@Article{WagnerSTLFAGPA:2019:UsUnCo,
               author = "Wagner, Fabien Hubert and Sanchez, Alber and Tarabalka, Yuliya and 
                         Lotte, Rodolfo Georjute and Ferreira, Matheus Pinheiro and Aidar, 
                         Marcos P. M. and Gloor, Emanuel 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 Sophia Antipo} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Institute 
                         of Botany} and {University of Leeds} and {University of Leeds} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Using the U-net convolutional network to map forest types and 
                         disturbance in the Atlantic rainforest with very high resolution 
                         images",
              journal = "Remote Sensing in Ecology and Conservation",
                 year = "2019",
               volume = "2019",
                pages = "1",
                 note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 15: Vida terrestre}",
             keywords = "Deep learning, Image segmentation, Keras, Rstudio, Tensorflow, 
                         Tree crown delineation, Tree species detection, Vegetation type 
                         detection, WorldView-3 image.",
             abstract = "Mapping forest types and tree species at regional scales to 
                         provide information for ecologists and forest managers is a new 
                         challenge for the remote sensing community. Here, we assess the 
                         potential of a U-net convolutional network, a recent deep learning 
                         algorithm, to identify and segment (1) natural forests and 
                         eucalyptus plantations, and (2) an indicator of forest 
                         disturbance, the tree species Cecropia hololeuca, in very high 
                         resolution images (0.3 m) from the WorldView-3 satellite in the 
                         Brazilian Atlantic rainforest region. The networks for forest 
                         types and Cecropia trees were trained with 7611 and 1568 
                         red-greenblue (RGB) images, respectively, and their dense labeled 
                         masks. Eighty per cent of the images were used for training and 
                         20% for validation. The U-net network segmented forest types with 
                         an overall accuracy >95% and an intersection over union (IoU) of 
                         0.96. For C. hololeuca, the overall accuracy was 97% and the IoU 
                         was 0.86. The predictions were produced over a 1600 km2 region 
                         using WorldView-3 RGB bands pan-sharpened at 0.3 m. Natural and 
                         eucalyptus forests compose 79 and 21% of the regions total forest 
                         cover (82 250 ha). Cecropia crowns covered 1% of the natural 
                         forest canopy. An index to describe the level of disturbance of 
                         the natural forest fragments based on the spatial distribution of 
                         Cecropia trees was developed. Our work demonstrates how a deep 
                         learning algorithm can support applications such as vegetation, 
                         tree species distributions and disturbance mapping on a regional 
                         scale.",
                  doi = "10.1002/rse2.111",
                  url = "http://dx.doi.org/10.1002/rse2.111",
                 issn = "2056-3485",
                label = "lattes: 5174466549126882 9 WagnerSTLFAGPA:2019:UsUnCo",
             language = "en",
           targetfile = "Wagner_et_al_Unet_2019.pdf",
        urlaccessdate = "27 abr. 2024"
}