@Article{WangZAMBBMMRRG:2019:MaTrDi,
               author = "Wang, Yunxia and Ziv, Guy and Adami, Marcos and Mitchard, Edward 
                         and Batterman, Sarah A. and Buermann, Wolfgang and Marimon, 
                         Beatriz Schwantes and Marimon Junior, Ben Hur and Reis, Simone 
                         Matias and Rodrigues, Domingos and Galbraith, David",
          affiliation = "{University of Leeds} and {University of Leeds} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of 
                         Edinburgh} and {University of Leeds} and {University of Leeds} and 
                         {University of Edinburgh} and {University of Edinburgh} and 
                         {University of Edinburgh} and {Universidade Federal de Mato Grosso 
                         (UFMT)} and {University of Leeds}",
                title = "Mapping tropical disturbed forests using multi-decadal 30 m 
                         optical satellite imagery",
              journal = "Remote Sensing of Environment",
                 year = "2019",
               volume = "22198",
                pages = "474--788",
                month = "Feb.",
             abstract = "Tropical disturbed forests play an important role in global carbon 
                         sequestration due to their rapid post-disturbance biomass 
                         accumulation rates. However, the accurate estimation of the carbon 
                         sequestration capacity of disturbed forests is still challenging 
                         due to large uncertainties in their spatial distribution. Using 
                         Google Earth Engine (GEE), we developed a novel approach to map 
                         cumulative disturbed forest areas based on the 27-year time-series 
                         of Landsat surface reflectance imagery. This approach integrates 
                         single date features with temporal characteristics from six 
                         time-series trajectories (two Landsat shortwave infrared bands and 
                         four vegetation indices) using a random forest machine learning 
                         classification algorithm. We demonstrated the feasibility of this 
                         method to map disturbed forests in three different forest 
                         ecoregions (seasonal, moist and dry forest) in Mato Grosso, 
                         Brazil, and found that the overall mapping accuracy was high, 
                         ranging from 81.3% for moist forest to 86.1% for seasonal forest. 
                         According to our classification, dry forest ecoregion experienced 
                         the most severe disturbances with 41% of forests being disturbed 
                         by 2010, followed by seasonal forest and moist forest ecoregions. 
                         We further separated disturbed forests into degraded old-growth 
                         forests and post-deforestation regrowth forests based on an 
                         existing post-deforestation land use map (TerraClass) and found 
                         that the area of degraded old-growth forests was up to 62% larger 
                         than the extent of post-deforestation regrowth forests, with 18% 
                         of old-growth forests actually being degraded. Application of this 
                         new classification approach to other tropical areas will provide a 
                         better constraint on the spatial extent of disturbed forest areas 
                         in Tropics and ultimately towards a better understanding of their 
                         importance in the global carbon cycle.",
                  doi = "10.1016/j.rse.2018.11.028",
                  url = "http://dx.doi.org/10.1016/j.rse.2018.11.028",
                 issn = "0034-4257",
             language = "en",
           targetfile = "wang_mapping.pdf",
        urlaccessdate = "27 abr. 2024"
}