@InProceedings{SaldanhaGuaLimDucBra:2009:IdMaRe,
               author = "Saldanha, Dejanira Luderitz and Guasselli, Laurindo Antonio and 
                         Lima e Cunha, Maria do Carmo and Ducati, Jorge Ricardo and Brack, 
                         Paulo",
          affiliation = "{Universidade Federal do Rio Grande do Sul/RS} and {Universidade 
                         Federal do Rio Grande do Sul/RS} and {Universidade Federal do Rio 
                         Grande do Sul/RS} and {Universidade Federal do Rio Grande do 
                         Sul/RS} and {Universidade Federal do Rio Grande do Sul/RS}",
                title = "Identification and mapping by remote sensing of native forests of 
                         the Atlantic Forest Biome in Rio Grande do Sul, Brazil",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "2995--3002",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 14. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "floresta tropical, monitoramento de florestas, classificador MVG, 
                         imagens SPOT, classifica{\c{c}}{\~a}o de uso do solo.",
             abstract = "Management initiatives aiming on the conservation of tropical rain 
                         forest in southeastern Brazil ask for mapping and long term 
                         monitoring. The mapping of the Atlantic Forest in Rio Grande do 
                         Sul State, Brazil, was done through a set of ten images taken from 
                         August 2002 to April 2003 by the HRG sensor aboard SPOT-5 
                         satellite. Images were geocoded using control points extracted 
                         from topographic maps at scale 1:50,000. Five forest subclasses 
                         were identified, based on analysis of images classified by the 
                         Gaussian Maximum Likelihood (GML) algorithm. Classification 
                         results were validated by ground truth surveyed at field trips. 
                         Besides the native forest classes, twelve other land-cover classes 
                         were implemented into the classification process. Final results 
                         include a set of 45 maps of the region, area delineation, and 
                         surface quantification for all forest classes. Botanical 
                         descriptions of native forest classes are given. The 
                         characteristic botanical composition of each class is the main 
                         factor to give for each one its characteristic spectral signature. 
                         Another separation parameter is geographical localization and 
                         resulting shadow effects. In a longer perspective, this project 
                         aims to monitor alterations of conditions across the forested 
                         areas, like additional deforestation and/or re-growth, aided by 
                         new imagery to be taken at five-year intervals.",
  conference-location = "Natal",
      conference-year = "25-30 abr. 2009",
                 isbn = "978-85-17-00044-7",
             language = "en",
         organisation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
                  ibi = "3ERPFQRTBW/348N52S",
                  url = "http://urlib.net/ibi/3ERPFQRTBW/348N52S",
           targetfile = "2995-3002.pdf",
                 type = "Floresta e Vegeta{\c{c}}{\~a}o",
        urlaccessdate = "05 jun. 2024"
}