Reservoir Sedimentation Assessment of Rihand Reservoir Using Remote Sensing Technique

Singh, Smrati; Yadav, Rajeev Kumar; Mishra, Arun Pratap

doi:10.1007/978-3-030-68124-1_23

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…The estimate of the revised capacity of a reservoir, which in turn gives the rate of sedimentation, can be determined by the conventional techniques, viz., bathymetric survey and in ow-out ow technique or recent technique of digital image processing of multi-date satellite data in optical bands (Singh et al 2021). The ability to map and estimate water spread area from satellite data is well understood and the digital classi cation of water bodies has been used by many investigators (Work and Gilmer 1976; Thiruvengadachari et al 1980; Goel and Jain 1996;Goel et al 2002;Jain and Kothiyari 2000;Jaiswal et al 2009;Fallah et al 2016, etc.…”

Section: Introductionmentioning

confidence: 99%

Efficient Segmentation Algorithm for Estimation of Revised Reservoir Capacities in Google Earth Engine

Jaiswal

Dhruw

Jain

et al. 2023

Water Resour Manage

View full text Add to dashboard Cite

The periodic assessment of the revised capacity is essential and conventional techniques through bathymetric surveys and in ow-out ow techniques are time and resource intensive. The unsupervised classi cation of optical remote sensing data is practiced for a long time to reduce the cost of the survey but has the limitation of selecting threshold values and ine cacy 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 data. The Otsu technique is e cient in classifying the image into two distinct classes using within and between-class variances (Bangare et al 2015). 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 has 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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Section: Introductionmentioning

confidence: 99%

Efficient Segmentation Algorithm for Estimation of Revised Reservoir Capacities in Google Earth Engine

Jaiswal

Dhruw

Jain

et al. 2023

Water Resour Manage

View full text Add to dashboard Cite

show abstract

Quantification of Sedimentation of a Tropical Reservoir Through Satellite Altimetry: A Case Study of Maithon Reservoir

Ghosh,

Thakur,

Sharma

et al. 2024

Natural Resource Monitoring, Planning and Management Based on Advanced Programming

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Efficient segmentation algorithm for estimation of revised reservoir capacities in Google Earth Engine

Jaiswal

Dhruw

Jain

et al. 2023

Preprint

View full text Add to dashboard Cite

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 unsupervised classification of optical remote sensing data is practiced for a long time to reduce the cost of the survey 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 data. The Otsu technique is efficient in classifying the image into two distinct classes using within and between-class variances (Bangare et al 2015). 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 has 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.

show abstract

Reservoir Sedimentation Assessment of Rihand Reservoir Using Remote Sensing Technique

Cited by 3 publications

References 15 publications

Efficient Segmentation Algorithm for Estimation of Revised Reservoir Capacities in Google Earth Engine

Efficient Segmentation Algorithm for Estimation of Revised Reservoir Capacities in Google Earth Engine

Quantification of Sedimentation of a Tropical Reservoir Through Satellite Altimetry: A Case Study of Maithon Reservoir

Efficient segmentation algorithm for estimation of revised reservoir capacities in Google Earth Engine

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