@Article{WagnerDaCaStPhGlAr:2020:ReMaSp,
               author = "Wagner, Fabien Hubert and Dalagnol da Silva, Ricardo and Casapia, 
                         Ximena Tagle and Streher, Annia Susin and Phillips, Oliver L. and 
                         Gloor, Emanuel and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto de 
                         Investigaciones de la Amazon{\'{\i}}a Peruana (IIAP)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {University 
                         of Leeds} and {University of Leeds} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Regional mapping and spatial distribution analysis of Canopy palms 
                         in an Amazon forest using deep learning and VHR images",
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
               number = "14",
                pages = "e2225",
                month = "July",
             keywords = "U-net, Semantic segmentation, deep learning, species distribution, 
                         very high resolution images.",
             abstract = "Mapping plant species at the regional scale to provide information 
                         for ecologists and forest managers is a challenge for the remote 
                         sensing community. Here, we use a deep learning algorithm called 
                         U-net and very high-resolution multispectral images (0.5 m) from 
                         GeoEye satellite to identify, segment and map canopy palms over 
                         \∼3000 km2 of Amazonian forest. The map was used to analyse 
                         the spatial distribution of canopy palm trees and its relation to 
                         human disturbance and edaphic conditions. The overall accuracy of 
                         the map was 95.5% and the F1-score was 0.7. Canopy palm trees 
                         covered 6.4% of the forest canopy and were distributed in more 
                         than two million patches that can represent one or more 
                         individuals. The density of canopy palms is affected by human 
                         disturbance. The post-disturbance density in secondary forests 
                         seems to be related to the type of disturbance, being higher in 
                         abandoned pasture areas and lower in forests that have been cut 
                         once and abandoned. Additionally, analysis of palm trees 
                         distribution shows that their abundance is controlled naturally by 
                         local soil water content, avoiding both flooded and waterlogged 
                         areas near rivers and dry areas on the top of the hills. They show 
                         two preferential habitats, in the low elevation above the large 
                         rivers, and in the slope directly below the hill tops. Overall, 
                         their distribution over the region indicates a relatively pristine 
                         landscape, albeit within a forest that is critically endangered 
                         because of its location between two deforestation fronts and 
                         because of illegal cutting. New tree species distribution data, 
                         such as the map of all adult canopy palms produced in this work, 
                         are urgently needed to support Amazon species inventory and to 
                         understand their distribution and diversity.",
                  doi = "10.3390/rs12142225",
                  url = "http://dx.doi.org/10.3390/rs12142225",
                 issn = "2072-4292",
             language = "en",
           targetfile = "Wagner_remotesensing-12-02225-v2.pdf",
        urlaccessdate = "25 abr. 2024"
}