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Mapping surface-water area using time series landsat imagery on Google Earth Engine: a case study of Telangana, India


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1 ICAR-National Academy of Agricultural Research Management, Rajendra Nagar, Hyderabad 500 030, India
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The extent of surface-water spread influences the hydrogeology and ecology of waterbodies. Remote sensing technology provides spatial and temporal datasets which aid in mapping the dynamics of surface waterbodies at the regional and global scale. In the present study, temporal changes in the surface area of waterbodies in Telangana, India, were monitored using indices like normalized difference vegetation index, normalized difference water index and modified NDWI and machine learning algorithms like a random forest using Landsat-8 data. Google Earth Engine cloud computing platform was used for processing earth observation data, based on the time series images of Landsat and compared with real-time groundwater levels. The results showed a significant increase (P < 0.01) in both surface-water area and groundwater levels in Telangana, especially after 2015, which we hypothesize could be due to the specialized water conservation project being implemented by the Government of Telangana since 2015.

Keywords

Cloud computing platform, groundwater level, machine learning algorithms, remote sensing, surface area, waterbodies.
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  • Mapping surface-water area using time series landsat imagery on Google Earth Engine: a case study of Telangana, India

Abstract Views: 410  |  PDF Views: 186 PDF Views: 144

Authors

P. D. Sreekanth
ICAR-National Academy of Agricultural Research Management, Rajendra Nagar, Hyderabad 500 030, India
P. Krishnan
ICAR-National Academy of Agricultural Research Management, Rajendra Nagar, Hyderabad 500 030, India
N. H. Rao
ICAR-National Academy of Agricultural Research Management, Rajendra Nagar, Hyderabad 500 030, India
S. K. Soam
ICAR-National Academy of Agricultural Research Management, Rajendra Nagar, Hyderabad 500 030, India
Ch. Srinivasarao
ICAR-National Academy of Agricultural Research Management, Rajendra Nagar, Hyderabad 500 030, India

Abstract


The extent of surface-water spread influences the hydrogeology and ecology of waterbodies. Remote sensing technology provides spatial and temporal datasets which aid in mapping the dynamics of surface waterbodies at the regional and global scale. In the present study, temporal changes in the surface area of waterbodies in Telangana, India, were monitored using indices like normalized difference vegetation index, normalized difference water index and modified NDWI and machine learning algorithms like a random forest using Landsat-8 data. Google Earth Engine cloud computing platform was used for processing earth observation data, based on the time series images of Landsat and compared with real-time groundwater levels. The results showed a significant increase (P < 0.01) in both surface-water area and groundwater levels in Telangana, especially after 2015, which we hypothesize could be due to the specialized water conservation project being implemented by the Government of Telangana since 2015.

Keywords


Cloud computing platform, groundwater level, machine learning algorithms, remote sensing, surface area, waterbodies.

References





DOI: https://doi.org/10.18520/cs%2Fv120%2Fi9%2F1491-1499