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In addition to differences in collection methods, there are also differences in their spatial and temporal characteristics.
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Different sensors provide us with different resolution imageries that are aimed at detecting specific land types. Satellite imagery is one of the primary sources of information and analysis when it comes to land use and land cover. However, these products differ on the basis of the methodology used to create them and the classification systems used to generate the several land use partitions (Defries and Townsend, 1994 Fritz, See, & Rembold, 2010). The availability of regional and global land cover products provides us with a wide variety of options to utilize for our own respective research. Land use and land cover change (LULCC) modeling is an effective way to determine the current human footprint on the planet (Parker, 2002 Parker et al., 2003). The modification of the planet’s terrestrial surface on the local, national and international levels is one of the major anthropogenic factors that contribute to ecosystem change (Parker, Manson, Janssen, Hoffmann, & Deadman, 2003). Also, within the region of the CCR, a large protected area, we find forest cover is not affected by anthropogenic factors, but instead is driven by the monsoon cycles affecting Southeast Asia. We observe the change in Singapore’s landmass through land reclamation.
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As a use case for exploring GEE, we analyze Singapore’s land use and cover. The ongoing refinement of this system makes it promising for big-data analysts from diverse user groups. We examine the challenges that GEE faces, also common to most parallel-processing, big-data architectures. 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. 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.
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We assess its capacity to conduct space–time analysis over two subregions of Singapore, namely, Tuas and the Central Catchment Reserve (CCR), for Urban and Wetlands land classes. 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, Moderate Resolution Imaging Spectroradiometer and GlobCover (2009) imagery.
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