author = "Bell{\'o}n, Beatriz and B{\'e}gu{\'e}, Agn{\`e}s and Seen, 
                         Danny Lo and Almeida, Cl{\'a}udio Aparecido de and Sim{\~o}es, 
          affiliation = "Cirad, UMR TETIS and Cirad, UMR TETIS and Cirad, UMR TETIS and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Embrapa 
                title = "A remote sensing approach for regional-scale mapping of 
                         agricultural land-use systems based on NDVI time series",
              journal = "Remote Sensing",
                 year = "2017",
               volume = "9",
               number = "6",
                month = "June",
             keywords = "geographic object-based image analysis (GEOBIA), Moderate 
                         Resolution Imaging Spectroradiometer (MODIS), principal components 
                         analysis (PCA), cropping systems, Stratification.",
             abstract = "In response to the need for generic remote sensing tools to 
                         support large-scale agricultural monitoring, we present a new 
                         approach for regional-scale mapping of agricultural land-use 
                         systems (ALUS) based on object-based Normalized Difference 
                         Vegetation Index (NDVI) time series analysis. The approach 
                         consists of two main steps. First, to obtain relatively 
                         homogeneous land units in terms of phenological patterns, a 
                         principal component analysis (PCA) is applied to an annual MODIS 
                         NDVI time series, and an automatic segmentation is performed on 
                         the resulting high-order principal component images. Second, the 
                         resulting land units are classified into the crop agriculture 
                         domain or the livestock domain based on their land-cover 
                         characteristics. The crop agriculture domain land units are 
                         further classified into different cropping systems based on the 
                         correspondence of their NDVI temporal profiles with the 
                         phenological patterns associated with the cropping systems of the 
                         study area. A map of the main ALUS of the Brazilian state of 
                         Tocantins was produced for the 20132014 growing season with the 
                         new approach, and a significant coherence was observed between the 
                         spatial distribution of the cropping systems in the final ALUS map 
                         and in a reference map extracted from the official agricultural 
                         statistics of the Brazilian Institute of Geography and Statistics 
                         (IBGE). This study shows the potential of remote sensing 
                         techniques to provide valuable baseline spatial information for 
                         supporting agricultural monitoring and for large-scale land-use 
                         systems analysis.",
                  doi = "10.3390/rs9060600",
                  url = "http://dx.doi.org/10.3390/rs9060600",
                 issn = "2072-4292",
             language = "en",
           targetfile = "Bellon_remote.pdf",
        urlaccessdate = "22 jan. 2021"