Application of satellite observations for assessment of regional hydrological and hydrogeological risks
Heading:
1Kostyuchenko, Yu.V, 2Kopachevsky, IM, 3Solovyov, DM, 4Yushchenko, MV, 5Akymenko, PO 1State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine», Kyiv, Ukraine 2State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Science of the National Academy of Sciences of Ukraine», Kyiv, Ukraine 3State institution «Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine», Kyiv; Marine Hydrophysical Institute Ukrainian National Academy of Sciences, Sevastopol 4State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine», Kyiv, Ukraine 5Aerospace Company of Ukraine, Kyiv,Ukraine |
Kosm. nauka tehnol. 2011, 17 ;(6):19-29 |
https://doi.org/10.15407/knit2011.06.019 |
Publication Language: Ukrainian |
Abstract: We discuss a procedure for the calculation of the hydrological and hydrogeological risk assessment with the use of remote sensing methods. A model for the propagation of dangerous processes is proposed and a procedure for satellite image decoding as well as for analyzing spectral indicators is developed on the basis of the model. Regional distributions of climatic parameters for the observational period are analyzed and key factors of impact are identified. The changes in values of the spectral indices are determined which can be used as information indicators of dangerous processes. We propose an equation to determine the stress probability from a set of spectral characteristics. A method for the assessment of the integrated risk associated with hydrological and hydrogeological hazards is developed.
|
Keywords: information indicators, satellite observations, spectral indices |
References:
1. Lyalko V. I. (Ed.) Earth Systems Change over Eastern Europe, 582 p. (Kiev, 2010) [in Russian].
2. Kostyuchenko Yu. V., Kopachevsky I. M., Yushchenko M. V. Theoretical and methodological principles of hydrological and hydrogeological risk assessment from remote sensing data. Kosm. nauka tehnol., 17 (6), 10—18 (2011) [in Ukrainian].
3. Lisichenko G. V., Zabulonov Yu. L., Khmil G. A. Natural, technological and environmental risks: analysis, valuation, management, 541 p. (Naukova dumka, Kyiv, 2008) [in Ukrainian].
4. Blackburn G. A. Spectral indices for estimation photosynthetic pigment concentrations: a test using senescent tree leaves. Int. J. Remote Sens., 4, 657— 675 (1998).
https://doi.org/10.1080/014311698215919 /a>
https://doi.org/10.1080/014311698215919 /a>
5. Choudhury B. J. Estimating gross photosynthesis using satellite and ancillary data: approach and preliminary results. Remote Sens. Environ., 75, 1— 21 (2001).
https://doi.org/10.1016/S0034-4257(00)00151-6
https://doi.org/10.1016/S0034-4257(00)00151-6
6. Dobrowski S. Z., Pushnic J. C., Zarco-Tejada P. J., Ustin S. L. Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale. Remote Sens. Environ., 97, 403—414 (2005).
7. Gamon J. A., Serrano L., Surfus J. S. The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types and nutrient levels. Oecologia, 112, 492—501 (1997).
https://doi.org/10.1007/s004420050337
https://doi.org/10.1007/s004420050337
8. Gao B. C. Normalized difference water index for remote sensing of vegetation liquid water from space. Proc. SPIE, 2480, 225—236 (1995).
https://doi.org/10.1117/12.210877
https://doi.org/10.1117/12.210877
9. Guha-Sapir D., Vos F., Below R., Ponserre S. Annual disaster statistical review 2010: the numbers and trends, 50 p. (Université catholique de Louvain — Centre for Research on the Epidemiology of Disasters, Brussels, 2011).
10. Huete A. R., Liu H., Batchily K., van Leeuwen W. A comparison of vegetation indices over a global set of TM Images for EOS-MODIS. Remote Sens. Environ., 59, 440—451 (1997).
https://doi.org/10.1016/S0034-4257(96)00112-5
https://doi.org/10.1016/S0034-4257(96)00112-5
11. Jackson R. D., Slater P. N., Pinter P. J. Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres. Remote Sens. Environ., 15, 187—208 (1983).
https://doi.org/10.1016/0034-4257(83)90039-1
https://doi.org/10.1016/0034-4257(83)90039-1
12. Kostyuchenko Y. V., Kopachevsky I., Solovyov D., et al. Way to reduce the uncertainties on ecological consequences assessment of technological disasters using satellite observations. In: Robust Design — Coping with Hazards, Risk and Uncertainty: Proc. of the 4th International Workshop on Reliable Engineering Computing, March 3—5, 2010, 765—776 (National University of Singapore, Singapore, 2010).
13. Penuelas J., Baret F., Filella I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reactance. Photosynthetica, 31, 221— 230 (1995).
14. Verma S. B., Sellers P. J., Walthall C. L., et al. Photosynthesis and stomatal conductance related to reflectance on the canopy scale. Remote Sens. Environ., 44, 103—116 (1993).