@Article{MatosakFoTaMaBeAd:2022:MaDeCe,
               author = "Matosak, Bruno Menini and Fonseca, Leila Maria Garcia and Taquary, 
                         Evandro Carrijo and Maretto, Raian Vargas and Bendini, Hugo do 
                         Nascimento and Adami, Marcos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {University of Twente} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Mapping Deforestation in Cerrado Based on Hybrid Deep Learning 
                         Architecture and Medium Spatial Resolution Satellite Time Series",
              journal = "Remote Sensing",
                 year = "2022",
               volume = "14",
               number = "1",
                pages = "e209",
                month = "Jan.",
             keywords = "Brazilian savanna, Cerrado, Deforestation, Landsat, LSTM, 
                         Sentinel, Time series, U-Net.",
             abstract = "Cerrado is the second largest biome in Brazil, covering about 2 
                         million km2. This biome has experienced land use and land cover 
                         changes at high rates due to agricultural expansion so that more 
                         than 50% of its natural vegetation has already been removed. 
                         Therefore, it is crucial to provide technology capable of 
                         controlling and monitoring the Cerrado vegetation suppression in 
                         order to undertake the environmental conservation policies. Within 
                         this context, this work aims to develop a new methodology to 
                         detect deforestation in Cerrado through the combination of two 
                         Deep Learning (DL) architectures, Long Short-Term Memory (LSTM) 
                         and U-Net, and using Landsat and Sentinel image time series. In 
                         our proposed method, the LSTM evaluates the time series in 
                         relation to the time axis to create a deforestation probability 
                         map, which is spatially analyzed by the U-Net algorithm alongside 
                         the terrain slope to produce final deforestation maps. The method 
                         was applied in two different study areas, which better represent 
                         the main deforestation patterns present in Cerrado. The resultant 
                         deforestation maps based on cost-free Sentinel-2 images achieved 
                         high accuracy metrics, peaking at an overall accuracy of 99.81% ± 
                         0.21 and F1-Score of 0.8795 ± 0.1180. In addition, the proposed 
                         method showed strong potential to automate the PRODES project, 
                         which provides the official Cerrado yearly deforestation maps 
                         based on visual interpretatio.",
                  doi = "10.3390/rs14010209",
                  url = "http://dx.doi.org/10.3390/rs14010209",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-14-00209-v2.pdf",
        urlaccessdate = "05 maio 2024"
}