@Article{DutraShimEsca:2018:DaMiUs,
               author = "Dutra, Andeise Cerqueira and Shimabukuro, Yosio Edemir and Escada, 
                         Maria Isabel Sobral",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Data mining using NDVI time series applied to change detection",
              journal = "Sciforum Electronic Conference Series",
                 year = "2018",
               volume = "2",
               number = "7",
                pages = "e05169",
                 note = "The 2nd International Electronic Conference on Remote Sensing 
                         (ECRS 2018), 22 March–5 April 2018;",
             keywords = "Land cover change, deforestation, GeoDMA, semiarid, Caatinga.",
             abstract = "Information about the land cover and land use of a region are 
                         fundamental in studies such as mapping of deforestation and forest 
                         degradation. Quantifying and monitoring woody cover distribution 
                         in semiarid regions is challenging, due to their scattered 
                         distribution. Data mining has been widely used in remote sensing 
                         data for information extraction of spectral and temporal data in 
                         the analysis of change detection. The main objective of this study 
                         was to characterize the land cover and land use over 2000-2010 
                         time period for the Brazilian Caatinga seasonal biome using a 
                         temporal NDVI series and Geographic Object-Based Image Analysis. 
                         For each of the target years was obtained NDVI images derived from 
                         MODIS (MOD13Q1, at 250 m spatial and 16 day temporal scale) sensor 
                         during the dry season to predict wood cover in the municipality of 
                         Buriti dos Montes, in the state of Piau{\'{\i}}, Northeast 
                         region of Brazil (H13V09 tile). The images were automatically 
                         pre-processed and in the GEOBIA approach was performed image 
                         segmentation, spatial and spectral attribute extraction and 
                         labelled according to the following legend: Tree Cover (TC) and 
                         Cropland/Grass (CG), to obtain a classification using the decision 
                         tree supervised algorithm. Our results showed that approach using 
                         GEOBIA presented Kappa Index of 0.58 and Global Accuracy (GA) of 
                         0.81% and showed better accuracy for the Tree Cover. Finally, we 
                         recommend new studies using a higher spatial resolution data, as 
                         well as the addition of other parameters strongly related to 
                         vegetation of semiarid regions.",
                  doi = "10.3390/ecrs-2-05169",
                  url = "http://dx.doi.org/10.3390/ecrs-2-05169",
                 issn = "2072-4292",
                label = "lattes: 8734553235868564 1 DutraShimEsca:2018:DaMiUs",
             language = "en",
           targetfile = "Dats_Mining_Published_proceedings-02-00356.pdf",
        urlaccessdate = "27 abr. 2024"
}