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@Article{FengLuMoDuCaOl:2017:ExSpDi,
               author = "Feng, Yunyun and Lu, Dengsheng and Moran, Emilio F. and Dutra, 
                         Luciano Vieira and Calvi, Miqu{\'e}ias Freitas and Oliveira, 
                         Maria Antonia Falc{\~a}o de",
          affiliation = "{Zhejiang Agriculture and Forestry University} and {Zhejiang 
                         Agriculture and Forestry University} and {Michigan State 
                         University} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Universidade Federal do Par{\'a} (UFPA)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Examining spatial distribution and dynamic change of urban Llnd 
                         covers in the Brazilian Amazon using multitemporal multisensor 
                         high spatial resolution satellite imagery",
              journal = "Remote Sensing",
                 year = "2017",
               volume = "9",
               number = "4",
                month = "Apr.",
             keywords = "urban land-cover change, high spatial resolution satellite images, 
                         multisensor data, change detection technique, moist tropical 
                         region, Belo Monte hydroelectric dam construction.",
             abstract = "The construction of the Belo Monte hydroelectric dam began in 
                         2011, resulting in rapidly increased population from less than 
                         80,000 persons before 2010 to more than 150,000 persons in 2012 in 
                         Altamira, Para State, Brazil. This rapid urbanization has produced 
                         many problems in urban planning and management, as well as 
                         challenging environmental conditions, requiring monitoring of 
                         urban land-cover change at high temporal and spatial resolutions. 
                         However, the frequent cloud cover in the moist tropical region is 
                         a big problem, impeding the acquisition of cloud-free optical 
                         sensor data. Thanks to the availability of different kinds of high 
                         spatial resolution satellite images in recent decades, RapidEye 
                         imagery in 2011 and 2012, Pleiades imagery in 2013 and 2014, SPOT 
                         6 imagery in 2015, and CBERS imagery in 2016 with spatial 
                         resolutions from 0.5 m to 10 m were collected for this research. 
                         Because of the difference in spectral and spatial resolutions 
                         among these satellite images, directly conducting urban land-cover 
                         change using conventional change detection techniques, such as 
                         image differencing and principal component analysis, was not 
                         feasible. Therefore, a hybrid approach was proposed based on 
                         integration of spectral and spatial features to classify the high 
                         spatial resolution satellite images into six land-cover classes: 
                         impervious surface area (ISA), bare soil, building demolition, 
                         water, pasture, and forest/plantation. A post-classification 
                         comparison approach was then used to detect urban land-cover 
                         change annually for the periods between 2011 and 2016. The focus 
                         was on the analysis of ISA expansion, the dynamic change between 
                         pasture and bare soil, and the changes in forest/plantation. This 
                         study indicates that the hybrid approach can effectively extract 
                         six land-cover types with overall accuracy of over 90%. ISA 
                         increased continuously through conversion from pasture and bare 
                         soil. The Belo Monte dam construction resulted in building 
                         demolition in 2015 in low-lying areas along the rivers and an 
                         increase in water bodies in 2016. Because of the dam construction, 
                         forest/plantation and pasture decreased much faster, while ISA and 
                         water increased much faster in 2011-2016 than they had between 
                         1991 and 2011. About 50% of the increased annual deforestation 
                         area can be attributed to the dam construction between 2011 and 
                         2016. The spatial patterns of annual urban land-cover distribution 
                         and rates of dynamic change provided important data sources for 
                         making better decisions for urban management and planning in this 
                         city and others experiencing such explosive demographic change.",
                  doi = "10.3390/rs9040381",
                  url = "http://dx.doi.org/10.3390/rs9040381",
                 issn = "2072-4292",
             language = "en",
           targetfile = "feng_examining.pdf",
        urlaccessdate = "04 dez. 2020"
}


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