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@Article{ShimabukuroArDuJoSaGaDu:2019:MoDeFo,
               author = "Shimabukuro, Yosio Edemir and Arai, Egidio and Duarte, Valdete and 
                         Jorge, Anderson and Santos, Erone Ghyizoni dos and Gasparini, Kaio 
                         Allan Cruz and Dutra, Andeise Cerqueira",
          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 {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Monitoring deforestation and forest degradation using 
                         multi-temporal fraction images derived from Landsat sensor data in 
                         the Brazilian Amazon",
              journal = "International Journal of Remote Sensing",
                 year = "2019",
               volume = "40",
               number = "4",
                pages = "5475--5496",
                month = "July",
             abstract = "Deforestation is the replacement of forest by other land use while 
                         degradation is a reduction of long-term canopy cover and/or forest 
                         stock. Forest degradation in the Brazilian Amazon is mainly due to 
                         selective logging of intact/un-managed forests and to uncontrolled 
                         fires. The deforestation contribution to carbon emission is 
                         already known but determining the contribution of forest 
                         degradation remains a challenge. Discrimination of logging from 
                         fires, both of which produce different levels of forest damage, is 
                         important for the UNFCCC (United Nations Framework Convention on 
                         Climate Change) REDD+ (Reducing Emissions from Deforestation and 
                         Forest Degradation) program. This work presents a semi-automated 
                         procedure for monitoring deforestation and forest degradation in 
                         the Brazilian Amazon using fraction images derived from Linear 
                         Spectral Mixing Model (LSMM). Part of a Landsat Thematic Mapper 
                         (TM) scene (path/row 226/068) covering part of Mato Grosso State 
                         in the Brazilian Amazon, was selected to develop the proposed 
                         method. First, the approach consisted of mapping deforested areas 
                         and mapping forest degraded by fires using image segmentation. 
                         Next, degraded areas due to selective logging activities were 
                         mapped using a pixel-based classifier. The results showed that the 
                         vegetation, soil, and shade fraction images allowed deforested 
                         areas to be mapped and monitored and to separate degraded forest 
                         areas caused by selective logging and by fires. The comparison of 
                         Landsat Operational Land Imager (OLI) and RapidEye results for the 
                         year 2013 showed an overall accuracy of 94%. We concluded that 
                         spatial resolution plays an important role for mapping selective 
                         logging features due to their characteristics. Therefore, when 
                         compared to Landsat data, the current availability of higher 
                         spatial and temporal resolution data, such as provided by 
                         Sentinel-2, is expected to improve the assessment of deforestation 
                         and forest degradation, especially caused by selective logging. 
                         This will facilitate the implementation of actions for forest 
                         protection.",
                  doi = "10.1080/01431161.2019.1579943",
                  url = "http://dx.doi.org/10.1080/01431161.2019.1579943",
                 issn = "0143-1161",
             language = "en",
           targetfile = "Monitoring deforestation and forest degradation using multi 
                         temporal fraction images derived from Landsat sensor data in the 
                         Brazilian Amazon.pdf",
        urlaccessdate = "29 nov. 2020"
}


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