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
|