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.38 no.5 pt.2, 2022년, pp.781 - 791
박수민 (한국항공우주연구원 위성활용부) , 손보경 (울산과학기술원 도시환경공학과) , 임정호 (울산과학기술원 도시환경공학과) , 강유진 (울산과학기술원 도시환경공학과) , 권춘근 (국립산림과학원 산림환경보전연구부) , 김성용 (국립산림과학원 산림환경보전연구부)
It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, dro...
Abdollahi, M., T. Islam, A. Gupta, and Q. K. Hassan, 2018. An advanced forest fire danger forecasting system: Integration of remote sensing and historical sources of ignition data, Remote Sensing, 10(6):923. https://doi.org/10.3390/rs10060923
Boychuk, D., C.B. McFayden, J. Evens, J. Shields, A. Stacey, D.G. Woolford, M. Wotton, D. Johnston, D. Leonard, and D. McLarty, 2020. Assembling and customizing multiple fire weather forecasts for burn probability and other fire management applications in Ontario, Canada, Fire, 3(2): 16. https://doi.org/10.3390/fire3020016
Farfan, M., C. Dominguez, A. Espinoza, A. Jaramillo, C. Alcantara, V. Maldonado, I. Tovar, and A. Flamenco, 2021. Forest fire probability under ENSO conditions in a semi-arid region: a case study in Guanajuato, Environmental Monitoring and Assessment, 193(10): 1-14. https://doi.org/10.3390/fire3020016
Gudmundsson, L., F.C. Rego, M. Rocha, and S. I. Seneviratne, 2014. Predicting above normal wildfire activity in southern Europe as a function of meteorological drought, Environmental Research Letters, 9(8): 084008. http://dx.doi.org/10.1088/1748-9326/9/8/084008
Jolly, W.M., P.H. Freeborn, W.G. Page, and, B.W. Butler, 2019. Severe fire danger index: A forecastable metric to inform firefighter and community wildfire risk management, Fire, 2(3): 47. https://doi.org/10.3390/fire2030047
Kang, Y.J., S.M. Park, E.N. Jang, J.H. Im, C.G. Kwon, and S.J. Lee, 2019. Spatio-temporal enhancement of forest fire risk index using weather forecast and satellite data in South Korea, Journal of the Korean Association of Geographic Information Studies, 22(4): 116-130 (in Korean with English abstract). https://doi.org/10.11108/kagis.2019.22.4.116
Kang, Y., E. Jang, J. Im, C. Kwon, and S. Kim, 2020. Developing a new hourly forest fire risk index based on catboost in South Korea, Applied Sciences, 10(22): 8213. https://doi.org/10.3390/app10228213
Kim, S.J., C.H. Lim, G.S. Kim, J. Lee, T. Geiger, O. Rahmati, Y. Son, and W. K. Lee, 2019. Multitemporal analysis of forest fire probability using socio-economic and environmental variables, Remote Sensing, 11(1): 86. https://doi.org/10.3390/rs11010086
Pham, B.T., A. Jaafari, M. Avand, N. Al-Ansari, T. Dinh Du, H.P.H. Yen, T.V. Phong, D.H Nguyen, D. Mafi-Gholami, I. Prakash, H. Thi Thuy, and T.T. Tuyen, 2020. Performance evaluation of machine learning methods for forest fire modeling and prediction, Symmetry, 12(6): 1022. http://dx.doi.org/10.3390/sym12061022
Preisler, H.K. and A.L. Westerling, 2007. Statistical model for forecasting monthly large wildfire events in western United States, Journal of Applied Meteorology and Climatology, 46(7): 1020-1030. https://doi.org/10.1175/JAM2513.1
Sahoo, S., P.K. Gupta, and S.K. Srivastav, 2020. Intercalibration of DMSP-OLS and SNPP-VIIRSDNB annual nighttime light composites using machine learning, GIScience & Remote Sensing, 57(8): 1144-1165. https://doi.org/10.1080/15481603.2020.1848323
Sothe, C., C.M. De Almeida, M.B. Schimalski, L.E.C. La Rosa, J.D.B. Castro, R.Q. Feitosa, M. Dalponte, C.L. Lima, V. Liesenberg, G.T. Miyoshi, and A.M.G. Tommaselli, 2020. Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data, GIScience & Remote Sensing, 57(3): 369-394. https://doi.org/10.1080/15481603.2020.1712102
Yarragunta, Y., S. Srivastava, D. Mitra, and H.C. Chandola, 2020. Influence of forest fire episodes on the distribution of gaseous air pollutants over Uttarakhand, India, GIScience & Remote Sensing, 57(2): 190-206. https://doi.org/10.1080/15481603.2020.1712100
*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.