@Article{SilvaWaEmStGaOmAr:2022:CaPaCo,
               author = "Silva, Ricardo Dalagnol da and Wagner, Fabien Hubert and Emilio, 
                         Thaise and Streher, Annia Susin and Galv{\~a}o, L{\^e}nio Soares 
                         and Ometto, Jean Pierre Henry Balbaud 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 
                         de Campinas (UNICAMP)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Canopy palm cover across the Brazilian Amazon forests mapped with 
                         airborne LiDAR data and deep learning",
              journal = "Remote Sensing in Ecology and Conservation",
                 year = "2022",
               volume = "8",
               number = "5",
                pages = "601--614",
                month = "Oct.",
             keywords = "Airborne LiDAR, Amazon, biodiversity, deep learning, palm cover.",
             abstract = "The Amazon region in Brazil contains c. 5% of the palm species of 
                         the world. However, palm cover at macroecological scales has not 
                         yet been quantified in this biome. Here, we used high spatial 
                         resolution LiDAR data, acquired from 610 flightlines over the 
                         Brazilian Amazon, to map canopy palm cover for the first time 
                         using a deep learning approach. The image segmentation model from 
                         U-Net deep learning was selected for mapping palm segments using 
                         the LiDAR canopy height model (CHM) at 0.5-m spatial resolution. 
                         To train and validate the model, we manually delineated 6971 
                         canopy palm segments over 931.43 ha of forests on four training 
                         sites by inspecting their unique star-shaped crown architecture in 
                         the CHM. The results indicated an accuracy of 80% to automatically 
                         map canopy palm area. The approach detected >1.1 million palm 
                         segments over the 480 000 ha sampled by LiDAR and roughly 
                         estimated 1.05 billion palm segments for the Brazilian Amazon. 
                         Palm cover was not evenly distributed over the Amazon, revealing 
                         undocumented hotspots of high cover (>5%) in eastern Amazon 
                         (Par{\'a} state), and confirming documented hotspots in southwest 
                         (Acre state) and north of the region (Roraima state). Palm segment 
                         height was strongly and positively correlated with forest height, 
                         where palm segments showed overall lower height. A higher canopy 
                         palm cover was observed over shorter forests, while the opposite 
                         was found over taller forests, where palms may not be visible from 
                         the canopy. Palm segments occurred more frequently at valleys but 
                         they were also observed in other landscapes, depending on site 
                         location and forest height. Our findings highlight the 
                         disproportional occurrence of palm cover in some Amazonian 
                         canopies. This fact should be taken into account to improve 
                         regional carbon cycle representation and promote initiatives of 
                         biodiversity conservation and bioeconomic use of these forests.",
                  doi = "10.1002/rse2.264",
                  url = "http://dx.doi.org/10.1002/rse2.264",
                 issn = "2056-3485",
             language = "en",
           targetfile = "Remote Sens Ecol Conserv - 2022 - Dalagnol - Canopy palm cover 
                         across the Brazilian Amazon forests mapped with airborne.pdf",
        urlaccessdate = "2024, May 02"
}