@InProceedings{SotheGeEsAlScFrLi:2017:AnMuCo,
               author = "Sothe, Camile and Gerente, J{\'e}ssica and Escada, Maria Isabel 
                         Sobral and Almeida, Cl{\'a}udia Maria de and Schimalski, Marcos 
                         Benedito and Francisco, Cristiane Nunes and Liesenberg, Veraldo",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "An{\'a}lise multitemporal da cobertura vegetal afetada por 
                         movimentos de massa no munic{\'{\i}}pio de Nova Friburgo-RJ",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1502--1509",
         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 = "In the past, different approaches for automated mass movements 
                         identification based on multispectral orbital images were 
                         developed to focus on the analysis of the spatial distribution of 
                         mass movements occurrences related to distinct triggering events. 
                         However, a continual multi-temporal analysis is important for 
                         monitoring vegetation recovery of affected areas. The first 
                         objective of this paper was to use a semi-automated mapping 
                         approach based on ALOS and RapidEye time series data. For change 
                         detection, a threshold method was applied in a difference image 
                         resulting from the subtraction between NDVI and GNDVI from 2010 
                         and 2011 images. The second objective was to check recovery 
                         vegetation areas incorporating the 2015 image at issue. For this 
                         purpose, NDVI and GNDVI of three images associated with change 
                         objects resulting from the first objective described above were 
                         used in a decision tree classification algorithm. The change 
                         detection approach resulted in the identification of nearly 
                         129-145 ha associated with mass movements occurrence. A 
                         quantitative accuracy assessment for these two methods has 
                         revealed a detection percentage of 75% of mass movements with the 
                         NDVI method, and 67% with the GNDVI method, however, NDVI resulted 
                         in higher commission errors. The classification with C4.5 decision 
                         tree algorithm revealed 121ha of areas under recovery in 2015, 
                         while 106 ha have not been undergone recovery yet. The study 
                         proved the suitability of the developed approaches for efficient 
                         spatiotemporal mass movements mapping areas, representing an 
                         important prerequisite for mass movements hazard and risk 
                         assessment at the regional scale.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "60022",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PS4GQN",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PS4GQN",
           targetfile = "60022.pdf",
                 type = "Detec{\c{c}}{\~a}o de mudan{\c{c}}as",
        urlaccessdate = "27 abr. 2024"
}