Browse > Article
http://dx.doi.org/10.7780/kjrs.2021.37.5.3.5

Wildfire-induced Change Detection Using Post-fire VHR Satellite Images and GIS Data  

Chung, Minkyung (Department of Civil and Environmental Engineering, Seoul National University)
Kim, Yongil (Department of Civil and Environmental Engineering, Seoul National University)
Publication Information
Korean Journal of Remote Sensing / v.37, no.5_3, 2021 , pp. 1389-1403 More about this Journal
Abstract
Disaster management using VHR (very high resolution) satellite images supports rapid damage assessment and also offers detailed information of the damages. However, the acquisition of pre-event VHR satellite images is usually limited due to the long revisit time of VHR satellites. The absence of the pre-event data can reduce the accuracy of damage assessment since it is difficult to distinguish the changed region from the unchanged region with only post-event data. To address this limitation, in this study, we conducted the wildfire-induced change detection on national wildfire cases using post-fire VHR satellite images and GIS (Geographic Information System) data. For GIS data, a national land cover map was selected to simulate the pre-fire NIR (near-infrared) images using the spatial information of the pre-fire land cover. Then, the simulated pre-fire NIR images were used to analyze bi-temporal NDVI (Normalized Difference Vegetation Index) correlation for unsupervised change detection. The whole process of change detection was performed on a superpixel basis considering the advantages of superpixels being able to reduce the complexity of the image processing while preserving the details of the VHR images. The proposed method was validated on the 2019 Gangwon wildfire cases and showed a high overall accuracy over 98% and a high F1-score over 0.97 for both study sites.
Keywords
Change Detection; Wildfire Damage Assessment; Very High Resolution (VHR); Geographic Information System (GIS);
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Cortes, C. and V. Vapnik, 1995. Support-Vector Networks, Machine Learning, 20(3): 273-297.   DOI
2 EGIS(Environmental Geographic Information Service). Production method for land cover map, https://egis.me.go.kr/intro/land.do, Accessed on Sep. 13, 2021 (in Korean).
3 Gao, F., L. Zhang, J. Wang, and J. Mei, 2015. Change detection in remote sensing images of damage areas with complex terrain using texture information and SVM, Proc. of International Conference on Circuits and Systems, Paris, FR, Aug. 9-10, pp. 225-229.
4 Haralick, R.M., K. Shanmugam, and I.H. Dinstein, 1973. Textural features for image classification, IEEE Transactions on Systems, Man, and Cybernetics, 6: 610-621.
5 Kim, Y., S.B. Lee, J. Kim, and Y. Park, 2017. Disaster Management Using High Resolution Optical Satellite Imagery and Case Analysis, Journal of the Korean Society of Hazard Mitigation, 17(3): 117-124 (in Korean with English Abstract).   DOI
6 Nami, M.H., A. Jaafari, M. Fallah, and S. Nabiuni, 2018. Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS, International Journal of Environmental Science and Technology, 15(2): 373-384.   DOI
7 Achanta, R., A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, 2012. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11): 2274-2282.   DOI
8 Bhandary, U. and B. Muller, 2009. Land use planning and wildfire risk mitigation: an analysis of wildfire-burned subdivisions using high-resolution remote sensing imagery and GIS data, Journal of Environmental Planning and Management, 52(7): 939-955.   DOI
9 Maillard, P., 2003. Comparing texture analysis methods through classification, Photogrammetric Engineering & Remote Sensing, 69(4): 357-367.   DOI
10 Pew, K.L. and C.P.S. Larsen, 2001. GIS analysis of spatial and temporal patterns of human-caused wildfires in the temperate rain forest of Vancouver Island, Canada, Forest Ecology and Management, 140(1): 1-18.   DOI
11 Tan, B., R. Wolfe, J. Masek, F. Gao, and E.F. Vermote, 2010. An illumination correction algorithm on Landsat-TM data, Proc. of 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA. Jul. 25-30, pp. 1964-1967.
12 Tong, X.Y., G.S. Xia, Q. Lu, H. Shen, S. Li, S. You, and L. Zhang, 2020. Land-cover classification with high-resolution remote sensing images using transferable deep models, Remote Sensing of Environment, 237: 111322.   DOI
13 Petropoulos, G.P., H.M. Griffiths, and D.P. Kalivas, 2014. Quantifying spatial and temporal vegetation recovery dynamics following a wildfire event in a Mediterranean landscape using EO data and GIS, Applied Geography, 50: 120-131.   DOI
14 Breiman, L., 2001. Random forests, Machine Learning, 45(1): 5-32.   DOI
15 Chung, M. 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: 179-197 (in Korean with English Abstract).   DOI
16 Gangwon, Research Institute for Gangwon, and Gangwon KOFST (Korean Federation of Science & Technology Societies), 2019. 1st Gangwon Province Disaster Prevention (Wildfire) Forum 2019, Research Institute for Gangwon, Kangwon, KR (in Korean).
17 Jung, M., J. Yeom, and Y. Kim, 2018. Comparison of pre-event VHR optical data and post-event PolSAR data to investigate damage caused by the 2011 Japan tsunami in built-up areas, Remote Sensing, 10(11): 1804.   DOI
18 Korea Forest Service, 2020. Comprehensive Plan for the Prevention of National Forest Fire 2020, Korea Forest Service, Daejeon, KR. https://www.forest.go.kr/kfsweb/cop/bbs/selectBoardArticle.do?nttId=3141052&bbsId=BBSMSTR_1008&pageUnit=9&mn=NKFS_06_09_05, Accessed on Sep. 13, 2020 (in Korean).
19 Teillet, P. M., B. Guindon, and D. G. Goodenough, 1982. On the slope-aspect correction of multispectral scanner data, Canadian Journal of Remote Sensing, 8(2): 84-106.   DOI
20 Zhang, P., Y. Ke, Z. Zhang, M. Wang, P. Li, and S. Zhang, 2018. Urban land use and land cover classification using novel deep learning models based on high spatial resolution satellite imagery, Sensors, 18(11): 3717.   DOI
21 Ministry of Land, Infrastructure and Transport(MOLIT), 2010. The 4th Basic Plan for National Spatial Information Policy (2010-2015), Ministry of Land, Transport and Maritime affairs, Sejong, KR (in Korean).
22 Kim, K.-S., D. Zhang, M.-C. Kang, and S.-J. Ko, 2013. Improved simple linear iterative clustering superpixels. Proc. of 2013 IEEE International Symposium on Consumer Electronics (ISCE), Hsinchu, TW, Jun. 3-6, pp. 259-260.
23 Chung, M., Y. 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
24 Wu, Z., Z. Hu, and Q. Fan, 2012. Superpixel-based unsupervised change detection using multidimensional change vector analysis and SVM-based classification. Proc. of ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, AU, Aug. 25-Sep. 1, pp. 257-262.