«IZVESTIYA IRKUTSKOGO GOSUDARSTVENNOGO UNIVERSITETA». SERIYA «NAUKI O ZEMLE»
«THE BULLETIN OF IRKUTSK STATE UNIVERSITY». SERIES «EARTH SCIENCES»
ISSN 2073-3402 (Print)

List of issues > Series «Earth Sciences». 2023. Vol 44

Modeling the Dependence of the Area and Volume of Posolsky Bay from the Level of Lake Baikal

Author(s)
E. V. Boldanova
Abstract
The article is devoted to the issues of regulating the level of Lake Baikal and assessing its consequences. Posolsky and Maly bays of Lake Baikal are of great fishery importance. It is here that the larvae of the omul of Posolsky population feed themselves. The survival of juveniles depends on fluctuations in the areas and volumes of these reservoirs. The purpose of this study is to determine the dependence of the areas and water bodies of the Posolsky and Maly bays on the level of Lake Baikal. To achieve this goal, the tasks of choosing indices for determining the coastline from Sentinel-2 satellite images, processing the initial data, calculating the areas of reservoirs at different levels of Baikal, estimating the model parameters and using it to build a digital elevation model (DEM) were set and solved. A number of indices for determining the boundaries of a reservoir are considered, and the NDWI index is chosen based on the overall accuracy and Cohen's kappa coefficient. With the help of the DEM, the volumes of reservoirs were determined. The obtained dependencies allowed us to conclude that at the level of the lake. Baikal, within the limits of 456.2-456.8 m of the Pacific height system, slight changes in areas and volumes are observed, which can be considered safe for the survival of omul juveniles. The most critical changes occur when the lake level drops below 456.0 m and exceeds 457.0 m. The results obtained will make it possible to make more informed decisions on lake level regulation.
About the Authors
Boldanova Elena Vladimirovna, Candidate of Sciences (Economy), Associate Professor, Department of Sectoral Economics and Natural Resources Management, Baikal State University, 11, Lenin st., Irkutsk, 664003, Russian Federation, e-mail: boldanova@mail.ru
For citation
Boldanova E.V. Modeling the Dependence of the Area and Volume of Posolsky Bay from the Level of Lake Baikal. The Bulletin of Irkutsk State University. Series Earth Sciences, 2023, vol. 44, pp. 33-43. https://doi.org/10.26516/2073- 3402.2023.44.33 (in Russian)
Keywords
Posolsky Bay, Small Bay, Lake Baikal, Sentinel-2, DEM, NDWI.
UDC
528.88 (571.54)
DOI
https://doi.org/10.26516/2073-3402.2023.44.33
References

Boldanova E.V. Otsenka trofnosti ozera Baikal s ispolzovaniem distantsionnogo zondirovaniya [Evaluation of the trophic status of lake Baikal using remote sensing]. Geograficheskiy vestnik [Geographical bulletin], 2022, vol. 2, no. 61, pp. 73-89. https://doi.org/10.17072/2079-7877-2022-2-73-89(in Russian)

Boldanova E.V. Proverka tochnosti vodnykh raznostnykh indeksov po dannym DZZ dlya otsenki beregovoy linii oz. Baykal [Verification of the accuracy of water difference indices according to remote sensing data for assessing the coastline of the lake. Baikal]. Sovremennyye tendentsii i perspektivy razvitiya gidrometeorologii v Rossii: materialy V Vserossiyskoy nauchno-prakticheskoy konferentsii [Modern trends and prospects for the development of hydrometeorology in Russia: materials of the V All-Russian Scientific and Practical Conference]. Irkutsk, ISU Publ., 2023, pp. 42-45.https://doi.org/10.26516/978-5-9624-2119-3.2023.1-455 (in Russian)

Kutyavina Т.I., Rutman V.V., Аshikhmina Т.Ya., Savinykh V.P. Ispolzovanie kosmicheskikh snimkov dlya opredeleniya granits vodoemov i izucheniya protsessov evtrofikatsii [The use of satellite images to determine the boundaries of water bodies and study the processes of eutrophication]. Teoreticheskaya i prikladnaya ekologiya [Theoretical and Applied Ecology], 2019, vol. 3, pp. 28-33.https://doi.org/10.25750/1995-4301-2019-3-028-033 (in Russian)

Lurie I.K. Geoinformatsionnoe kartografirovanie. Metody geoinformatiki i tsifrovoi obrabotki kosmicheskikh snimkov [Geoinformation mapping. Methods of geoinformatics and digital processing of space images]. Moscow, KDU Publ., 2008, 424 p. (in Russian)

Sukhodolov A.P., Fedotov A.P., Makarov M.M. [et al.]. Perspektivy rybokhozyaistvennogo ispolzovaniya Malomorskogo rybopromyslovogo raiona: ekonomicheskaya otsenka i obosnovanie [Prospects of fish-husbandry utilization of Maloye more fishing area: economic assessment and substantiation]. Izvestiya Baikalskogo gosudarstvennogo universiteta [Bulletin of Baikal State University], 2020, vol. 30, no. 2, pp. 233-244. https://doi.org/10.17150/2500-2759.2020.30(2).233-244 (in Russian)

Pogorelov A.V., Lipilin D.A., Kurnosova A.S. Sputnikovyi monitoring Krasnodarskogo vodokhranilishcha [Satellite monitoring of the Krasnodar reservoir] Geograficheskiy vestnik [Geographical bulletin], 2017, vol. 1, no. 40, pp. 130-137. https://doi.org/10.17072/2079-7877-2017-1-130-137 (in Russian)

Kutyavina T.I., Kantor G.Ya., Ashikhmina T.Ya., Savinykh V.P. Primenenie metodov obrabotki i analiza kosmicheskikh snimkov dlya izucheniya evtrofirovannykh vodoemov (obzor) [Application of methods for processing and analysis of satellite images for the study of eutrophied reservoirs (review)]. Teoreticheskaya i prikladnaya ekologiya[Theoretical and Applied Ecology], 2020, vol. 2, pp. 14-25. https://doi.org/10.25750/1995-4301-2020-2-014-025 (in Russian)

Rusetskaya G.D., Bykova D.Yu. Ekologicheski ustoichivoe i sotsialno-ekonomicheski otvetstvennoe prirodopolzovanie v sisteme ostrova Olkhon [Environmentally sustainable and socioeconomically responsible management of natural resources in the ecosystem of Olkhon island]. Izvestiya Baikalskogo gosudarstvennogo universiteta [Bulletin of Baikal State University], 2020, vol. 30, no. 1, pp. 7-13.https://doi.org/10.17150/2500-2759.2020.30(1).7-13 (in Russian)

Rusetskaya G.D., Dmyterko E.A. Osobo okhranyaemye prirodnye territorii – instrument ustoichivogo upravleniya prirodopolzovaniem [Nature conservation areas as a tool of sustainable natural resource management]. Izvestiya Baikalskogo gosudarstvennogo universiteta [Bulletin of Baikal State University], 2017, vol. 27, no. 4, pp. 478-487. https://doi.org/10.17150/2500-2759.2017.27(4).478-487 (in Russian)

Rylov S.A., Pestunov I.A. Opredelenie ploshchadei ozer po dannym so sputnikov serii Sentinel-2 [Assessment of Lakes Areas by Sentinel-2 Satellite Data]. Zhurnal SFU. Tekhnika i tekhnologii [Journal of Siberian Federal University. Engineering & Technologies], 2019, no. 5, pp. 526-535.https://doi.org/10.17516/1999-494X-0108 (in Russian)

Jiang W., Ni Y., Pang Z. [et al.]. A new index for identifying water body from Sentinel2 satellite remote sensing imagery. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., vol. V3-2020, pp. 33-38, https://doi.org/10.5194/isprs-annals-V-3-2020-33-2020

Bhangale U., More S., Shaikh T. [et al.]. Analysis of Surface Water Resources Using Sentinel2 Imagery. Procedia Computer Science, 2020, vol. 171, pp. 2645-2654. https://doi.org/10.1016/j.procs.2020.04.287

Karim M., Maanan M., Maanan M. et al. Assessment of water body change and sedimentation rate in Moulay Bousselham wetland, Morocco, using geospatial technologies. Int. J. Sediment Res., 2019, vol. 34, pp. 65-72, https://doi.org/10.1016/j.ijsrc.2018.08.007

Kordelas G.A, Manakos I., Lefebvre G., Poulin B. Automatic Inundation Mapping Using Sentinel-2 Data Applicable to Both Camargue and Doñana Biosphere Reserves. Remote Sensing, 2019, vol. 11, no. 19, 2251. https://doi.org/10.3390/rs11192251

Babaei H., Janalipour M., Tehrani N.A. A simple, robust, and automatic approach to extract water body from Landsat images (case study: Lake Urmia, Iran). Journal of Water and Climate Change, 2021, vol. 12, no. 1, pp. 238-249. https://doi.org/10.2166/wcc.2019.078

Congedo L. Semi-automatic classification plugin documentation. Release, 2016, vol. 4, no. 0.1. pp. 29. https://doi.org/10.13140/ RG.2.2.29474.02242/

Copernicus Open Access Hub. Available at:https://scihub.copernicus.eu/dhus/#/home (date of access: 10.11.2022).

Ali M.I., Dirawan G.D., Hasim A.H., Abidin M.R. Detection of Changes in Surface Water Bodies Urban Area with NDWI and MNDWI Methods. International Journal on Advanced Science, Engineering and Information Technology, 2019, vol. 9, no. 3, pp. 946-951.https://doi.org/10.18517/ijaseit.9.3.8692

Yang X., Qin Q., Yésou H. [et al.]. Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data. Remote Sens. Environ., 2020, vol. 244, 111803.https://doi.org/10.1016/j.rse.2020.111803

Sandoval S., Escobar-Flores J.G., Sanchez-Ortiz E. Water Resource Inventory in the Sierra Madre Occidental (Mexico) based on Remote Sensing and GIS. Invest. Geog., 2020, no. 102, e59975. https://doi.org/10.14350/rig.59975

Pena-Regueiro J., Sebastiá-Frasquet M.-T., Estornell J., Aguilar-Maldonado J. A. Sentinel2 application to the surface characterization of small water bodies in wetlands. Water, 2020, vol. 12, pp. 1487. https://doi.org/10.3390/w12051487

Wieland M., Martinis S. A. Modular Processing Chain for Automated Flood Monitoring from Multi-Spectral Satellite Data. Remote Sensing, 2019, vol. 11, no. 19, 2330. https://doi.org/10.3390/rs11192330


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