@Article{BendiniFoMaMaHaVa:2022:EvSeBe,
               author = "Bendini, Hugo do Nascimento and Fonseca, Leila Maria Garcia and 
                         Matosak, Bruno Menini and Mariano, Ravi Fernandes and Haidar, R. 
                         F. and Valeriano, Dalton de Morisson",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Universidade Federal do Tocantins (UFTO)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Evaluating the separability beteween dry tropical forests and 
                         Savanna woodlands in the brazilian Savanna using Landsat dense 
                         image time series and artificial intelligence",
              journal = "International Archives of the Photogrammetry, Remote Sensing and 
                         Spatial Information Sciences - ISPRS Archives",
                 year = "2022",
               volume = "1,",
               number = "2",
                pages = "841--847",
                month = "June",
             keywords = "Cerrado, Dry Forests, Machine Learning, Random Forest, Recurrent 
                         Neural Networks.",
             abstract = "The Brazilian Savanna is the second largest biogeographical region 
                         in Brazil and present different vegetation types, consisting 
                         mostly of tropical savannas, grasslands, and forests. The forest 
                         types have different tree cover and floristic composition, which 
                         is associated to leaf deciduousness. Considering the importance of 
                         Cerrado to biodiversity conservation and the maintaining of 
                         environmental services, the development of methods to map the 
                         different forest types in Cerrado is important for conservation 
                         programmes, subsidize restauration plains, and to allow 
                         estimations of carbon sink and stock. Mapping heterogeneous 
                         tropical areas, such as the Brazilian Savanna, is very complex due 
                         to the natural factors and peculiarities of the vegetation types, 
                         and it's still particularly challenging to separate between 
                         different forest formations. In this study we tested machine 
                         learning approaches based on the use of dense image time series, 
                         in order to evaluate the separability Dry Tropical Forests and 
                         Savanna woodlands. We considered the Brazilian State of Tocantins 
                         as the study area, which is located in the Northern region of the 
                         country. RF classification of Landsat dense time series showed an 
                         overall accuracy of 0.85005, while the LSTM approach presented an 
                         overall accuracy of 0.88601, with the highest f1-score for the 
                         savanna woodlands class, suggesting the capability of the 
                         recurrent neural networks on handling complex long-term 
                         dependencies such as the EVI dense time series data. This study 
                         showed the potential for the development of a semi-automatic 
                         method for discriminating the different types of forest formations 
                         in the Brazilian Savanna, based on remote sensing.",
                  doi = "10.5194/isprs-archives-XLIII-B3-2022-841-2022",
                  url = "http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2022-841-2022",
                 issn = "0256-1840",
             language = "en",
           targetfile = "isprs-archives-XLIII-B3-2022-841-2022.pdf",
        urlaccessdate = "11 maio 2024"
}