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@Article{BarbozaCastilloTASRSBOE:2020:MoWiNo,
               author = "Barboza Castillo, Elgar and Turpo Cayo, Efrain Yury and Almeida, 
                         Cl{\'a}udia Maria de and Salas L{\'o}pez, Rolando and Rojas 
                         Briceņo, Nilton Beltr{\'a}n and Silva L{\'o}pez, Jhonsy Omar and 
                         Barrena Gurbill{\'o}n, Miguel {\'A}ngel and Oliva, Manuel and 
                         Espinoza-Villar, Raul",
          affiliation = "{Universidad Nacional Toribio Rodr{\'{\i}}guez de Mendoza de 
                         Amazonas (UNTRM)} and {Universidad Nacional Agraria La Molina} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidad Nacional Toribio Rodr{\'{\i}}guez de Mendoza de 
                         Amazonas (UNTRM)} and {Universidad Nacional Toribio 
                         Rodr{\'{\i}}guez de Mendoza de Amazonas (UNTRM)} and 
                         {Universidad Nacional Toribio Rodr{\'{\i}}guez de Mendoza de 
                         Amazonas (UNTRM)} and {Universidad Nacional Toribio 
                         Rodr{\'{\i}}guez de Mendoza de Amazonas (UNTRM)} and 
                         {Universidad Nacional Toribio Rodr{\'{\i}}guez de Mendoza de 
                         Amazonas (UNTRM)} and {Universidad Nacional Agraria La Molina}",
                title = "Monitoring wildfires in the northeastern peruvian amazon using 
                         landsat-8 and sentinel-2 imagery in the GEE platform",
              journal = "ISPRS International Journal of Geo-Information",
                 year = "2020",
               volume = "9",
               number = "10",
                pages = "e564",
                month = "Oct.",
             keywords = "remote sensing, GIS, spectral analysis, burn severity, forests, 
                         vegetation cover, biodiversity.",
             abstract = "During the latest decades, the Amazon has experienced a great loss 
                         of vegetation cover, in many cases as a direct consequence of 
                         wildfires, which became a problem at local, national, and global 
                         scales, leading to economic, social, and environmental impacts. 
                         Hence, this study is committed to developing a routine for 
                         monitoring fires in the vegetation cover relying on recent 
                         multitemporal data (20172019) of Landsat-8 and Sentinel-2 imagery 
                         using the cloud-based Google Earth Engine (GEE) platform. In order 
                         to assess the burnt areas (BA), spectral indices were employed, 
                         such as the Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 
                         (NBR2), and Mid-Infrared Burn Index (MIRBI). All these indices 
                         were applied for BA assessment according to appropriate 
                         thresholds. Additionally, to reduce confusion between burnt areas 
                         and other land cover classes, further indices were used, like 
                         those considering the temporal differences between pre and 
                         post-fire conditions: differential Mid-Infrared Burn Index 
                         (dMIRBI), differential Normalized Burn Ratio (dNBR), differential 
                         Normalized Burn Ratio 2 (dNBR2), and differential Near-Infrared 
                         (dNIR). The calculated BA by Sentinel-2 was larger during the 
                         three-year investigation span (16.55, 78.50, and 67.19 km2 ) and 
                         of greater detail (detected small areas) than the BA extracted by 
                         Landsat-8 (16.39, 6.24, and 32.93 km2 ). The routine for 
                         monitoring wildfires presented in this work is based on a sequence 
                         of decision rules. This enables the detection and monitoring of 
                         burnt vegetation cover and has been originally applied to an 
                         experiment in the northeastern Peruvian Amazon. The results 
                         obtained by the two satellites imagery are compared in terms of 
                         accuracy metrics and level of detail (size of BA patches). The 
                         accuracy for Landsat-8 and Sentinel-2 in 2017, 2018, and 2019 
                         varied from 82.791.4% to 94.598.5%, respectively.",
                  doi = "10.3390/ijgi9100564",
                  url = "http://dx.doi.org/10.3390/ijgi9100564",
                 issn = "2220-9964",
                label = "self-archiving-INPE-MCTIC-GOV-BR",
             language = "en",
           targetfile = "castillo-monitoring.pdf",
        urlaccessdate = "13 abr. 2021"
}


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