최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기대한원격탐사학회지 = Korean journal of remote sensing, v.37 no.5 pt.3, 2021년, pp.1373 - 1387
이재세 (울산과학기술원 도시환경공학과) , 김우혁 (울산과학기술원 도시환경공학과) , 임정호 (울산과학기술원 도시환경공학과) , 권춘근 (국립산림과학원 산림환경보전연구부 산불.산사태연구과) , 김성용 (국립산림과학원 산림환경보전연구부 산불.산사태연구과)
Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy ...
Addison, P. and T. Oommen, 2018. Utilizing satellite radar remote sensing for burn severity estimation, International Journal of Applied Earth Observation and Geoinformation, 73: 292-299.
Al-Rawi, K.R., J.L. Casanova, and A. Calle, 2001. Burned area mapping system and fire detection system, based on nueral networks and NOAAAVHRR imagery, International Journal of Remote Sensing, 22(10): 2015-2032.
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.
Bayer, T., R. Winter, and G. Schreier, 1991. Terrain influences in SAR backscatter and attempts to their correction, IEEE Transactions on Geoscience and Remote Sensing, 29(3): 451-462.
Bin, W., L. Ming, J. Dan, L. Suju, C. Qiang, W. Chao, Z. Yang, Y. Huan, and Z. Jun, 2019. A method of automatically extracting forest fire burned areas using gf-1 remote sensing images, Proc. of International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, JP, Jul, 28-Aug. 2, pp. 9953-9955.
Bowman, D.M.J.S., J.K. Balch, P. Artaxo, W. J. Bond, J.M. Carlson, M.A. Cochrane, C.M. D'Antonio, R.S. DeFries, J.C. Doyle, S.P. Harrison, F.H. Johnston, J.E. Keeley, M.A. Krawchuk, C.A. Kull, J.B. Marston, M.A. Moritz, I.C. Prentice, C.I. Roos, A.C. Scott, T.W. Swetnam, G.R. Van Der Werfand, and S.J. Pyne, 2009. Fire in the earth system, Science, 324(5926): 481-484.
Brown, A.R., G.P. Petropoulos, and K.P. Ferentinos, 2018. Appraisal of the Sentinel-1 and 2 use in a large-scale wildfire assessment: A case study from Portugal's fires of 2017, Applied Geography, 100: 78-89.
Chen, D., T.V. Loboda, and J.V. Hall, 2020. A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems, ISPRS Journal of Photogrammetry and Remote Sensing, 159: 63-77.
De Luca, G., J.M.N. Silva, and G. Modica, 2021. A workflow based on Sentinel-1 SAR data and open-source algorithms for unsupervised burned area detection in Mediterranean ecosystems, GIScience and Remote Sensing, 58(4): 516-541.
Drusch, M., U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, C. Isola, P. Laberinti, P. Martimort, A. Meygret, F. Spoto, O. Sy, F. Marchese, and P. Bargellini, 2012. Sentinel2: ESA's Optical High-Resolution Mission for GMES Operational Services, Remote Sensing of Environment, 120: 25-36.
Engelbrecht, J., A. Theron, L. Vhengani, and J. Kemp, 2017. A simple normalized difference approach to burnt area mapping using multi-polarisation C-Band SAR, Remote Sensing, 9(8): 9-11.
Franco, M.G., I.A. Mundo, and T.T. Veblen, 2020. Field-validated burn-severity mapping in North Patagonian forests, Remote Sensing, 12(2): 1-18.
Geudtner, D., R. Torres, P. Snoeij, M. Davidson, and B. Rommen, 2014. Sentinel-1 System capabilities and applications, Proc. of 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, CA, Jul. 13-18, pp. 1457-1460.
Gimeno, M., J.S.M. Ayanz, P.M. Barbosa, and G. Schmuck, 2003. Burnt-area mapping from ERS-SAR time series using the principal components transformation, In SAR Image Analysis, Modeling, and Techniques V, International Society for Optics and Photonics, 4883: 171-180.
Gill, A.M. S.L. Stephens, and G.J. Cary, 2013. The worldwide "wildfire" problem, Ecological Applications, 23(2): 438-454.
Imperatore, P., R. Azar, F. Calo, D. Stroppiana, P.A. Brivio, R. Lanari, and A. Pepe,2017. Effect of the Vegetation Fire on Backscattering: An Investigation Based on Sentinel-1 Observations, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(10): 4478-4492.
Jung, J., S.H. Yun, D.J. Kim, and M. Lavalle, 2018. Damage-Mapping Algorithm Based on Coherence Model Using Multitemporal Polarimetric-Interferometric SAR Data, IEEE Transactions on Geoscience and Remote Sensing, 56(3): 1520-1532.
Key, C.H. and N.C. Benson, 2006. Landscape Assessment (LA) sampling and analysis methods, USDA Forest Service - General Technical Report RMRS-GTR-164-CD, Washington, D.C., D.C., USA.
Korea Forest Service, 2021. Forestfire statistical yearbook 2020, Korea Forest Service, Daejeon, KR (in Korean).
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.
Mallinis, G., I. Mitsopoulos, and I. Chrysafi, 2018. Evaluating and comparing sentinel 2A and landsat-8 operational land imager (OLI) spectral indices for estimating fire severity in a mediterranean pine ecosystem of Greece, GIScience and Remote Sensing, 55(1): 1-18.
Miller, J.D. and A.E. Thode, 2007. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR), Remote Sensing of Environment, 109(1): 66-80.
Miller, J.D., E.E. Knapp, C.H. Key, C.N. Skinner, C.J. Isbell, R.M. Creasy, and J.W. Sherlock, 2009. Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA, Remote Sensing of Environment, 113(3): 645-656.
Navarro, G., I. Caballero, G. Silva, P.C. Parra, A. Vazquez, and R. Caldeira, 2017. Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery, International Journal of Applied Earth Observation and Geoinformation, 58: 97-106.
Parks, S.A., G.K. Dillon, and C. Miller, 2014. A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio, Remote Sensing, 6(3): 1827-1844.
Quintano, C., A. Fernandez-Manso, and O. Fernandez-Manso, 2018. Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity, International Journal of Applied Earth Observation and Geoinformation, 64: 221-225.
Rousseeuw, P. J., 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, 20: 53-65.
Rizkinia, M. and D. Sudiana, 2021. Evaluation of Combining Optical and SAR Imagery for Burned Area Mapping using Machine Learning, Proc. of 2021 IEEE 11th Annual Computing and Communication Workshop and Conference, NV, USA, Jan. 27-30, pp. 52-59.
Roy, D.P., L. Boschetti, and A.M. Smith, 2013. Satellite Remote Sensing of Fires, Fire Phenomena and the Earth System: An Interdisciplinary Guide to Fire Science, 2013: 77-93.
Saulino, L., A. Rita, A. Migliozzi, C. Maffei, E. Allevato, A. Pierto Garonna, and A. Saracino, 2020. Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data, Remote Sensing, 12(4): 1-21.
Schimel, D. and D. Baker, 2002. The wildfire factor, Nature, 420: 29-30.
Tanase, M.A., M. Santoro, J. De La Riva, F. Perez-Cabello, and T. Le Toan, 2010. Sensitivity of X-, C-, and L-band SAR backscatter to burn severity in Mediterranean pine forests, IEEE Transactions on Geoscience and Remote Sensing, 48(10): 3663-3675.
Tanase, M.A., M.A. Belenguer-Plomer, E. Roteta, A. Bastarrika, J. Wheeler, A. Fernandez-Carrillo, K. Tansey, W. Wiedemann, P. Navratil, S. Lohberger, F. Siegert, and E. Chuvieco, 2020. Burned area detection and mapping: Intercomparison of Sentinel-1 and Sentinel-2 based algorithms over tropical Africa, Remote Sensing, 12(2): 334.
Tariq, A., H. Shu, Q. Li, O. Altan, M.R. Khan, M.F. Baqa, and L. Lu, 2021. Quantitative analysis of forest fires in southeastern australia using sar data, Remote Sensing, 13(12): 2386.
Vassilvitskii, S. and D. Arthur, 2006. k-means++: The advantages of careful seeding, In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pp. 1027-1035.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.