Browse > Article
http://dx.doi.org/10.7780/kjrs.2022.38.5.2.2

Forest Fire Area Extraction Method Using VIIRS  

Chae, Hanseong (Department of Geography, Kyung Hee University)
Ahn, Jaeseong (Department of Land and Information Science, Kyungil University)
Choi, Jinmu (Department of Geography, Kyung Hee University)
Publication Information
Korean Journal of Remote Sensing / v.38, no.5_2, 2022 , pp. 669-683 More about this Journal
Abstract
The frequency and damage of forest fires have tended to increase over the past 20 years. In order to effectively respond to forest fires, information on forest fire damage should be well managed. However, information on the extent of forest fire damage is not well managed. This study attempted to present a method that extracting information on the area of forest fire in real time and quasi-real-time using visible infrared imaging radiometer suite (VIIRS) images. VIIRS data observing the Korean Peninsula were obtained and visualized at the time of the East Coast forest fire in March 2022. VIIRS images were classified without supervision using iterative self-organizing data analysis (ISODATA) algorithm. The results were reclassified using the relationship between the burned area and the location of the flame to extract the extent of forest fire. The final results were compared with verification and comparison data. As a result of the comparison, in the case of large forest fires, it was found that classifying and extracting VIIRS images was more accurate than estimating them through forest fire occurrence data. This method can be used to create spatial data for forest fire management. Furthermore, if this research method is automated, it is expected that daily forest fire damage monitoring based on VIIRS will be possible.
Keywords
Forest fire; VIIRS; ISODATA; Unsupervised classification; Area extraction;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 Youn, H.J. and J.C. Jeong, 2019. Detection of Forest Fire and NBR Mis-classified Pixel Using Multitemporal Sentinel-2A Images, Korean Journal of Remote Sensing, 35(6-2): 1107-1115 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2019.35.6.2.7   DOI
2 Ongeri, D. and B.K. Kenduiywo, 2020. Burnt Area Detection using Medium Resolution Sentinel 2 and Landsat 8 Satellites, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43(5): 131-137. https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-131-2020   DOI
3 Pinto, M.M., R.M. Trigo, I.F. Trigo, and C.C. DaCamara, 2021. A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS, Remote Sensing, 13(9): 1608. https://doi.org/10.3390/rs13091608   DOI
4 Schwert, B., C. Albury, J. Clark, A. Schaaf, S. Urbanski, and B. Nordgren, 2016. Implementation of a near real-time burned area detection algorithm calibrated for VIIRS imagery, U.S. Department of Agriculture, Forest Service, Remote Sensing Applications Center, Salt Lake City, UT, USA.
5 NASA, 2021. NASA Visible Infrared Imaging Radiometer Suite Level-1B Product User Guide, https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/NASA_VIIRS_L1B_UG_August_2021.pdf, Accessed on Aug. 16, 2022.
6 Pereira, J.M.C., A.C.L. Sa, A. Sousa, J.M.N. Silva, T.N. Santos, and J. Carreiras, 1999. Spectral characterisation and discrimination of burnt areas, In: Chuvieco, E. (eds), Remote Sensing of Large Wildfires, Springer, Berlin, Heidelberg, Germany, pp. 123-138. https://doi.org/10.1007/978-3-642-60164-4_7   DOI
7 Son, R.H., H.J. Kim, S.Y. Wang, J.H. Jeong, S.H. Woo, J.Y. Jeong, B.D. Lee, S.H. Kim, M. LaPlante, C.G. Kwon, and J.H. Yoon, 2021. Changes in fire weather climatology under 1.5℃ and 2.0℃ warming, Environmental Research Letters, 16(3): 034058. https://doi.org/10.1088/1748-9326/abe675   DOI
8 Song, Y.S., H.G. Sohn, and S.W. Lee, 2006. Analysis of Forest Fire Damage Using LiDAR Data and SPOT-4 Satellite Images, KSCE Journal of Civil and Environmental Engineering Research, 26(3D): 527-534 (in Korean with English abstract).   DOI
9 Mallinis, G., I. Mitsopoulos, and I. Chrysafi, 2017. Evaluating and comparing Sentinel 2A and Landsat8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece, GIScience & Remote Sensing, 55(1): 1-18. https://doi.org/10.1080/15481603.2017.1354803   DOI
10 Oliva, P. and W. Schroeder, 2015. Assessment of VIIRS 375m active fire detection product for direct burned area mapping, Remote Sensing of Environment, 160: 144-155. https://doi.org/10.1016/j.rse.2015.01.010   DOI
11 Pinto, M.M., R. Libonati, R.M. Trigo, I.F. Trigo, and C.C. DaCamara, 2020. A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images, ISPRS Journal of Photogrammetry and Remote Sensing, 160: 260-274. https://doi.org/10.1016/j.isprsjprs.2019.12.014   DOI
12 Ramo, R. and E. Chuvieco, 2017. Developing a Random Forest Algorithm for MODIS Global Burned Area Classification, Remote Sensing, 9(11): 1193. https://doi.org/10.3390/rs9111193   DOI
13 Lestari, A.I., D.L. Luhurkinanti, H.I. Fitriasari, R. Harwahyu, and R.F. Sari, 2020. Machine Learning Approaches for Burned Area Identification Using Sentinel-2 in Central Kalimantan, Journal of Applied Engineering Science, 18(2): 207-215. https://doi.org/10.5937/jaes18-25495   DOI
14 Korea Forest Service, 2022a. Statistical Yearbook of Forest Fire of 2021, Korea Forest Service, Daejeon, South Korea.
15 Eva, H. and E.F. Labmin, 1998. Burnt area mapping in Central Atrica using ATSR data, International Journal of Remote Sensing, 19(18): 3473-3497. https://doi.org/10.1080/014311698213768   DOI
16 Gladkova, I., A. Ignatov, F. Shahriar, Y. Kihai, D. Hillger, and B. Petrenko, 2016. Improved VIIRS and MODIS SST Imagery, Remote Sensing, 8(1):79. https://doi.org/10.3390/rs8010079   DOI
17 Jones, H.P., P.C. Jones, E.B. Barbier, R.C. Blackburn, J.M. Rey Beayas, K.D. Holl, M. McCrackin, P. Meli, D. Montoya, and D.M. Mateos, 2018. Restoration and repair of Earth's damaged ecosystems, Proceedings of the Royal Society B: Biological Sciences, 285(1873): 20172577. http://doi.org/10.1098/rspb.2017.2577   DOI
18 Chen, W., K. Moriya, T. Sakai, L. Koyama, and C.X. Cao, 2016. Mapping a burned forest area from Landsat TM data by multiple methods, Geomatics, Natural Hazards and Risk, 7(1): 384-402. https://doi.org/10.1080/19475705.2014.925982   DOI
19 Christnacher, F., S. Schertzer, N. Metzger, E. Bacher, M. Laurenzis, and R. Habermacher, 2015. Influence of gating and of the gate shape on the penetration capacity of range-gated active imaging in scattering environments, Optics Express, 23(26): 32897-32908. https://doi.org/10.1364/OE.23.032897   DOI
20 Choi, J.W., H.L. Park, N.H. Park, S.H. Han, and J.H. Song, 2017. Deforestation Analysis Using Unsupervised Change Detection Based on ITPCA, Korean Journal of Remote Sensing, 33(6): 1233-1242 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2017.33.6.3.7   DOI
21 Giglio, L., L. Boschetti, D.P. Roy, M.L. Humber, and C.O. Justice, 2018. The Collection 6 MODIS burned area mapping algorithm and product, Remote Sensing of Environment, 217: 72-85. https://doi.org/10.1016/j.rse.2018.08.005   DOI
22 Giglio, L., T. Loboda, D.P. Roy, B. Quayle, and C.O. Justice, 2009. An active-fire based burned area mapping algorithm for the MODIS sensor, Remote Sensing of Environment, 113: 408-420. https://doi.org/10.1016/j.rse.2008.10.006   DOI
23 Hu, X., Y. Ban, and A. Nascetti, 2021. Sentinel-2 MSI data for active fire detection in major fire-pronebiomes: A multi-criteria approach, International Journal of Applied Earth Observation and Geoinformation, 101: 102347. https://doi.org/10.1016/j.jag.2021.102347   DOI
24 Hussain, M., D. Chen, A. Cheng, H. Wei, and D. Stanley, 2013. Change detection from remotely sensed images: From pixel-based to object-based approaches, ISPRS Journal of Photogrammetry and Remote Sensing, 80: 91-106. https://doi.org/10.1016/j.isprsjprs.2013.03.006   DOI
25 Kang, J.M., C. Zhang, J.K. Park, and M.K. Kim, 2010. Forest Fire Damage Analysis Using Satellite Images, Journal of the Korean Society of Survey, Geodesy, Photogrammetry, and Cartography, 28(1): 21-28 (in Korean with English abstract).
26 Kim, T.H. and J.M. Choi, 2020. The Method of Linking Fire Survey Data with Satellite Image-based Fire Data, Korean Journal of Remote Sensing, 36(5): 1125-1137 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.5.3.10   DOI
27 Korea Forest Service, 2022b. Forest Fire Damage Register, https://huyang.forest.go.kr/kfsweb/kfi/kfs/frfr/selectFrfrStatsNow.do?mn=NKFS_02_02_01_05, Accessed on Jun. 6, 2022.
28 Libonati, R., C.C. DaCamara, A.W. Setzer, F. Morelli, and A.E. Melchiori, 2015. An Algorithm for Burned Area Detection in the Brazilian Cerrado Using 4 μm MODIS Imagery, Remote Sensing, 7(11): 15782-15803. https://doi.org/10.3390/rs71115782   DOI
29 Liu, S., Y. Zheng, M. Dalponte, and X. Tong, 2020. A novel fire index-based burned area change detection approach using Landsat-8 OLI data, European Journal of Remote Sensing, 53(1): 104-112. https://doi.org/10.1080/22797254.2020.1738900   DOI
30 Ministry of Government Legislation, 2021. Forest Protection Act, https://elaw.klri.re.kr/kor_service/lawView.do?hseq=53816&lang=ENG, Accessed on Aug. 16, 2022.
31 NASA, 2016. Visible Infrared Imaging Radiometer Suite (VIIRS) 375m Active Fire Detection and Characterization Algorithm Theoretical Basis Document 1.0, https://viirsland.gsfc.nasa.gov/PDF/VIIRS_activefire_375m_ATBD.pdf, Accessed on Aug. 16, 2022.
32 Lee, S.J. and Y.W. Lee, 2020. Detection of WildfireDamaged Areas Using Kompsat-3 Image: A Case of the 2019 Unbong Mountain Fire in Busan, South Korea, Korean Journal of Remote Sensing, 36(1): 29-39 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.1.3   DOI
33 Briones-Herrera, C.I., D.J. Vega-Nieva, N.A. MonjarasVega, J. Briseno-Reyes, P.M. Lopes-Serrano, J.J. Corral-Rivas, E. Alvarado-Celestino, S. ArellanoPerez, J.G. Alvarez-Gonzalez, A.D. Ruiz-Gonzalez, W.M. Jolly, and S.A. Parks, 2020. Near RealTime Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico, Remote Sensing, 12(12): 2061. https://doi.org/10.3390/rs12122061   DOI
34 Shimabukuro, Y.E., A.C. Dutra, E. Arai, V. Duarte, H.L.G. Cassol, G. Pereira, and F.S. Cardozo, 2020. Mapping Burned Areas of Mato Grosso State Brazilian Amazon Using Multisensor Datasets, Remote Sensing, 12(22): 3827. https://doi.org/10.3390/rs12223827   DOI
35 Urbanski, S., B. Nordgren, C. Albury, B. Schwert, D. Peterson, B. Quayle, and W.M. Hao, 2018. A VIIRS direct broadcast algorithm for rapid response mapping of wildfire burned area in the western United States, Remote Sensing of Environment, 219: 271-283. https://doi.org/10.1016/j.rse.2018.10.007   DOI
36 Raspaud, M., D. Hoese, A. Dybbroe, P. Lahtinen, A. Devasthale, M. Itkin, U. Hamann, L.O. Rasmussen, E.S. Nielsen, T. Leppelt, A. Maul, C. Kliche, and H. Thorstiensson, 2018. Pytroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data, Bulletin of the American Meteorological Society, 99(7): 1329-1336. https://doi.org/10.1175/BAMS-D-17-0277.1   DOI