@InProceedings{WagnerSTLFAGPA:2018:UsCoNe,
               author = "Wagner, Fabien Hubert and Sanchez Ipia, Alber Hamersson and 
                         Tarabakla, Yuliya and Lotte, Rodolfo Georjute and Ferreira, 
                         Matheus Pinheiro and Aidar, Marcos P. M. and Gloor, Manuel and 
                         Phillips, Oliver L. 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 INRIA and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {} and {University of Leeds} and 
                         {University of Leeds} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Using convolutional network to identify tree species related to 
                         forest disturbance in a neotropical forest with very high 
                         resolution multispectral images",
                 year = "2018",
         organization = "AGU Fall Meeting",
             abstract = "Mapping tree species at landscape scale to provide information for 
                         ecologists and forest managers is a new challenge for the remote 
                         sensing community. Here, we tested the potential of a recent deep 
                         learning algorithm to identify and segment tree species associated 
                         with forest disturbance in very high-resolution multispectral 
                         images (0.3 m) from WorldView-3 satellite. The study was conducted 
                         in a region of the critically endangered Brazilian Atlantic 
                         Rainforest, which is a global priority for biodiversity 
                         conservation due to its abundance of species of flora and fauna 
                         occurring across an extremely fragmented and degraded landscape. 
                         The convolutional network generated in this study for identifying 
                         trees from different species was trained with about 1500 
                         high-resolution true colour synthetic optical images and their 
                         labelled masks for each species. Additionally, we created a new 
                         framework for measuring disturbance levels within forest fragments 
                         based on the spatial distribution of individual 
                         disturbance-related trees. Our deep learning network segmented 
                         tree species with overall accuracies of above 95% and Dice 
                         coefficients of above 0.85. Then, the segmentation of tree species 
                         was produced over a region >1000 kmē using WorldView-3 Red, Green 
                         and Blue bands pan-sharpened at 0.3 m. We found that the crowns of 
                         disturbance-related species covered between 1 and 5 % of the 
                         natural forest canopies. Our results based on the trees 
                         distribution shown that disturbance tends to increase with 
                         fragment size and revealed information that were not accessible 
                         from classical landscape fragmentation analysis, which is mainly 
                         based on size and connection of the forest fragments. We are still 
                         far from recognizing all the species, however, species that are 
                         indicator of disturbance and early successional stage of forests 
                         can be accurately mapped. Our work shows how deep learning 
                         algorithm can support applications such as mapping tree species 
                         and forest disturbance at the landscape scale from space.",
  conference-location = "Washington, D. C.",
      conference-year = "10-14 dec.",
             language = "en",
           targetfile = "wagner_using.pdf",
        urlaccessdate = "27 abr. 2024"
}