Using Multispectral Satellite Images to Estimate Alteration in the Water Surface Area of Lake Dankia During the 2020--2021 Dry Season, Lam Dong Province, Vietnam

Abstract

Vietnam in recent years, especially in the Central Highlands and the South Central region, is experiencing severe droughts due to global climate change, depletion of the surface water resources and intensive agricultural production. The study used four Sentinel 2 MSI satellite images received during the 2020--2021 dry seasons to evaluate alteration in the water surface area of Lake Dankia in the Lam Dong Province of the Vietnam Central Highlands. Optical green channel (channel 3) and shortwave infrared channel (channel 11) of the Sentinel 2 images were used to calculate the modified normalized difference water index MNDWI and to decipher the land--water boundary by the thresholding method. The obtained results demonstrated that the Lake Dankia area at the dry season end (March 18. 2021) decreased by 86.46 hectares compared to November 18. 2020 (dry season start), which was 31.7 % of the original lake area. This study shows that the Sentinel 2 MSI satellite images could be effectively used to monitor alterations in the surface water area and provide valuable input information for models to assess the drought impact on water resources in the areas

Please cite this article as:

Trinh L.H., Zablotskiy V.R., Zenkov I.V., et al. Using multispectral satellite images to estimate alteration in the water surface area of Lake Dankia during the 2020--2021 dry season, Lam Dong Province, Vietnam. Herald of the Bauman Moscow State Technical University, Series Instrument Engineering, 2023, no. 2 (143), pp. 111--123. DOI: https://doi.org/10.18698/0236-3933-2023-2-111-123

References

[1] Trinh L.H., Zablotskiy V., Dao K.H. A study of the long-term dynamics of soil moisture in the Bac Binh district (Binh Thuan province, Vietnam) using LANDSAT multispectral images. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, vol. 15, no. 7, pp. 89--101 (in Russ.). DOI: https://doi.org/10.21046/2070-7401-2018-15-7-89-101

[2] Trinh L.H., Vu D.T. Application of remote sensing technique for drought assessment based on normalized difference drought index, a case study of Bac Binh district, Binh Thuan province (Vietnam). Russ. J. Earth Sc., 2019, vol. 19, art. ES2003 (in Russ.).DOI: https://doi.org/10.2205/2018ES000647

[3] Ridd M.K., Liu J.A. A comparison of four algorithms for change detection in an urban environment. Remote Sens. Environ., 1998, vol. 63, no. 2, pp. 95--100. DOI: https://doi.org/10.1016/S0034-4257(97)00112-0

[4] Du Z., Linghu B., Ling F., et al. Estimating surface water area changes using time-series Landsat data in the Qingjiang river basin. J. Appl. Remote. Sens., 2012, vol. 6, no. 1, art. 063609. DOI: https://doi.org/10.1117/1.JRS.6.063609

[5] Zenkov I.V., Trinh L.H., Yuronen Yu.P., et al. Investigation of the transformation of the plant ecosystem in the vicinity of the Tsemesskaya Bay in the Black Sea based on remote sensing data. Ecology and Industry of Russia, 2021, vol. 25, no. 6, pp. 61--67 (in Russ.). DOI: https://doi.org/10.18412/1816-0395-2021-6-61-67

[6] Areffian A., Eslamian S., Sadr M., et al. Monitoring the effects of drought on vegetation cover and ground water using MODIS satellite images and ANN. KSCE J. Civ. Eng., 2021, vol. 25, no. 3, pp. 1095--1105. DOI: https://doi.org/10.1007/s12205-021-2062-x

[7] Bhaga T., Dube T., Shekede M., et al. Impacts of climate variability and drought on surface water resources in Sub-Saharan Africa using remote sensing: a review. Remote Sens., 2020, vol. 12, no. 24, art. 4184. DOI: https://doi.org/10.3390/rs12244184

[8] Thenkabail P.S., Gamage M.S., Smakhtin V.U. The use of remote sensing data for drought assessment and monitoring in southwest Asia. Research Report 85. IWMI, 2004.

[9] Foody G. Status of land cover classification accuracy assessment. Remote Sens. Environ., 2002, vol. 80, no. 1, pp. 185--201. DOI: https://doi.org/10.1016/S0034-4257(01)00295-4

[10] Li W., Du Z., Ling F., et al. A comparison of land surface water mapping using the normalized difference water index from TM, ETM+ and ALI. Remote Sens., 2013, vol. 5, no. 11, pp. 5530--5549. DOI: https://doi.org/10.3390/rs5115530

[11] Alesheikh A., Ghorbanali A., Nouri N. Coastline change detection using remote sensing. Int. J. Environ. Sc. Technol., 2007, vol. 4, no. 1, pp. 61--66. DOI: https://doi.org/10.1007/BF03325962

[12] Winarso G., Budhiman S. The potential application of remote sensing data for coastal study. Proc. 22nd Asian Conf. on Remote Sensing, 2001. Available at: https://a-a-r-s.org/proceeding/ACRS2001/Papers/CTZ-01.pdf (accessed: 15.02.2023).

[13] Trinh L.H., Le T.G., Kieu V.H., et al. Application of remote sensing technique for shoreline change detection in Ninh Binh and Nam Dinh provinces (Vietnam) during the period 1988 to 2018 based on water indices. Russ. J. Earth Sc., 2020, vol. 20, no. 2, art. ES2004 (in Russ.). DOI: https://doi.org/10.2205/2020ES000686

[14] Zhai K., Wu X., Qin Y., et al. Comparison of surface wuater extraction performances of different classic water indices using OLI and TM imageries in different situations. Geo Spat. Inf. Sc., 2015, vol. 18, no. 1, pp. 32--42. DOI: https://doi.org/10.1080/10095020.2015.1017911

[15] McFeeters S.K. The use of normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens., 1996, vol. 17, no. 7, pp. 1425--1432. DOI: https://doi.org/10.1080/01431169608948714

[16] Gao B.C. NDWI --- a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ., 1996, vol. 58, no. 3, pp. 257--266. DOI: https://doi.org/10.1016/S0034-4257(96)00067-3

[17] Xiao X., Boles S., Frolking S., et al. Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using vegetation sensor data. Int. J. Remote Sens., 2002, vol. 23, no. 15, pp. 3009--3022. DOI: https://doi.org/10.1080/01431160110107734

[18] Liu Y., Wang X., Ling F., et al. Analysis of coastline extraction from Landsat-8 OLI imagery. Water, 2017, vol. 9, no. 11, art. 816. DOI: https://doi.org/10.3390/w9110816

[19] Xu H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens., 2006, vol. 27, no. 14, pp. 3025--3033. DOI: https://doi.org/10.1080/01431160600589179

[20] Shen L., Li C. Water body extraction from Landsat ETM+ imagery using adaboost algorithm. Proc. 18th Int. Conf. on Geoinformatics, 2010. DOI: https://doi.org/10.1109/GEOINFORMATICS.2010.5567762

[21] Feyisa G., Meiby H., Fensholt R., et al. Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sens. Environ., 2014, vol. 140, pp. 23--35. DOI: https://doi.org/10.1016/j.rse.2013.08.029

[22] Kaab A., Winsvold S.H., Altena B., et al. Glacier remote sensing using Sentinel-2. Part I: Radiometric and geometric performance and application to ice velocity. Remote Sens., 2016, vol. 8, no. 7, art. 598. DOI: https://doi.org/10.3390/rs8070598

[23] Copernicus open access hub. scihub.copernicus.eu: website. Available at: https://scihub.copernicus.eu/dhus/#/home (accessed: 15.02.2023).

[24] Kelley G.W., Hobgood J.S., Bedford K.W., et al. Generation of three-dimensional lake model forecasts for Lake Erie. Weather and Forecasting, 1998, vol. 13, no. 3, pp. 659--687. DOI: https://doi.org/10.1175/1520-0434(1998)013%3C0659:GOTDLM%3E2.0.CO;2

[25] Liu D., Yu J. Otsu method and K-means. Proc. 9th HIS, 2009, pp. 344--349.DOI: https://doi.org/10.1109/HIS.2009.74