@InProceedings{WagnerDaStPhGlAr:2019:AnReDi,
               author = "Wagner, Fabien Hubert and Dalagnol, Ricardo and Streher, Annia 
                         Susin and Phillips, Oliver L. and Gloor, Emanuel Ulrich 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 {Universidade Estadual 
                         Paulista (UNESP)} and {University of Leeds} and {University of 
                         Leeds} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Analysing the Regional Distribution of a Key Canopy Palm Species 
                         Using a Convolutional Network in an Amazon Forest",
                 year = "2019",
         organization = "AGU Fall Meeting",
             abstract = "Mapping plant species at landscape scale to provide information 
                         for ecologists and forest managers is a new challenge for the 
                         remote sensing community. Here, we use a deep learning algorithm 
                         associated with very high-resolution multispectral images (0.5 m) 
                         from GeoEye satellite to identify and segment a palm tree species, 
                         Attalea speciosa, in the canopy of an Amazon forest. This study 
                         was conducted in a region of the critically endangered Brazilian 
                         Amazon Rainforest, between two deforestation fronts, which is a 
                         global conservation priority due to its abundance of species of 
                         flora and fauna and its carbon stock. The convolutional network 
                         generated in this study for identifying palm trees was trained 
                         with about 1024 high-resolution true colour optical images and 
                         their labelled masks. Additionally, we analysed the spatial 
                         distribution of the palm trees at the regional scale based on 
                         patches locations and edaphic conditions. Our deep learning 
                         network segmented palm trees patches with overall accuracies of 
                         95.5 % and Dice coefficients of 0.67. Then, the segmentation of 
                         tree species was produced over a region >2500 kmē using GeoEye 
                         Red, Green and Blue bands pan-sharpened at 0.5 m. We found that 
                         the palm trees covered 5 % of the natural forest canopies and were 
                         distributed in more than one million patches. Our results based on 
                         the palm trees distribution shown that their abundance tends to 
                         vary primarily with local soil water content over the landscape. 
                         Overall, their distribution over the region seems to indicate a 
                         relatively pristine landscape. However, we observed that they are 
                         sparsely distributed in secondary forests and could likely be used 
                         as an indicator of large past perturbation. Our work shows how 
                         deep learning algorithm can support applications such as mapping 
                         plant species to understand plant distributions and landscape 
                         features.",
  conference-location = "San Francisco, CA",
      conference-year = "09-13 dec.",
             language = "en",
           targetfile = "wagner_analysis.pdf",
        urlaccessdate = "01 maio 2024"
}