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Water spread mapping of multiple lakes using remote sensing and satellite data


The situated lakes in Uttarakhand India attract every year lakhs of tourists for their picturesque features. The water from these lakes is also used for irrigation and domestic purpose by living near this lake. Due to overexploitation of water from these lakes, the water spread area needs to be monitored. In this study, mapping of water spread areas of Bhimtal, Sattal, and Naukuchiatal lakes, situated in Nainital District, has been done from 2001 to 2018. Landsat-8 OLI and Landsat-7 ETM satellite imagery has been used. To calculate the lakes’ water spread area, each study year has been divided into three periods: October to February, March to June, and July to October. Water spread areas have been calculated based on the band rationing indices, namely Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Water Ratio Index (WRI), and Normalized Difference Vegetation Index (NDVI). Furthermore, based on a physical GPS survey of the lake, the best-suited band rationing technique has been adopted to estimate the lake’s water spread area from 2001 to 2018. Based on this GPS survey, WRI has been the most accurate water index in the present study. The Mann-Kendall test has been done to know the variation of this lake’s water spread areas. Sen’s slope estimator test has been used to calculate the magnitude of the trend. The results suggested a significant decreasing trend in the water surface area of Sattal Lake for the period November to February (−0.00087 km2/year) and of Naukuchiatal Lake for the period March to June place (−0.00056 km2/year).

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  1. Acharya TD, Subedi A, Huang H, Lee DH (2019a) Application of water indices in surface water change detection using Landsat imagery in Nepal. Sensors Mater 31:1429–1447.

  2. Acharya TD, Subedi A, Huang H, Lee DH (2019b) Application of water indices in surface water change detection using Landsat imagery in Nepal. Sens Mater 31:1429–1447

    Google Scholar 

  3. Acharya TD, Subedi A, Lee DH (2018a) Evaluation of water indices for surface water extraction in a Landsat 8 Scene of Nepal. Sensors 18:2580

  4. Acharya TD, Subedi A, Lee DH (2018b) Evaluation of water indices for surface water extraction in a Landsat 8 Scene of Nepal. Sensors 18:2580

  5. Acharya TD, Yang IT, Subedi A, Lee DH (2017a) Change detection of lakes in Pokhara, Nepal Using Landsat Data. Proceedings 1:17

  6. Acharya TD, Yang IT, Subedi A, Lee DH (2017b) Change detection of lakes in Pokhara, Nepal Using Landsat Data. Proceedings 1:17

  7. Ali R, Kuriqi A, Abubaker S, Kisi O (2019) Long-term trends and seasonality detection of the observed flow in Yangtze River using Mann-Kendall and Sen’s Innovative Trend Method. Water 11:1855

  8. Asmadin SVP, Sofian I, Jaya I, Wijanarto AB (2018) Feature extraction of coastal surface inundation via water index algorithms using multispectral satellite on North Jakarta. IOP Conference Series: Earth Env Sci 176:012032.

    Article  Google Scholar 

  9. Bhatti SS, Tripathi NK (2014) Built-up area extraction using Landsat 8 OLI imagery. Gisci Remote Sens 51:445–467.

    Article  Google Scholar 

  10. Chakouri M, Lhissou R, El Harti A, Maimouni S, Adiri Z (2020) Assessment of the image-based atmospheric correction of multispectral satellite images for geological mapping in arid and semi-arid regions. Remote Sens App: Soc Environ 20:100420

  11. Chen F, Chen X, Van de Voorde T, Roberts D, Jiang H, Xu W (2020) Open water detection in urban environments using high spatial resolution remote sensing imagery. Remote Sens Environ 242:111706.

    Article  Google Scholar 

  12. Cogato A, Pezzuolo A, Sozzi M, Marinello F (2020) A sample of Italian vineyards: Landscape and management parameters dataset. Data in Brief 33:106589.

    Article  Google Scholar 

  13. Deng Y, Jiang W, Tang Z, Li J, Lv J, Chen Z, Jia K (2017) Spatio-temporal change of lake water extent in Wuhan Urban Agglomeration based on Landsat images from 1987 to 2015. Remote Sens 9:270

  14. Du Z et al (2012) Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China J Appl Remote Sens 6:063609.

    Article  Google Scholar 

  15. Elsahabi M, Negm A, Hamid MH, El Tahan A (2016) Performances evaluation of surface water areas extraction techniques using Landsat ETM+ Data: case study Aswan High Dam Lake (AHDL). Proc Tech 22:1205–1212.

    Article  Google Scholar 

  16. Gautam VK, Gaurav PK, Murugan P, Annadurai M (2015) Assessment of Surface Water Dynamicsin Bangalore Using WRI, NDWI, MNDWI, Supervised Classification and K-T Transformation. Aquat Pr 4:739–746.

    Article  Google Scholar 

  17. Gu Y, Brown JF, Verdin JP, Wardlow B (2007) A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophys Res Lett 34.

  18. Guo Q, Pu R, Li J, Cheng J (2017) A weighted normalized difference water index for water extraction using Landsat imagery. Int J Remote Sens 38:5430–5445.

    Article  Google Scholar 

  19. Hamed KH (2008) Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis. J Hydrol 349:350–363.

    Article  Google Scholar 

  20. Hui F, Xu B, Huang H, Yu Q, Gong P (2008) Modelling spatial-temporal change of Poyang Lake using multitemporal Landsat imagery. Int J Remote Sens 29:5767–5784.

    Article  Google Scholar 

  21. Isiacik Colak T, Senel G, Goksel C (2019) Coastline zone extraction using landsat-8 oli imagery, case study: bodrum peninsula, turkey. Int Arch Photogramm Remote Sens Spatial Inf Sci XLII-4/W12:101-104

  22. Jiang H, Feng M, Zhu Y, Lu N, Huang J, Xiao T (2014) An Automated Method for Extracting Rivers and Lakes from Landsat Imagery. Remote Sens. 6:5067-5089

  23. Kuriqi A et al (2020) Seasonality shift and streamflow flow variability trends in central India. Acta Geophys. 68:1461–1475.

    Article  Google Scholar 

  24. Li J, Peng B, Wei Y, Ye H (2021) Accurate extraction of surface water in complex environment based on Google Earth Engine and Sentinel-2. PLOS ONE 16:e0253209.

    Article  Google Scholar 

  25. Li L, Xia H, Li Z, Zhang Z (2015) Temporal-Spatial Evolution Analysis of Lake Size-Distribution in the Middle and Lower Yangtze River Basin Using Landsat Imagery Data. Remote Sens 7:10364-10384

  26. Li X, Liu X, Liu L, Xue K (2013) Comparative Study of Water-Body Information Extraction Methods Based on Electronic Sensing Image. In: Jin D, Lin S (eds) Advances in Mechanical and Electronic Engineering, Berlin, Heidelberg, 2013// 2013. Springer Berlin Heidelberg, pp 331-336

  27. Malahlela OE (2016) Inland waterbody mapping: towards improving discrimination and extraction of inland surface water features. Int J Remote Sens 37:4574–4589.

    Article  Google Scholar 

  28. Masser I (2001) Managing our urban future: the role of remote sensing and geographic information systems. Habitat Int 25:503–512.

    Article  Google Scholar 

  29. McFeeters SK (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int J Remote Sens 17:1425–1432.

    Article  Google Scholar 

  30. Mishra K, Prasad PRC (2015) Automatic Extraction of Water Bodies from Landsat Imagery Using Perceptron Model. J Comput Envi Scien 2015:903465.

    Article  Google Scholar 

  31. Mueller N et al (2016) Water observations from space: Mapping surface water from 25years of Landsat imagery across Australia. Remote Sens Environ 174:341–352.

    Article  Google Scholar 

  32. Mukherjee NR, Samuel C (2016) Assessment of the temporal variations of surface water bodies in and around Chennai using Landsat imagery. In. J. of Scie. and Tech. 9:1–7

    Google Scholar 

  33. Nielsen A, Bolding K, Hu F, Trolle D (2017) An open source QGIS-based workflow for model application and experimentation with aquatic ecosystems. Environ. Modell. Softw. 95:358–364.

    Article  Google Scholar 

  34. Partal T, Kahya E (2006) Trend analysis in Turkish precipitation data. Hydrol. Process.: An International Journal 20:2011-2026 10.1002/hyp.5993

  35. Patel K, Jain R, Patel AN, Kalubarme MH (2021) Shoreline change monitoring for coastal zone management using multi-temporal Landsat data in Mahi River estuary, Gujarat State. App. Geo. 10.1007/s12518-021-00353-8

  36. Peng J, Pan Y, Liu Y, Zhao H, Wang Y (2018) Linking ecological degradation risk to identify ecological security patterns in a rapidly urbanizing landscape. Habitat Int. 71:110–124.

    Article  Google Scholar 

  37. Reddy SLK, Rao CV, Kumar PR, Anjaneyulu RVG, Krishna BG (2018) A novel method for water and water canal extraction from landsat-8 oli imagery. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLII-5:323–328.

    Article  Google Scholar 

  38. Rokni K, Ahmad A, Selamat A, Hazini S (2014) Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery. Remote Sens. 6:4173-4189

  39. Sarp G, Ozcelik M (2017) Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey. J. of Taibah Uni. for Sci. 11:381–391.

    Article  Google Scholar 

  40. Subramaniam S, Babu AVS, Roy PS (2011) Automated Water Spread Mapping Using ResourceSat-1 AWiFS Data for Water Bodies Information System. IEEE J. Sel. Top. Appl. 4:205–215.

    Article  Google Scholar 

  41. Tucker CJ et al (2005) An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26:4485–4498.

    Article  Google Scholar 

  42. Wang S, Baig MHA, Zhang L, Jiang H, Ji Y, Zhao H, Tian J (2015) A Simple Enhanced Water Index (EWI) for Percent Surface Water Estimation Using Landsat Data. IEEE J. Sel. Top. Appl. 8:90–97.

    Article  Google Scholar 

  43. Wang X, Liu Y, Ling F, Liu Y, Fang F (2017) Spatio-Temporal Change Detection of Ningbo Coastline Using Landsat Time-Series Images during 1976–2015. ISPRS Int. Geo-Inf. 6:68

  44. Yang J, Du X (2017) An enhanced water index in extracting water bodies from Landsat TM imagery. Annals of GIS 23:141–148.

    Article  Google Scholar 

  45. Yu L, Wang Z, Tian S, Ye F, Ding J, Kong J (2017) Convolutional Neural Networks for Water Body Extraction from Landsat Imagery. Int. J. of Comp. Intell. and Appl. 16:1750001

  46. Zhang F, Li J, Zhang B, Shen Q, Ye H, Wang S, Lu Z (2018) A simple automated dynamic threshold extraction method for the classification of large water bodies from landsat-8 OLI water index images. Int. J. Remote Sens. 39:3429–3451.

    Article  Google Scholar 

  47. M-m Z, Chen F, B-s T (2018) An automated method for glacial lake mapping in High Mountain Asia using Landsat 8 imagery. J. Mt. Sci. 15:13–24.

    Article  Google Scholar 

  48. Zhu L, Meng J, Zhu L (2020) Applying Geodetector to disentangle the contributions of natural and anthropogenic factors to NDVI variations in the middle reaches of the Heihe River Basin. Ecol. Indic. 117:106545.

    Article  Google Scholar 

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Alban Kuriqi acknowledges the Portuguese Foundation for Science and Technology (FCT) support through PTDC/CTA-OHR/30561/2017 (WinTherface) project.

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Correspondence to Alban Kuriqi.

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Responsible Editor: Biswajeet Pradhan

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Deoli, V., Kumar, D., Kumar, M. et al. Water spread mapping of multiple lakes using remote sensing and satellite data. Arab J Geosci 14, 2213 (2021).

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  • Satellite imagery
  • Trend
  • Climate change
  • Water storage
  • Water ratio index