@Article{AdamiBAFSERA:2018:SeVeTy,
               author = "Adami, Marcos and Bernardes, S{\'e}rgio and Arai, Egidio and 
                         Freitas, Ramon M. and Shimabukuro, Yosio Edemir and 
                         Esp{\'{\i}}rito-Santo, Fernando D. B. and Rudorff, Bernardo F. 
                         T. and Anderson, Liana O.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {University 
                         of Georgia Athen} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Aerial Photography \& Imaging Science} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Lancaster 
                         Environment Centre (LEC)} and {Agrosat{\'e}lite Geotecnologia 
                         Aplicada Ltda} and {Centro Nacional de Monitoramento e Alertas de 
                         Desastres Naturais (CEMADEN)}",
                title = "Seasonality of vegetation types of South America depicted by 
                         moderate resolution imaging spectroradiometer (MODIS) time 
                         series",
              journal = "International Journal of Applied Earth Observation and 
                         Geoinformation",
                 year = "2018",
               volume = "69",
                pages = "148--163",
             keywords = "Vegetation dynamics, Land cover, Disturbance, Phenology, 
                         MODISSpectral mixing model, Principal component analysis, Time 
                         series.",
             abstract = "The development, implementation and enforcement of policies 
                         involving the rational use of the land and the conservation of 
                         natural resources depend on an adequate characterization and 
                         understanding of the land cover, including its dynamics. This 
                         paper presents an approach for monitoring vegetation dynamics 
                         using high-quality time series of MODIS surface reflectance data 
                         by generating fraction images using Linear Spectral Mixing Model 
                         (LSMM) over South America continent. The approach uses 
                         physically-based fraction images, which highlight target 
                         information and reduce data dimensionality. Further dimensionality 
                         was also reduced by using the vegetation fraction images as input 
                         to a Principal Component Analysis (PCA). The RGB composite of the 
                         first three PCA components, accounting for 92.9% of the dataset 
                         variability, showed good agreement with the main ecological 
                         regions of South America continent. The analysis of 21 temporal 
                         profiles of vegetation fraction values and precipitation data over 
                         South America showed the ability of vegetation fractions to 
                         represent phenological cycles over a variety of environments. 
                         Comparisons between vegetation fractions and precipitation data 
                         indicated the close relationship between water availability and 
                         leaf mass/chlorophyll content for several vegetation types. In 
                         addition, phenological changes and disturbance resulting from 
                         anthropogenic pressure were identified, particularly those 
                         associated with agricultural practices and forest removal. 
                         Therefore the proposed method supports the management of natural 
                         and non-natural ecosystems, and can contribute to the 
                         understanding of key conservation issues in South America, 
                         including deforestation, disturbance and fire occurrence and 
                         management.",
                  doi = "10.1016/j.jag.2018.02.010",
                  url = "http://dx.doi.org/10.1016/j.jag.2018.02.010",
                 issn = "0303-2434",
                label = "lattes: 7484071887086439 1 AdamiBAFSERA:2018:SeVeTy",
             language = "en",
           targetfile = "adami_seasonality.pdf",
        urlaccessdate = "27 abr. 2024"
}