Abstract
Abstract. Dynamics, distribution and quality of water has a direct impact on environment and its dependent human activities. Regular monitoring of these hydrological processes help in understanding water cycle and better management policy making. Recent increase in remote sensing satellites offer multiple observations with high spatial and temporal resolution, thus calling for extensive use of high end computational resources. Google Earth Engine(GEE) is an open Application Programing Interface (API), which offers free computational resources and satellite data on cloud computational platform minimising the users need for computational resources and data availability. Five year Landsat-8 imagery (2013–18) from GEE database has been used to study the surface water extent of large inland water bodies (surface area greater than 6000 ha) of India. We have used a pixel based classification system to delineate water and non-water pixels. A knowledge based Decision Tree (DT) model has been employed to cluster the classes according to Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI) distribution. We report an anomalous departure from the 5-year trend line suggesting that the maximum decrease of water extent was found in year 2015–2016. Analysis of the decay pattern of reservoirs can provide timely inputs for better policy making and management of water resources. To understand the decay pattern, a Modified Gaussian model fit on time series of surface extent helps to determine maximum water extent, peak extent day and storage cycle of the water body.
Highlights
Terrestrial water is a valuable natural resource for sustaining life and plays a major role in the global water cycle
We present a novel approach using the computational power of Google Earth Engine (GEE) and time series data to assess the spatiotemporal dynamics of large Indian Wetlands
A knowledge based Decision Tree (DT) model has been employed to cluster the classes according to Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI) distribution
Summary
Terrestrial water is a valuable natural resource for sustaining life and plays a major role in the global water cycle. Terrestrial waters include rivers, lakes, manmade reservoirs and wetlands. These water bodies undergo episodically change in inundated areas, its mapping is crucially important. Surface water extent and its storage is important to understand the water dynamics and the global water cycle. Changes in distribution and quality of water over large geospatial scale is best analysed by remote sensing observations. Satellite observations pave way for large spatial and temporal analysis of the surface water dynamics. Coupled with the hydrological model of the wetlands (Hammer et al 1986, Liang et al 1994), these observations can provide useful information in the form of spatial extent, drying pattern and bathymetry.
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