@Article{LoboSouNovCarBar:2018:MaMiAr,
               author = "Lobo, Felipe de Lucia and Souza-Filho, Pedro Walfir Martins and 
                         Novo, Evlyn M{\'a}rcia Le{\~a}o de Moraes and Carlos, Felipe 
                         Menino and Barbosa, Cl{\'a}udio Clemente Faria",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Tecnol{\'o}gico Vale (ITV)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Mapping mining areas in the Brazilian amazon using MSI/Sentinel-2 
                         imagery (2017)",
              journal = "Remote Sensing",
                 year = "2018",
               volume = "10",
               number = "8",
                pages = "e1178",
                month = "Aug.",
             keywords = "small-scale mining, industrial mining, google engine, image 
                         classification, land-use cover change.",
             abstract = "Although mining plays an important role for the economy of the 
                         Amazon, little is known about its attributes such as area, type, 
                         scale, and current status as well as socio/environmental impacts. 
                         Therefore, we first propose a low time-consuming and high 
                         detection accuracy method for mapping the current mining areas 
                         within 13 regions of the Brazilian Amazon using Sentinel-2 images. 
                         Then, integrating the maps in a GIS (Geography Information System) 
                         environment, mining attributes for each region were further 
                         assessed with the aid of the DNPM (National Department for Mineral 
                         Production) database. Detection of the mining area was conducted 
                         in five main steps. (a) MSI (MultiSpectral Instrument)/Sentinel-2A 
                         (S2A) image selection; (b) definition of land-use classes and 
                         training samples; (c) supervised classification; (d) vector 
                         editing for quality control; and (e) validation with 
                         high-resolution RapidEye images (Kappa = 0.70). Mining areas 
                         derived from validated S2A classification totals 1084.7 km2 in the 
                         regions analyzed. Small-scale mining comprises up to 64% of total 
                         mining area detected comprises mostly gold (617.8 km2 ), followed 
                         by tin mining (73.0 km2 ). The remaining 36% is comprised by 
                         industrial mining such as iron (47.8), copper (55.5) and manganese 
                         (8.9 km2 ) in Caraj{\'a}s, bauxite in Trombetas (78.4) and Rio 
                         Capim (48.5 km2 ). Given recent events of mining impacts, the 
                         large extension of mining areas detected raises a concern 
                         regarding its socio-environmental impacts for the Amazonian 
                         ecosystems and for local communities.",
                  doi = "10.3390/rs10081178",
                  url = "http://dx.doi.org/10.3390/rs10081178",
                 issn = "2072-4292",
             language = "en",
        urlaccessdate = "01 maio 2024"
}