@InProceedings{SilvaCord:2017:UsAnIm,
               author = "Silva, Thiago Sanna Freire and Cordeiro, Carlos Leandro de 
                         Oliveira",
                title = "Uso de an{\'a}lise de imagens baseada em objetos (OBIA) e 
                         classifica{\c{c}}{\~a}o n{\~a}o-supervisionada para 
                         identifica{\c{c}}{\~a}o de envelopes bioclim{\'a}ticos no bioma 
                         amaz{\^o}nico",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4443--4450",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Mapping bioclimatic envelopes onto geographical space is an 
                         important process for predicting and understanding species range 
                         sizes and distribution limits This mapping depends on the 
                         availability of relevant environmental datasets at the appropriate 
                         scale, several which are commonly derived from remote sensing 
                         sources. Object-based image analysis (OBIA) is a relatively new 
                         approach in remote sensing, and together with machine learning 
                         algorithms, has been used successfully to map land cover classes 
                         in highly heterogeneous satellite and aerial images. Its 
                         framework, however, is equally applicable to other problems 
                         involving the delineation and classification of spatially 
                         homogeneous regions. Therefore, we investigated the applicability 
                         of OBIA and unsupervised clustering to detect possible bioclimatic 
                         envelopes and support biogeographical studies. Our selected study 
                         area was a portion of the Amazon basin, corresponding to 
                         interfluve between the Negro and Branco rivers. We combined 
                         topographic data from the SRTM mission, precipitation data from 
                         the CHIRPS dataset, radar backscattering from the ALOS/PALSAR 
                         sensor and spatially explicit estimates of canopy height and 
                         vegetation biomass, which was supplied as input data for image 
                         segmentation and posterior clustering, based on mean and standard 
                         deviation attributes. Overall, several bioclimatic envelopes could 
                         be mapped, some of them coinciding to land features usually 
                         associated with vicariance events, such as the Branco River 
                         channel. Our results emphasize the applicability of modern image 
                         analysis methods for identifying bioclimatic envelopes using 
                         spatially-explicit environmental data, and also the potential role 
                         of bioclimatic discontinuities, as well as vicariance, in 
                         explaining current distribution patterns for amazonian species.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59307",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSM35P",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSM35P",
           targetfile = "59307.pdf",
                 type = "Monitoramento e modelagem ambiental",
        urlaccessdate = "27 abr. 2024"
}