Special Articles
Chen, J., Zheng, W., Wu, S., Liu, C., Yan, H., 2022, Fire monitoring algorithm and its application on the Geo-Kompsat-2A geostationary meteorological satellite, Remote Sensing, 14(11), 2655.
10.3390/rs14112655Chuvieco, E., Mouillot, F., Van der Werf, G.R., San Miguel, J., Tanase, M., Koutsias, N., García, M., Yebra, M., Padilla, M., Gitas, I., Heil, A., Hawbaker, T.J., Giglio, L., 2019, Historical background and current developments for mapping burned area from satellite Earth observation, Remote Sensing of Environment, 225, 45-64.
10.1016/j.rse.2019.02.013Di Biase, V., Laneve, G., 2018, Geostationary sensor based forest fire detection and monitoring: An improved version of the SFIDE algorithm, Remote Sensing, 10(5), 741.
10.3390/rs10050741Ghali, R., Akhloufi, M.A., 2023, Deep learning approaches for wildland fires remote sensing: Classification, detection, and segmentation, Remote Sensing, 15(7), 1821.
10.3390/rs15071821Hall, J.V., Zhang, R., Schroeder, W., Huang, C., Giglio, L., 2019, Validation of GOES-16 ABI and MSG SEVIRI active fire products, International Journal of Applied Earth Observation and Geoinformation, 83, 101928.
10.1016/j.jag.2019.101928Jang, E., Kang, Y., Im, J., Lee, D.W., Yoon, J., Kim, S.K., 2019, Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea, Remote Sensing, 11(3), 271.
10.3390/rs11030271Kang, Y., Jang, E., Im, J., Kwon, C., 2022, A deep learning model using geostationary satellite data for forest fire detection with reduced detection latency, GIScience & Remote Sensing, 59(1), 2019-2035.
10.1080/15481603.2022.2143872Kim, D., Gu, M., Oh, T.H., Kim, E.K., Yang, H.J., 2021, Introduction of the advanced meteorological imager of Geo-Kompsat-2a: In-orbit tests and performance validation, Remote Sensing, 13(7), 1303.
10.3390/rs13071303Kim, G., Kim, D.S., Park, K.W., Cho, J., Han, K.S., Lee, Y.W., 2014, Detecting wildfires with the Korean geostationary meteorological satellite, Remote Sensing Letters, 5(1), 19-26.
10.1080/2150704X.2013.862602Lee, D., Kim, S.I., Ahn, D.S., Kim, S.C., Seo, D., Choi, M., Lee, Y., Kim, J., 2025, Machine learning-based near-real-time monitoring of wildfire spread extent using GK2A and VIIRS, Korean Journal of Remote Sensing, 41(5), 869-881.
10.7780/kjrs.2025.41.5.13Liu, C., Chen, R., He, B., 2023, Integrating machine learning and a spatial contextual algorithm to detect wildfire from Himawari-8 data in Southwest China, Forests, 14(5), 919.
10.3390/f14050919Romano, F., Cimini, D., Di Paola, F., Gallucci, D., Larosa, S., Nilo, S.T., Ricciardelli, E., Iisager, B.D., Hutchison, K., 2024, The evolution of meteorological satellite cloud-detection methodologies for atmospheric parameter retrievals, Remote Sensing, 16(14), 2578.
10.3390/rs16142578Schroeder, W., Giglio, L., 2017, Visible infrared imaging radiometer suite (VIIRS) 375 m & 750 m active fire detection data sets based on NASA VIIRS land science investigator processing system (SIPS) reprocessed data - Version 1, National Aeronautics and Space Administration (NASA), Retrieved from https://lpdaac.usgs.gov/documents/132/VNP14_User_Guide_v1.3.pdf.
Schroeder, W., Giglio, L., Csiszar, I., Tsidulko, M., 2020, Algorithm theoretical basis document for NOAA NDE VIIRS I-band (375m) active fire, National Oceanic and Atmospheric Administration (NOAA), Retrieved from https://www.star.nesdis.noaa.gov/jpss/documents/ATBD/ATBD_Iband_ActiveFires_v1.0.pdf.
Wang, H., Zhang, G., Yang, Z., Xu, H., Liu, F., Xie, S., 2024, Satellite remote sensing false forest fire hotspot excavating based on time-series features, Remote Sensing, 16(13), 2488.
10.3390/rs16132488Xie, Z., Song, W., Ba, R., Li, X., Xia, L., 2018, A spatiotemporal contextual model for forest fire detection using Himawari-8 satellite data, Remote Sensing, 10(12), 1992.
10.3390/rs10121992Xu, G., Zhong, X., 2017, Real-time wildfire detection and tracking in Australia using geostationary satellite: Himawari-8, Remote Sensing Letters, 8(11), 1052-1061.
10.1080/2150704X.2017.1350303Zhang, G., Li, B., Luo, J., He, L., 2020, A self-adaptive wildfire detection algorithm with two-dimensional otsu optimization, Mathematical Problems in Engineering, 2020(1), 3735262.
10.1155/2020/3735262Zhang, J.H., Yao, F.M., Liu, C., Yang, L.M., Boken, V.K., 2011, Detection, emission estimation and risk prediction of forest fires in China using satellite sensors and simulation models in the past three decades—An overview, International Journal of Environmental Research and Public Health, 8(8), 3156-3178.
10.3390/ijerph808315621909297PMC3166733Zheng, Z., Hu, H., Huang, W., Zhou, F., Ma, Y., Liu, Q., Jiang, L., Wang, S., 2024, Wildfire detection based on the spatiotemporal and spectral features of Himawari-8 data, IEEE Transactions on Geoscience and Remote Sensing, 62, 5408213.
10.1109/TGRS.2024.3434434Zhou, F., Wen, G., Ma, Y., Ma, Y., Pan, H., Geng, H., Cao, J., Fu, Y., Zhou, S., Wang, K., 2023, A two-branch cloud detection algorithm based on the fusion of a feature enhancement module and Gaussian mixture model, Mathematical Biosciences and Engineering: MBE, 20(12), 21588-21610.
10.3934/mbe.2023955- Publisher :Korean Society of Engineering Geology
- Publisher(Ko) :대한지질공학회
- Journal Title :The Journal of Engineering Geology
- Journal Title(Ko) :지질공학
- Volume : 35
- No :4
- Pages :629-641
- Received Date : 2025-12-03
- Revised Date : 2025-12-19
- Accepted Date : 2025-12-22
- DOI :https://doi.org/10.9720/kseg.2025.4.629
The Journal of Engineering Geology
