author = "Bendini, Hugo do Nascimento and Fonseca, Leila Maria Garcia and 
                         Schwieder, M. and Rufin, P. and K{\"o}rting, Thales Sehn and 
                         Koumrouyan, Adriana and Hostert, P.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Humboldt-Universitat 
                         zu Berlin} and {Humboldt-Universitat zu Berlin} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Humboldt-Universitat zu Berlin}",
                title = "Combining environmental and landsat analysis ready data for 
                         vegetation mapping: a case study in the Brazilian Savanna Biome",
              journal = "International Archives of the Photogrammetry, Remote Sensing and 
                         Spatial Information Sciences - ISPRS Archives",
                 year = "2020",
               volume = "43",
               number = "B3",
                pages = "953--960",
                month = "Aug.",
                 note = "2020 24th ISPRS Congress - Technical Commission III; Nice, 
                         Virtual; France; 31 August 2020 through 2 September 2020",
             keywords = "Vegetation mapping, Cerrado, Phenology, Data mining, Random 
             abstract = "The Cerrado biome in Brazil covers approximately 24% of the 
                         country. It is one of the richest and most diverse savannas in the 
                         world, with 23 vegetation types (physiognomies) consisting mostly 
                         of tropical savannas, grasslands, forests and dry forests. It is 
                         considered as one of the global hotspots of biodiversity because 
                         of the high level of endemism and rapid loss of its original 
                         habitat. This work aims to analyze the potential of Landsat 
                         Analysis Ready Data (ARD) in combination with different 
                         environmental data to classify the vegetation in the Cerrado in 
                         two different hierarchical levels. Here we present results of a 
                         pixel-based modelling exercise, in which field data were combined 
                         with a set of input variables using a Random Forest classification 
                         approach. On the first hierarchical level, with the three classes 
                         savanna, grasslands and forest, our model results reached 
                         f1-scores of 0.86, 0.87 and 0.85 leading to an overall accuracy of 
                         0.86. In the second hierarchical level we differentiated a total 
                         of 12 vegetation physiognomies with an overall accuracy of 0.77.",
                  doi = "10.5194/isprs-archives-XLIII-B3-2020-953-2020",
                  url = "http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2020-953-2020",
                 issn = "0256-1840",
             language = "en",
           targetfile = "bendini_combining.pdf",
        urlaccessdate = "17 abr. 2021"