Wildfire Severity Mapping Using Sentinel Satellite Data Based on Machine Learning Approaches |
Sim, Seongmun
(Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Kim, Woohyeok (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) Lee, Jaese (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) Kang, Yoojin (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) Im, Jungho (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) Kwon, Chunguen (Division of Forest Disaster Management, National Institute of Forest Science) Kim, Sungyong (Division of Forest Disaster Management, National Institute of Forest Science) |
1 | Addison, P., and T. Oommen, 2018. Utilizing satellite radar remote sensing for burn severity estimation, International Journal of Applied Earth Observation and Feoinformation, 73: 292-299. DOI |
2 | Bak, S. H., H. M. Kim, B. K. Kim, D. H. Hwang, E. Unuzaya, and H. J. Yoon, 2018. Study on Detection Technique for Cochlodinium polykrikoides Red tide using Logistic Regression Model and Decision Tree Model, The Journal of the Korea Institute of Electronic Communication Sciences, 13(4): 777-786 (in Korean with English abstract). DOI |
3 | Ban, Y., P. Zhang, A. Nascetti, A. R. Bevington, and M. A. Wulder, 2020. Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning, Scientific Reports, 10(1): 1-15. DOI |
4 | Breiman, L., 2001. Random forests, Machine Learning, 45(1): 5-32. DOI |
5 | Brown, A. R., G. P. Petropoulos, and K. P. Ferentinos, 2018. Appraisal of the Sentinel-1 & 2 use in a large-scale wildfire assessment: A case study from Portugal's fires of 2017, Applied Geography, 100: 78-89. DOI |
6 | Cieslak, D. A., and N. V. Chawla, 2008. Learning decision trees for unbalanced data, In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, Berlin, Heidelberg, pp. 241-256. |
7 | Collins, L., P. Griffioen, G. Newell, and A. Mellor, 2018. The utility of Random Forests for wildfire severity mapping, Remote Sensing of Environment, 216: 374-384. DOI |
8 | Escuin, S., R. Navarro, and P. Fernandez, 2008. Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images, International Journal of Remote Sensing, 29(4): 1053-1073. DOI |
9 | Hall, R. J., J. T. Freeburn, W. J. De Groot, J. M. Pritchard, T. J. Lynham, and R. Landry, 2008. Remote sensing of burn severity: experience from western Canada boreal fires, International Journal of Wildland Fire, 17(4): 476-489. DOI |
10 | Han, D., Y. J. Kim, J. Im, S. Lee, Y. Lee, and H. C. Kim, 2018. The estimation of arctic air temperature in summer based on machine learning approaches using IABP buoy and AMSR2 satellite data, Korean Journal of Remote Sensing, 34(6-2): 1261-1272 (in Korean with English abstract). DOI |
11 | Hosmer Jr, D. W., S. Lemeshow, and R. X. Sturdivant, 2013. Applied logistic regression (Vol. 398). John Wiley & Sons. |
12 | Mountrakis, G., J. Im, and C. Ogole, 2011. Support vector machines in remote sensing: A review, ISPRS Journal of Photogrammetry and Remote Sensing, 66(3): 247-259. DOI |
13 | Korea Forest Service, 2016. Forest basic statistics 2016, Korea Forest Service, 189, Cheongsas-ro, Seogu, Daejeon. (in Korean). |
14 | Korea Forest Service, 2020. Forestfire statistical yearbook 2019, Korea Forest Service, 189, Cheongsas-ro, Seo-gu, Daejeon (in Korean). |
15 | Lasaponara, R., and B. Tucci, 2019. Identification of burned areas and severity using SAR Sentinel-1, IEEE Geoscience and Remote Sensing Letters, 16(6): 917-921. DOI |
16 | Lentile, L. B., Z. A. Holden, A. M. Smith, M. J. Falkowski, A. T. Hudak, P. Morgan, and N. C. Benson, 2006. Remote sensing techniques to assess active fire characteristics and post-fire effects, International Journal of Wildland Fire, 15(3): 319-345. DOI |
17 | Liu, T., A. Abd-Elrahman, J. Morton, and V. L. Wilhelm, 2018. Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system, GIScience & Remote Sensing, 55(2): 243-264. DOI |
18 | National Institute of Forest Science, 2013. A study on damage characteristics and development of burn severity evaluation methods, National Institute of Forest Science Research report, Seoul, Korea, pp. 13-37 (in Korean). |
19 | Park, S., M. Shin, J. Im, C. K. Song, M. Choi, J. Kim, and S. K. Kim, 2019. Estimation of groundlevel particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea, Atmospheric Chemistry and Physics, 19(2): 1097-1097. DOI |
20 | 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 |
21 | 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. DOI |
22 | Won, M., K. Kim, and S. Lee, 2014. Analysis of Burn Severity in Large-fire Area Using SPOT5 Imagesand Field Survey Data, Korean Journal of Agricultural and Forest Meteorology, 16(2): 114-124 (in Korean with English abstract). DOI |
23 | 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. DOI |
24 | Stankova, N., and R. Nedkov, 2015. Monitoring forest regrowth with different burn severity using aerial and Landsat data, In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, IT, Jul. 26-31, pp. 2166-2169. |
25 | Wilson, K. B., and D. D. Baldocchi, 2000. Seasonal and interannual variability of energy fluxes over a broadleaved temperate deciduous forest in North America, Agricultural and Forest Meteorology, 100(1): 1-18. DOI |
26 | Won, M., K. Jang, S. Yoon, and H.T. Lee, 2019. Change Detection of Damaged Area and Burn Severity due to Heat Damage from Gangwon Large Fire Area in 2019, Korean Journal of Remote Sensing, 35(6): 1083-1093 (in Korean with English abstract). DOI |
27 | Yoo, C., D. Han, J. Im, and B. Bechtel, 2019. Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images, ISPRS Journal of Photogrammetry and Remote Sensing, 157: 155-170. DOI |
28 | Youn, H., J. Jeong, 2019. Detection of Forest Fire and NBR Mis-classified Pixel Using Multi-temporal Sentinel-2A Images, Korean Journal of Remote Sensing, 35(6-2): 1107-1115 (in Korean with English abstract). DOI |