecological Engineering and Environment Protection 8(2017): 29-37. DOI: 10.32006/eeep.2017.1.2937
  57. Morresi, D., R. Marzano, E. Lingua, R. Motta and M. Garbarino(2022) Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery. Remote Sensing of Environment 269: 112800. https://doi.org/10.1016/j.rse.2021.112800
  58. Mouillot, F. and C.B. Field(2005) Fire history and the global carbon budget: A 1° × 1° fire history reconstruction for the 20th century. Glob. Chang. Biol 11: 398-420. doi:10.1111/j.1365-2486.2005.00920.x.
  59. Newcomer, M., D. Delgado, C. Gantenbein, T. Wang, B. Schiffman, S. Prichard, C. Schmidt and J.W. Skiles(2009) Burn severity assessment in the Okanogan-Wenatchee forest using NASA satellite missions. ASPRS Annual Conference.
  60. Pausas, J.G. and E. Ribeiro(2013) Fire and productivity. Global Ecology and Biogeography 22: 728-736. https://doi.org/10.1111/geb.12043.
  61. Ramo, R., E. Roteta, I. Bistinas, D. van Wees, A. Bastarrika, E. Chuvieco and G.R. Van der Werf(2021) African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data. Proceedings of the National Academy of Sciences 118(9): 1-17. https://doi.org/10.1073/pnas.2011160118
  62. Ratz, A.(1995) Long-term spatial patterns created by fire: A model oriented towards boreal forests. International Journal of Wildland Fire 5: 25-34. https://doi.org/10.1071/WF9950025
  63. Raut, S., K.R. Rijal, S. Khatiwada, S. Karna, R. Khanal, J. Adhikari and B. Adhikari(2020) Trend and characteristics of Acinetobacter baumannii infections in patients attending Universal College of Medical Sciences, Bhairahawa, Western Nepal: A Longitudinal Study of 2018. Infect Drug Resist 8(13): 1631-1641. doi: 10.2147/IDR.S257851
  64. Roteta, E., A. Bastarrika, M. Franquesa and E. Chuvieco(2021) Landsat and Sentinel-2 based burned area mapping tools in Google Earth Engine. Remote Sensing 13(4): 816. https://doi.org/10.3390/rs13040816
  65. 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: 10.1016/j.rse.2018.12.011.
  66. Roy, D. P., H. Huang, L. Boschetti, L. Giglio, L. Yan, H.H. Zhang and Z. Li(2019) Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach. Remote Sensing of Environment 231: 111254. https://doi.org/10.1016/j.rse.2019.111254
  67. Roy, D.P., L. Boschetti and S.N. Trigg(2006) Remote sensing of fire severity: Assessing the performance of the Normalized Burn Ratio. IEEE Geoscience and Remote Sensing Letters 3(1): 112-116. https://doi.org/10.1109/LGRS.2005.858485
  68. Schimmel, J. and A. Granstrom(1996) Fire severity and vegetation response in the boreal swedish forest. Ecology 77(5): 1436-1450. https://doi.org/10.2307/2265541
  69. Schroeder, K., S.A. Josey, M. Herrmann, L. Grignon, G.P. Gasparini and H.L. Bryden(2010) Abrupt warming and salting of the Western Mediterranean deep water after 2005: Atmospheric forcings and lateral advection. Journal of Geophysical Research 115(C8). https://doi.org/10.1029/2009JC005749
  70. Senande-Rivera, M., D. Insua-Costa and G. Miguez-Macho(2022) Spatial and temporal expansion of global wildland fire activity in response to climate change. Nature Communications 13: 1208. doi.org/10.1038/s41467-022-28835-2
  71. Shin, M.H., J.H. Lim and W.S. Kong(2014) Relationship between environment factors and distribution ofPinus densiflora after fire in Goseong, Gangwon Province, Korea. Journal of the Korean Society of Environmental Restoration Technology 17(2): 49-60. DOI: http://dx.doi.org/10.13087/kosert.2014.17.2.49
  72. Soverel, N.O., D.D.B. Perrakis and N.C. Coops(2010) Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada. Remote Sensing of Environment 114: 1896-1909. 10.1016/j.rse.2010.03.013
  73. Teckentrup, L., S.P. Harrison, S. Hantson, A. Heil, J.R. Melton and M. Forrest(2019) Response of simulated burned area to historical changes in environmental and anthropogenic factors: A comparison of seven fire models. Biogeosciences 16(19): 3883-3910. https://doi.org/10.5194/bg-16-3883-2019
  74. Tran, B.N., M.A. Tanase, L.T. Bennett and C. Aponte(2018) Evaluation of spectral indices for assessing fire severity in Australian Temperate Forests. Remote Sens 10: 1680. doi: 10.3390/rs10111680
  75. Tyukavina, A., P. Potapov, M.C. Hansen, A.H. Pickens, S.V. Stehman, S. Turubanova, D. Parker, V. Zalles, A. Lima, I. Kommareddy, X.P. Song, L. Wang and N. Harris(2022) Global trends of forest loss due to fire from 2001 to 2019. Front. Remote Sens 3: 825190. doi: 10.3389/frsen.2022.825190
  76. van Gerrevink, M.J. and S. Veraverbeke(2021) Evaluating the hyperspectral sensitivity of the Differenced Normalized Burn Ratio for assessing fire severity. Remote Sens 13: 4611. https://doi.org/10.3390/rs13224611
  77. Verhegghen, A., H. Eva, B. Desclee and F. Achard(2016) Review and combination of recent remote sensing based products for forest cover change assessments in Cameroon. International Forestry Review 18(2): 2016-2017. 10.1505/146554816819683807
  78. Wang, W., W. Wu, F. Guo and G. Wang(2022b) Fire regime and management in Canada's protected areas. International Journal of Geoheritage and Parks. 10: 240-251. https://doi.org/10.1016/j.ijgeop.2022.04.003
  79. Wang, W., X. Wang, W. Wu, F. Guo, J. Park and G. Wang(2022a) Burn severity in Canada's mountain national parks: Patterns, drivers, and predictions. Geophysical Research Letters 49(12): 1-11. https://doi.org/10.1029/2022GL097945
  80. White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops and G.W. Hobart(2017) A nationwide annual characterization of 25- years of forest disturbance and recovery for canada using landsat time series. Remote Sensing of Environment 194: 303-321. doi:10.1016/j.rse.2017.03.035
  81. White, J.D., K.C. Ryan, C.C. Key and S.W. Running(1996) Remote sensing of forest fire severity and vegetation recovery. Int J Wildland Fire 6(3): 125-136. DOI: 10.1071/WF9960125
  82. White, L.A. and R.K. Gibson(2022) Comparing fire extent and severity mapping between sentinel 2 and landsat 8 satellite sensors. Remote Sensing 14(7): 1661.. https://doi.org/10.3390/rs14071661
  83. Wright, H.A. and A.W. Bailey(1982) Fire ecology: United state and Southern Canada. John Wiley and Sons, New York. 501p.
  84. Ying, L., J. Han, Y. Du and Z. Shen(2018) Forest fire characteristics in China: Spatial patterns and determinants with thresholds. Forest Ecology and Management 424: 345-354. https://doi.org/10.1016/j.foreco.2018.05.020
  85. Yoo, J.Y., J.W. Han, D.W. Kim and T.W, Kim(2021) Evaluating impact factors of forest fire occurrences in Gangwon province using PLS-SEM: A focus on drought and meteorological factors. KSCE Journal of Civil and Environmental Engineering Research 41(3): 209-217. (In Korean with English abstract)