Abstract
The periodic assessment of the revised capacity is essential and conventional techniques through bathymetric surveys and inflow-outflow techniques are time and resource intensive. The application of optical remote sensing data is practiced for a long time to compute the revised capacity of the reservoir but has the limitation of selecting threshold values and inefficacy of the method during the cloudy season. This research proposed the application of the Otsu segmentation technique in Google Earth Engine (GEE) to determine revised water spread using microwave remote sensing data. The Otsu technique is efficient in classifying the image into two distinct classes using within and between-class variances. An application has been developed using Sentinel data in GEE, which has the capacity of cloud-based computing and applied to the four reservoirs of Mahanadi Reservoir Project (MRP) namely Ravishankar Sagar, Murumsilli, Dudhawa, and Sondur reservoir in the Chhattisgarh state of India. The original capacities of these reservoirs vary from 910.51 MCM of Ravishankar (RS) Sagar to 165.89 MCM of Murumsilli reservoir. The revised water spreads computed in the GEE were further used to compute revised volume and in turn the revised cumulative capacities at different levels. The analysis suggested that 17.65% of dead storage (118.26 MCM from 143.00 MCM) and 9.25% of gross storage (85.26 MCM from 910.52 MCM) of RS Sagar has been lost in 42 years (1978 to 2020). Similarly, 10.33% gross capacity of Murumsilli, 13.12% of Sondur, and 12.37% of Dudhawa reservoirs have been lost due to the deposit of sediments from the catchments, and results were found comparable with bathymetric survey results of three reservoirs. The proposed application has been developed in GEE so it can be used in any part of the world in cloudy weather with no human interference.
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Data Availability
The remote sensing data used for the study was taken from the GEE depository and freely available to all. The details of reservoirs and results of the bathymetry survey are collected and can be made available with the consent of WRD, Chhattisgarh.
References
Agnihotri AK, Ohri A, Das N, Gaur S, Mishra S (2019) Flood inundation mapping and monitoring using SAR data and its impact on Ramganga River in Ganga basin. Environ Monit Assess 191(12):1–16. https://doi.org/10.1007/s10661-019-7903-4
Amitrano D, Di Martino G, Iodice A, Riccio D, Ruello G (2018) Unsupervised rapid flood mapping using Sentinel-1 GRD SAR images. IEEE Trans Geosci Remote Sens 56(6):3290–3299. https://doi.org/10.1109/TGRS.2018.2797536
Andredaki M, Georgoulas A, Kotsovinos N, Hrissanthou V (2015) Assessment of reservoir sedimentation effect on coastal erosion and evaluation of sediment removal techniques for its reduction -The case of Nestos river, Greece. Hrissanthou V (Ed.). Greece. InTech. https://doi.org/10.5772/61459
Avinash G, Chandrmouli PN (2018) Assessment of reservoir sedimentation using RS and GIS techniques - a case study of Kabini reservoir, Karnataka, India. Int Res J Eng Technol 5(8):630–635
Bangare SL, Dubal A, Bangare PS (2015) Reviewing Otsu’s method for image thresholding. Int J Appl Eng Res 10(9):21777–21783
Bindu H (2009) An improved medical image segmentation algorithm using Otsu method. Int J Recent Trends Eng 2(3):88–90
Biswas B, Dey KN, Chakrabarti A (2015) Remote sensing image fusion using multi threshold Otsu method in Shearlet domain. Procedia Comp Sci 57:554–562. https://doi.org/10.1016/j.procs.2015.07.388
Bourgeau-Chavez LL, Kasischke ES, Frick AL, Mudd JP, Smith KB, Brunzell SM (2001) Analysis of space-borne SAR data for wetland mapping in Virginia riparian ecosystems. Int J Remote Sens 22:3665–3687. https://doi.org/10.1080/01431160010029174
Bui NB, Phan A, Nguyen TTN (2020) Land-cover mapping from Sentinel Time-Series Imagery on the Google Earth Engine: a case study for Hanoi, 2020 7th NAFOSTED Conf Infor Comp Sci (NICS). 140–145. https://doi.org/10.1109/NICS51282.2020.9335892
Campos JC, Sillero N, Brito JC (2012) Normalized difference water indexes have dissimilar performances in detecting seasonal and permanent water in the Sahara-Sahel transition zone. J Hydrol 464–465:438–446. https://doi.org/10.1016/j.jhydrol.2012.07.042
Cao Q, Qingge L, Yang P (2021) Performance analysis of Otsu-based thresholding algorithms: a comparative study. J Sensors 1–14. https://doi.org/10.1155/2021/4896853
Cheng T, Hong S, Huang B, Zhao B, Tan C, Qiu J (2021) Passive microwave remote sensing soil moisture data in agricultural drought monitoring: application in north-eastern China. Water. https://doi.org/10.3390/w13192777
Clemente JP, Fontanelli G, Lapini A, Santi E, Ovando GG, Roa YLB (2020) Google earth engine: application of algorithms for remote sensing of crops in Tuscany (Italy) IEEE Latin am GRSS & ISPRS remote sens conf (LAGIRS). 195–200. https://doi.org/10.1109/LAGIRS48042.2020.9165561
Coleman RW, Stavros N, Parazoo N, Yadav V (2020) A simplified framework for high-resolution urban vegetation classification with optical imagery in the Los Angeles Megacity. Rem Sens 12(15). https://doi.org/10.3390/RS12152399
Condeça J, Nascimento J, Barreiras N (2022) Monitoring the storage volume of water reservoirs using Google Earth Engine. Water Resour Res 58. https://doi.org/10.1029/2021WR030026
Dadoria D, Tiwari HL, Jaiswal RK (2017) Assessment of reservoir sedimentation in Chhattisgarh state using remote sensing and GIS. Int J Civil Engg Technol 8(4):526–534
Esphtein B, Ofek E, Wexler Y (2010) Detecting text in natural scenes with stroke width transform. Proc IEEE Conf Comp Vision Pattern Recognition 13–18:2963–2970. https://doi.org/10.1109/CVPR.2010.5540041. (San Francisco)
Fallah M, Kavian A, Omidvar E (2016) Watershed prioritization in order to implement soil and water conservation practices. Environ Earth Sci 75:1248. https://doi.org/10.1007/s12665-016-6035-1
Feng Y, Zhao H, Li H, Li X, Zhang X (2017) A multi-scale 3D Otsu thresholding algorithm for medical image segmentation. Digit Signal Proc 60:186–199. https://doi.org/10.1016/j.dsp.2016.08.003
Francis LM, Sreenath N (2020) TEDLESS —Text detection using least-square SVM from natural scene. J King Saudi Uni - Comp Inform Sci 32(3):287–299. https://doi.org/10.1016/j.jksuci.2017.09.001
Gao (1996) NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
Ghaffarian S, Farhadabad AR, Kerle N (2020) Post-disaster recovery monitoring with google earth engine. Appl Sci 10:4574. https://doi.org/10.3390/app10134574
Goel MK, Jain SK, Agrawal PK (2002) Assessment of sediment deposition rate in Bargi Reservoir using digital image processing. Hydrol Sci J 47(S):S81–S92. https://doi.org/10.1080/02626660209493024
Goldblatt R, Deininger K, Hanson G (2018) Utilizing publicly available satellite data for ban research: Mapping built-up land cover and land use in Ho Chi Minh City, Vietnam. Develop Eng 3:83–99. https://doi.org/10.1016/j.deveng.2018.03.001
Gorelick N, Hancher M, Thau D, Dixon M, Moore R, Ilyushchenko S (2017) Google earth engine: planetary-scale geospatial analysis for everyone. Rem Sens Environ 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Goumehei E, Tolpekin V, Stein A, Yan W (2019) Surface water body detection in polarimetric SAR data using contextual complex Wishart classification. Water Resour Res 55:7047–7059. https://doi.org/10.1029/2019WR025192
Haralick RM, Shapiro LG (1985) Image segmentation techniques. Comp Vis Graph Image Process 29(1):100–132
Huang C, Li X, Wen Y (2021) AN OTSU image segmentation based on the fruitfly optimization algorithm. Alexandria Eng J 60(1):183–188. https://doi.org/10.1016/j.aej.2020.06.054
Jain S, Jaiswal RK, Lohani AK, Galkate R (2021) Development of cloud-based rainfall–run-off model using google earth engine. Curr Sci 21(11):1433–1440. https://doi.org/10.18520/cs/v121/i11/1433-1440
Jain SK, Saraf AK, Goswami A, Ahmad T (2006) Flood inundation mapping using NOAA AVHRR data. Water Resour Manage 20(6):949–959. https://doi.org/10.1007/s11269-006-90164
Jaiswal RK, Nayak TR, Lohani AK, Jain SK (2016) Application of RS data for reservoir sediment profiling using latin hypercube-one at time (LH-OAT) technique. Int J Adv Agril Sci Technol 4(8):10–17
Jena M, Mishra SP, Mishra D (2018) A survey on applications of machine learning techniques for medical image segmentation. Int J Eng Technol 7(4):4489–4495. https://doi.org/10.14419/ijetv7i4.19005
Juntakut P, Jantakat Y, Jantakat C (2021) Google earth engine for monitoring drought impacts on urban tree using the standardized vegetation index (SVI) in Amphoe Mueang, Nakhonratchasima province, Thailand. Int J Build Urban Interior Landscape Technol (BUILT) 18:41–54
Karthika M, James A (2015) A novel approach for document image binarization using bit-plane slicing. Procedia Technol 19:758–765. https://doi.org/10.1016/j.protcy.2015.02.107
Khan AM, Ravi S (2013) Image segmentation method: a comparative study. Int J Soft Comp Eng 3(4):84–92
Kumar A, Tiwari A (2019) A comparative study of Otsu thresholding and K-means algorithm of image segmentation. Int J Eng Tech Res 9(5):12–14. https://doi.org/10.31873/IJETR.9.5.2019.62
Kumar H, Karwariya SK, Kumar R (2022) Google earth engine-based identification of flood extent and flood-affected paddy rice fields using Sentinel-2 MSI and Sentinel-1 SAR data in Bihar state, India. J Indian Soc Remote Sens 50:791–803. https://doi.org/10.1007/s12524-021-01487-3
Kumar L, Mutanga O (2018) Google earth engine applications since inception: usage, trends, and potential. Remote Sens 10:1509. https://doi.org/10.3390/rs10101509
Li J, Wang S (2015) An automatic method for mapping inland surface waterbodies with Radarsat-2 imagery. Int J Remote Sens 36(5):1367–1384. https://doi.org/10.1080/01431161.2015.1009653
Lin L, Yang W, Li C, Tang J, Cao X (2017) Inference with collaborative model for interactive tumor segmentation in medical image sequences. IEEE Trans Cybernet 46:2796–2809. https://doi.org/10.1109/tcyb.2015.2489719
Liu C, Huang H, Sun F (2021) A pixel-based vegetation greenness trend analysis over the russian tundra with all available landsat data from 1984 to 2018. Rem Sens 13:4933. https://doi.org/10.3390/rs13234933
Liu D, Yu J (2009) Otsu Method and K-means. Ninth int conf. Hybrid Intel Sys 344–349. https://doi.org/10.1109/HIS.2009.74
Liu S (2011) Image segmentation technology of the Otsu method for image materials based on binary PSO algorithm. Adv Comp Sci Intel Sys Environ 415–419. https://doi.org/10.1016/j.aej.2020.06.054
Martin EH, Jensen RR, Eustace A, Kisingo AW, Hardin PJ, Shoo RA (2019) Assessing changes in Tanzania’s Kwakuchinja Wildlife Corridor using multitemporal satellite imagery and open source tools. Appl Geogr 110. https://doi.org/10.1016/j.apgeog.2019.102051
Merina RN, Shashikumar MC, Rizwana N, Adlin R (2016) Sedimentation study in a reservoir using remote sensing technique. Appl Ecol Environ Res 14(4):296–304
Mustafa WA, Mydin M, Abdul Kader M (2017) Binarization of document images: a comprehensive review. 1st Int Conf Green Sustainable Computing (ICoGeS). https://doi.org/10.1088/1742-6596/1019/1/012023
Mutanga O, Kumar L (2019) Google earth engine applications. Rem Sens 11:591. https://doi.org/10.3390/rs11050591
Nghia BPQ, Pal I, Chollacoop N, Mukhopadhyay A (2022) Applying Google earth engine for flood mapping and monitoring in the downstream provinces of Mekong river. Progr Disaster Sci 14. https://doi.org/10.1016/j.pdisas.2022.100235
Nina O, Morse B, Barrett W (2011) A recursive Otsu thresholding method for scanned document binarization, 2011 IEEE Workshop Appl Comp Vision (WACV). 307–314. https://doi.org/10.1109/WACV.2011.5711519
Ninija Merina R, Shashikumar MC, Adlin R, Rizvana N (2016) Sedimentation study in a reservoir using remote sensing technique. Appl Eco Environ Res 14(4):264–304. https://doi.org/10.15666/aeer/1404_296304
Nyo MT, Mebarek-Oudina F, Hlaing SS et al (2022) Otsu’s thresholding technique for MRI image brain tumor segmentation. Multimed Tools Appl. https://doi.org/10.1007/s11042-022-13215-1
Otsu NA (1979) Threshold selection method from Gray-Level Histograms. IEEE Trans Syst Man Cybern 9:62–66. https://doi.org/10.1109/TSMC.1979.4310076
Pal NR, Pal SK (1993) A review on image segmentation techniques. Pattern recognit 26(9):1277–1294. https://doi.org/10.1016/0031-3203(93)90135-J
Pandey A, Chaube UC, Kumar D, Mishra SK (2016) Assessment of reservoir sedimentation using remote sensing and recommendations for desilting Patratu Reservoir, India. Hydrol Sci J 61(4):711–718. https://doi.org/10.1080/02626667.2014.993988
Pare S, Mittal H, Saxena A, Bansal JC, Prasad M, Sajid M, Jan T, Pedrycz W (2021) Remote sensing imagery segmentation: a hybrid approach. Rem Sens 13(22):4604. https://doi.org/10.3390/rs13224604
Parera D, Williams S, Smakhtin V (2023) Present and future losses of storage in large reservoirs due to sedimentation: a country-wise global assessment. Sustainability 15(1):219. https://doi.org/10.3390/su15010219
Pekel JF, Cottam A, Belward A, Gorelick N (2016) High-resolution mapping of global surface water and its long-term changes. Nature 540(7633):418–422. https://doi.org/10.1038/nature20584
Pham Duc B, Prignet C, Aires F (2017) Surface water monitoring within Cambodia and the vietnamese Mekong delta over a year with Sentinel-1 SAR observations. Water 9:366. https://doi.org/10.3390/w9060366
Rakhmatullaev S, Huneau F, Celle-Jeanton H, Motelica-Heino M, Bakiev M, Coustumer PL (2013) Water reservoirs, irrigation and sedimentation in central Asia: a first-cut assessment for Uzbekistan. Environ Earth Sci 68:985–998. https://doi.org/10.1007/s12665-012-1802-0
Ranade R, Garg A, Pandey K, Jain S (2015) Satellite image enhancement toolbox (SIET) – an open source image enhancement implementation. In: Open Source Geospatial Tools in Climate Change Research and Natural Resources Management (ed. OSGEO-India), 8–13
Rayner M, Balzter H, Stoate C, Jones L, Whelan M (2021) Effects of improved land-cover mapping on predicted ecosystem service outcomes in a lowland river catchment. Ecol Indic 33. https://doi.org/10.1016/j.ecolind.2021.108463
Rodrigues LN, Sano EE, Steenhuis TS, Passo DP (2011) Estimation of small reservoir storage capacities with remote sensing in the brazilian Savannah region. Water Resour Manage 26:873–882
Rogowska J (2009) Overview and fundamentals of medical image segmentation. In: Bankman I N (ed) Handbook of medical image processing and analysis (2nd edition) Acemedic Press, https://doi.org/10.1016/B978-012373904-9.50013-1
Sari T, Kefali A, Bahi H (2014) Text extraction from historical document images by the combination of several thresholding techniques. Adv Multimedia. https://doi.org/10.1155/2014/934656
Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance equation. J Electron Imaging 13(1):146–165. https://doi.org/10.1117/1.1631315
Senthilkumaran N, Vaithegi S (2016) Image segmentation by using thresholding techniques for medical images. Comp Sci Eng: An Int J (CSEIJ) 6(1):1–13. https://doi.org/10.5121/cseij.2016.6101
Shekhar S, Singh A, Agrawal SC (2015) An object centric image retrieval framework using multi-agent model for retrieving non-redundant web images. Int J Image Min 1:4–22. https://doi.org/10.1504/IJIM.2015.070018
Singh S, Yadav AK, Mishra AP (2021) Reservoir sedimentation assessment of Rihand reservoir using remote sensing technique. In: Pande CB, Moharir KN (eds) Groundwater resources development and planning in semi-arid region. Springer, Berlin. https://doi.org/10.1007/978-3-030-68124-1_23
Srinivas CVVS, Prasad MVRV, Sirisha M (2019) Remote sensing image segmentation using OTSU algorithm. Int J Comp Appl 178(12):46–50. https://doi.org/10.5120/ijca2019918885
Suresh K, Srinivasa Rao P (2019) Various image Segmentation Algorithms: a Survey. In: Satapathy S, Bhateja V, Das S (eds) Smart Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol 105. Springer, Singapore. https://doi.org/10.1007/978-981-13-1927-3_24
Tsolakidis I, Vafiadis M (2019) Comparison of hydrographic survey and satellite bathymetry in monitoring Kerkini Reservoir Storage. Environ Process 6:1031–1049
Tulbure M, Broich M (2013) Spatiotemporal dynamic of surface water bodies using landsat time- series data from 1999 to 2011. ISPRS J Photogramme Rem Sens 79(5):44–52. https://doi.org/10.1016/j.isprsjprs.2013.01.010
Vorosmarty CJ, Meybeck M, Fekete B, Syvitski JPM, Sharma K, Green P (2003) Anthropogenic sediment retention: major global impact from registered river impoundments. Glob Planet Change 39(1–2):169–190. https://doi.org/10.1016/S0921-8181(03)00023-7
Wahap NA, Shafri HZM (2020) Utilization of Google Earth Engine (GEE) for land cover monitoring over Klang Valley, Malaysia. IOP Conf Series Earth Environ Sci 540(1). https://doi.org/10.1088/1755-1315/540/1/012003
World Commission on Dams (WCD) (2000) Dams and development. A new framework for decision-making. Earthscan publications Ltd, London, p 356
Yang T, Wang Y (2021) Studying spatiotemporal fractional vegetation cover variations from 2000 to 2020 in Changjiang basin, China with google earth engine. IEEE Int Geosci Remote Sens Symp IGARSS 6743–6746. https://doi.org/10.1109/IGARSS47720.2021.9553983
Zhou S, Kan P, Silbernagel J, Jin J (2020) Application of image segmentation in surface water extraction of freshwater lakes using radar data. ISPRS Int J Geo-Inf 9(7):424. https://doi.org/10.3390/ijgi9070424
Zhu Q, Jing L, Bi R (2010) Exploration and improvement of Ostu threshold segmentation algorithm exploration and improvement of Ostu threshold segmentation algorithm and automation, 8th World Cong Intel Control Automation (WCICA). https://doi.org/10.1109/WCICA.2010.5554431
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We acknowledge Dr. Sudhir Kumar, Director, National Institute of Hydrology for his overall support during the study.
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The authors are thankful to the World Bank for funding the study under the National Hydrology Project of the Ministry of Jal Shakti, Govt. of India.
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Jaiswal, R.K., Dhruw, G., Jain, S. et al. Efficient Segmentation Algorithm for Estimation of Revised Reservoir Capacities in Google Earth Engine. Water Resour Manage 37, 3795–3812 (2023). https://doi.org/10.1007/s11269-023-03511-2
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DOI: https://doi.org/10.1007/s11269-023-03511-2