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http://dx.doi.org/10.7780/kjrs.2022.38.5.3.7

Monitoring the Ecological Drought Condition of Vegetation during Meteorological Drought Using Remote Sensing Data  

Won, Jeongeun (Major of Environmental Engineering, Division of Earth Environmental System Science, Pukyong National University)
Jung, Haeun (Major of Environmental Engineering, Division of Earth Environmental System Science, Pukyong National University)
Kang, Shinuk (SmartCity R&D Laboratory, K-water Research Institute)
Kim, Sangdan (Major of Environmental Engineering, Division of Earth Environmental System Science, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.38, no.5_3, 2022 , pp. 887-899 More about this Journal
Abstract
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 proposed to quantitatively monitor the degree of impact. EDCI-veg is derived from a copula-based bi-variate joint probability model between vegetation and meteorological drought information, and can be expressed numerically how affected the current vegetation condition was by the drought when the drought occurred. Comparing past meteorological drought events with their corresponding vegetation condition, the proposed index was examined, and it was confirmed that EDCI-veg could properly monitor the ecological drought of vegetation. In addition, it was possible to spatially identify ecological drought conditions by creating a high-resolution drought map using remote sensing data.
Keywords
Copula; Drought; EDCI-veg; Remote sensing data; Vegetation;
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1 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   DOI
2 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   DOI
3 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   DOI
4 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   DOI
5 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
6 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   DOI
7 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   DOI
8 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   DOI
9 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   DOI
10 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   DOI
11 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   DOI
12 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   DOI
13 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   DOI
14 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.
15 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   DOI
16 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   DOI
17 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<0621:GDWFS>2.0.CO;2   DOI
18 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   DOI
19 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   DOI
20 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   DOI
21 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   DOI
22 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   DOI
23 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   DOI
24 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   DOI
25 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   DOI
26 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   DOI
27 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   DOI