Dynamics of surface water is of great significance to understand the impacts of global changes and human activities on water resources. Remote sensing provides many advantages in monitoring surface water; however, in large scale, the efficiency of traditional remote sensing methods is extremely low because these methods consume a high amount of manpower, storage, and computing resources. The data that are available in Google Earth Engine are available in several different multi-band assets: one for the mapped products, one for the water history and one for the metadata datasets. The datasets that are available are intended to show different facets of the spatial and temporal distribution of surface water over the last 30 year from 1984 to 2014. This paper investigates the web-based remote sensing platform, Google Earth Engine (GEE) and evaluates the platform's utility for performing raster and vector manipulations on Landsat and GLCF: Landsat Tree Cover Continuous Fields imagery. We assess its capacity to conduct space–time analysis over Indian State of Maharashtra to thereby detect the change in Tree Canopy Cover over three epochs. In its current state, GEE has proven to be a powerful tool by providing access to a wide variety of imagery in one consolidated system. Furthermore, it possesses the ability to perform spatial aggregations over global-scale data at a high computational speed though; supporting both spatial and temporal analysis is not an obvious task for the platform.
Volume 11 | 08-Special Issue
Pages: 3053-3063