@Article{CassolArSaDuHoSh:2020:MaFrIm,
               author = "Cassol, Henrique Luis Godinho and Arai, Eg{\'{\i}}dio and Sano, 
                         Edson Eyji and Dutra, Andeise Cerqueira and Hoffmann, T{\^a}nia 
                         Beatriz and Shimabukuro, Yosio Edemir",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Empresa Brasileira de 
                         Pesquisa Agropecu{\'a}ria (EMBRAPA)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Maximum fraction images derived from year-based Project for 
                         On-Board Autonomy-Vegetation (PROBA-V) data for the rapid 
                         assessment of land use and land cover areas in Mato Grosso State, 
                         Brazil",
              journal = "Land",
                 year = "2020",
               volume = "9",
                pages = "e139",
             keywords = "spectral unmixing, machine learning, fraction images, cloud 
                         computing.",
             abstract = "This paper presents a new approach for rapidly assessing the 
                         extent of land use and land cover (LULC) areas in Mato Grosso 
                         state, Brazil. The novel idea is the use of an annual time series 
                         of fraction images derived from the linear spectral mixing model 
                         (LSMM) instead of original bands. The LSMM was applied to the 
                         Project for On-Board Autonomy-Vegetation (PROBA-V) 100-m data 
                         composites from 2015 (~73 scenes/year, cloud-free images, in 
                         theory), generating vegetation, soil, and shade fraction images. 
                         These fraction images highlight the LULC components inside the 
                         pixels. The other new idea is to reduce these time series to only 
                         six single bands representing the maximum and standard deviation 
                         values of these fraction images in an annual composite, reducing 
                         the volume of data to classify the main LULC classes. The whole 
                         image classification process was conducted in the Google Earth 
                         Engine platform using the pixel-based random forest algorithm. A 
                         set of 622 samples of each LULC class was collected by visual 
                         inspection of PROBA-V and Landsat-8 Operational Land Imager (OLI) 
                         images and divided into training and validation datasets. The 
                         performance of the method was evaluated by the overall accuracy 
                         and confusion matrix. The overall accuracy was 92.4%, with the 
                         lowest misclassification found for cropland and forestland (<9% 
                         error). The same validation data set showed 88% agreement with the 
                         LULC map made available by the Landsat-based MapBiomas project. 
                         This proposed method has the potential to be used operationally to 
                         accurately map the main LULC areas and to rapidly use the PROBA-V 
                         dataset at regional or national levels.",
                  doi = "10.3390/land9050139",
                  url = "http://dx.doi.org/10.3390/land9050139",
                 issn = "2073-445X",
             language = "en",
           targetfile = "land-09-00139.pdf",
        urlaccessdate = "26 abr. 2024"
}