Analyzing the variations in the water surface area of Taleqan Dam of Iran using ground-based and satellite observations

Tayfehrostami, A.; Abedini, Abbas; Tajfirooz, Bahman

doi:10.1109/migars57353.2023.10064509

Cited by 2 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Water indices are commonly computed to improve the differentiation between water bodies and other objects. These indices are valued for their straightforward implementation and robust calculation capabilities, making them extensively utilized in dynamic analyses (Campos et al, 2012;Li & Roy, 2017;Tayfehrostami et al, 2023). The GEE-based monitoring in this study incorporates NDWI, thresholding, and post-processing techniques, including vegetation masking to eliminate nonwater fields and minimum connection pixels to reduce sparse noise.…”

Section: Water Surface Area From Sentinel-2mentioning

confidence: 99%

Using Optical Satellite Images and Satellite Altimetry Data to Estimate Volume Variations in Dams

Ghanbari,

Tayfehrostami,

Forouzanfar

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. This study focused on monitoring the water volume variations of the Doroudzan dam reservoir in Shiraz, Iran, using satellite observations. In particular, Sentinel-3 altimetry mission (SRAL) Level-1B and Level-2 data were employed to calculate water level changes, addressing the limitations in accuracy for inland and shallow waters. Re-tracking of returned waveforms was applied to improve the accuracy of Level-2 altimetry results. Additionally, Sentinel-2 optical images were utilized to monitor the water surface area of the dam reservoir. The results demonstrated that re-tracking the returned waveforms significantly improved the water level observations compared to Level-2 data. The analysis extended to comparing the time series of water surface area estimated from Sentinel-2 images with in-situ data, revealing a high accuracy of 5.39%. Combining optimum water level and surface area data in Heron's equation facilitated the calculation of water volume variations. A remarkable correlation of 95.27% was found when comparing the time series of estimated water volume variations and in-situ data. This study underscores the effectiveness of Copernicus satellites, particularly Sentinel-3 and Sentinel-2 missions, in monitoring inland water bodies and demonstrates the reliability of the techniques employed for tracking dam reservoir volume variations.

show abstract

Section: Water Surface Area From Sentinel-2mentioning

confidence: 99%

Using Optical Satellite Images and Satellite Altimetry Data to Estimate Volume Variations in Dams

Ghanbari,

Tayfehrostami,

Forouzanfar

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Information gathered from several sources utilizing the InSAR method of SAR, or synthetic aperture radar. Dam water [7] levels and surface areas are only two of the many hydrological metrics that may be tracked with the use of improved spatial-temporal resolution in the available remote sensing data.…”

Section: Literature Surveymentioning

confidence: 99%

Ground Water Level Prediction Using Machine Learning

2024

IRJMETS

View full text Add to dashboard Cite

Groundwater level data from three districts in Tamil Nadu are analyzed in this study. In every of these districts, we have analyzed data from over a hundred observation wells and created seasonal models that depict the behavior of the groundwater. One each for periodic, polynomials, and rainfall models were created. The rainfall model investigates the relationship between precipitation and water levels, in contrast to the periodic and polynomial approaches that record patterns in observation well water levels. All three models periodic, polynomial, and rainfall are built using data on water levels measured by observation wells. After reviewing the available data and analyzing it thoroughly, we have decided to use an artificial neural network (ANN) to detect water levels. This will allow us to make more accurate predictions, which will in turn help with local water conservation decisions and provide specific to the region input to governmental planning authorities.

show abstract

Analyzing the variations in the water surface area of Taleqan Dam of Iran using ground-based and satellite observations

Cited by 2 publications

References 34 publications

Using Optical Satellite Images and Satellite Altimetry Data to Estimate Volume Variations in Dams

Using Optical Satellite Images and Satellite Altimetry Data to Estimate Volume Variations in Dams

Ground Water Level Prediction Using Machine Learning

Contact Info

Product

Resources

About