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@Article{SidhuPebeCama:2018:SiUsCa,
               author = "Sidhu, Nanki and Pebesma, Edzer and Camara, Gilberto",
          affiliation = "{Westfaelische-Wilhelms Universitaet} and {Westfaelische-Wilhelms 
                         Universitaet} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Using Google Earth engine to detect land cover change: Singapore 
                         as a use case",
              journal = "European Journal of Remote Sensing",
                 year = "2018",
               volume = "51",
               number = "1",
                pages = "486--500",
             keywords = "Google Earth Engine, big-data architecture, land cover, urban 
                         areas, time series analysis.",
             abstract = "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. 
                         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. 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. We examine the challenges that GEE faces, also common to 
                         most parallel-processing, big-data architectures. The ongoing 
                         refinement of this system makes it promising for big-data analysts 
                         from diverse user groups. As a use case for exploring GEE, we 
                         analyze Singapore's land use and cover. We observe the change in 
                         Singapore's landmass through land reclamation. 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.",
                  doi = "10.1080/22797254.2018.1451782",
                  url = "http://dx.doi.org/10.1080/22797254.2018.1451782",
                 issn = "2279-7254",
             language = "en",
           targetfile = "sidhu_using.pdf",
        urlaccessdate = "25 nov. 2020"
}


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