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.3, 2022년, pp.887 - 899
원정은 (부경대학교 지구환경시스템과학부 환경공학전공) , 정하은 (부경대학교 지구환경시스템과학부 환경공학전공) , 강신욱 (K-water연구원 스마트시티 R&D 실증센터) , 김상단 (부경대학교 지구환경시스템과학부 환경공학전공)
Drought caused by meteorological factors negatively affects vegetation in terrestrial ecosystems. In this study, the state in which meteorological drought affects vegetation was defined as the ecological drought of vegetation, and the ecological drought condition index of vegetation (EDCI-veg) was p...
Almamalachy, Y.S., A.M.F. Al-Quraishi, and H. Moradkhani, 2020. Agricultural drought monitoring over Iraq utilizing MODIS products, In: Al-Quraishi, A., Negm, A. (eds), Environmental Remote Sensing and GIS in Iraq, Springer, Cham, Switzerland, pp. 253-278. https://doi.org/10.1007/978-3-030-21344-2_11
Boori, M.S., K. Choudhary, and A. Kupriyanov, 2022. Detecting vegetation drought dynamics in European Russia, Geocarto International, 37(9): 2490-2505. https://doi.org/10.1080/10106049.2020.1750063
Brandt, M., G. Tappan, A.A. Diouf, G. Beye, C. Mbow, and R. Fensholt, 2017. Woody vegetation die off and regeneration in response to rainfall variability in the West African Sahel, Remote Sensing, 9(1): 39. https://doi.org/10.3390/rs9010039
Choi, M., J.M. Jacobs, M. C. Anderson, and D. D. Bosch, 2013. Evaluation of drought indices via remotely sensed data with hydrological variables, Journal of Hydrology, 476: 265-273. https://doi.org/10.1016/j.jhydrol.2012.10.042
Crausbay, S.D., A.R. Ramirez, S.L. Carter, M.S. Cross, K.R. Hall, D.J. Bathke, J.L. Betancourt, S. Colt, A.E. Cravens, M.S. Dalton, J.B. Dunham, L.E. Hay, M.J. Hayes, J. McEvoy, C.A. McNutt, M.A. Moritz, K.H. Nislow, N. Raheem, and T. Sanford, 2017. Defining ecological drought for the twenty-first century, Bulletin of the American Meteorological Society, 98(12): 2543-2550. https://doi.org/10.1175/BAMS-D-16-0292.1
Cunha, A.P.M., R.C. Alvala, C.A. Nobre, and M.A. Carvalho, 2015. Monitoring vegetative drought dynamics in the Brazilian semiarid region, Agricultural and Forest Meteorology, 214: 494-505. https://doi.org/10.1016/j.agrformet.2015.09.010
Djebou, D.C.S., V.P. Singh, and O.W. Frauenfeld, 2015. Vegetation response to precipitation across the aridity gradient of the southwestern United states, Journal of Arid Environments, 115: 35-43. https://doi.org/10.1016/j.jaridenv.2015.01.005
Fang, W., S. Huang, Q. Huang, G. Huang, H. Wang, G. Leng, L. Wang, and Y. Guo, 2019. Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China, Remote Sensing of Environment, 232: 111290. https://doi.org/10.1016/j.rse.2019.111290
Javed, T., Y. Li, S. Rashid, F. Li, Q. Hu, H. Feng, X. Chen, S. Ahmad, F. Liu, and B. Pulatov, 2021. Performance and relationship of four different agricultural drought indices for drought monitoring in China's mainland using remote sensing data, Science of The Total Environment, 759: 143530. https://doi.org/10.1016/j.scitotenv.2020.143530
Jehanzaib, M. and T.W. Kim, 2020. Exploring the influence of climate change-induced drought propagation on wetlands, Ecological Engineering, 149: 105799. https://doi.org/10.1016/j.ecoleng.2020.105799
Jha, S., J. Das, A. Sharma, B. Hazra, and M.K. Goyal, 2019. Probabilistic evaluation of vegetation drought likelihood and its implications to resilience across India, Global and Planetary Change, 176: 23-35. https://doi.org/10.1016/j.gloplacha.2019.01.014
Kogan, F.N., 1997. Global drought watch from space, Bulletin of the American Meteorological Society, 78(4): 621-636. https://doi.org/10.1175/1520-0477(1997)078 2.0.CO;2
Lovelock, C.E. and J. Ellison, 2007. Chapter 9 Vulnerability of mangroves and tidal wetlands of the Great Barrier Reef to climate change, In: Johnson, J., Marshall, P. (eds), Climate Change and the Great Barrier Reef, Great Barrier Reef Marine Park Authority and Australian Greenhouse Office, Australia, pp. 237-269. http://hdl.handle.net/11017/542
McKee, T.B., N.J. Doesken, and J. Kleist, 1993. The relationship of drought frequency and duration to time scales, Proc. of the 8th Conference on Applied Climatology, Anaheim, CA, Jan. 17-22, vol. 17, pp. 179-183.
Rhee, J., J. Im, and G. J. Carbone, 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data, Remote Sensing of Environment, 114(12): 2875-2887. https://doi.org/10.1016/j.rse.2010.07.005
Sadegh, M., E. Ragno, and A. AghaKouchak, 2017. Multivariate Copula Analysis Toolbox (MvCAT): describing dependence and underlying uncertainty using a Bayesian framework, Water Resources Research, 53(6): 5166-5183. https://doi.org/10.1002/2016WR020242
Sandeep, P., G.O. Reddy, R. Jegankumar, and K.A. Kumar, 2021. Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets, Ecological Indicators, 121: 107033. https://doi.org/10.1016/j.ecolind.2020.107033
Shukla, S., A. McNally, G. Husak, and C. Funk, 2014. A seasonal agricultural drought forecast system for food-insecure regions of East Africa, Hydrology and Earth System Sciences, 18(10): 3907-3921. https://doi.org/10.5194/hess-18-3907-2014
Sur, C., J. Hur, K. Kim, W. Choi, and M. Choi, 2015. An evaluation of satellite-based drought indices on a regional scale, International Journal of Remote Sensing, 36(22): 5593-5612. https://doi.org/10.1080/01431161.2015.1101653
Wan, Z., P. Wang, and X. Li, 2004. Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA, International Journal of Remote Sensing, 25(1): 61-72. https://doi.org/10.1080/0143116031000115328
Wang, H., H. Lin, and D. Liu, 2014. Remotely sensed drought index and its responses to meteorological drought in Southwest China, Remote Sensing Letters, 5(5): 413-422. https://doi.org/10.1080/2150704X.2014.912768
Wang, L., S. Huang, Q. Huang, G. Leng, Z. Han, J. Zhao, and Y. Guo, 2021. Vegetation vulnerability and resistance to hydrometeorological stresses in water-and energy-limited watersheds based on a Bayesian framework, Catena, 196: 104879. https://doi.org/10.1016/j.catena.2020.104879
Wang, S., X. Mo, S. Hu, S. Liu, and Z. Liu, 2018. Assessment of droughts and wheat yield loss on the North China Plain with an aggregate drought index (ADI) approach, Ecological Indicators, 87: 107-116. https://doi.org/10.1016/j.ecolind.2017.12.047
West, H., N. Quinn, and M. Horswell, 2019. Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities, Remote Sensing of Environment, 232: 111291. https://doi.org/10.1016/j.rse.2019.111291
Won, J., J. Seo, J. Lee, O. Lee, and S. Kim, 2021. Vegetation Drought Vulnerability Mapping Using a Copula Model of Vegetation Index and Meteorological Drought Index, Remote Sensing, 13(24): 5103. https://doi.org/10.3390/rs13245103
Zhang, M., X. Yuan, and J.A. Otkin, 2020. Remote sensing of the impact of flash drought events on terrestrial carbon dynamics over China, Carbon Balance and Management, 15(1): 1-11. https://doi.org/10.1186/s13021-020-00156-1
Zhou, L., J. Wu, J. Zhang, S. Leng, M. Liu, J. Zhang, L. Zhao, F. Zhang, and Y. Shi, 2013. The integrated surface drought index (ISDI) as an indicator for agricultural drought monitoring: theory, validation, and application in Mid-Eastern China, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3): 1254-1262. https://doi.org/10.1109/JSTARS.2013.2248077
*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.