author = "Pereira, Francisca Rocha de Souza and Vincent, Gregoire and 
                         Couteron, Pierre and Kampel, Milton",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and AMAP, IRD, 
                         CNRS, CIRAD, INRA and AMAP, IRD, CNRS, CIRAD, INRA and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Training a satellite imagery texture based approach to monitor AGB 
                         in mangrove using aerial lidar",
            booktitle = "Proceedings...",
                 year = "2018",
         organization = "IUFRO Conference",
             abstract = "Mangroves are important intertidal ecosystems typically in 
                         tropical and subtropical regions. Their restoration and 
                         conservation are important for the regulation of carbon fluxes and 
                         climate change control, also to maintain their valuable services 
                         for the coastal zone. The main goal of this study is to 
                         investigate the potential use of textural indices derived from a 
                         very high spatial resolution WorldView-2 image to estimate the 
                         aboveground biomass (AGB) of a mangrove forest in the 
                         Environmental Protection Area of Guapimirim (RJ, Brazil) subject 
                         to different levels of disturbance. Fourier-based Textural 
                         Ordination (FOTO) (Couteron, 2002, Proisy et al. 2007) and 
                         Grey-Level Co-occurrence Matrix (GLCM) (Haralick et al. 1973) 
                         textural indices were extracted from the panchromatic optical 
                         image. An accurate map of AGB was derived from lidar data and this 
                         map was used to train and test Random Forest, and AutoPLS methods 
                         to estimate AGB. The textural variability pattern associated with 
                         the canopy characteristics of the mangrove measured by FOTO and 
                         GLCM indices showed reasonable relationships with AGB. When many 
                         training points (from lidar) and both types of texture indices 
                         were used together the results improved markedly (RMSE (LOO) 
                         =25.64 t/ha, Rē(LOO) =0.41). One source of uncertainty comes from 
                         the fact that degraded forests with low AGB values present coarse 
                         textures and can be confused with the textural pattern of high and 
                         more preserved forest characterized by large crowns. Our 
                         methodology can be applied to forests with different degrees of 
                         development but requires cautions for degraded forests for which 
                         texture gradients are not univocal. Nevertheless, the Random 
                         Forest classification based on the textural indices showed good 
                         results for the discrimination of different types of land cover 
                         such as non-mangrove, altered and preserved mangroves. Efforts 
                         such as those developed in this work are necessary to quantify AGB 
                         and carbon stocks, for monitoring purposes, as to assist public 
                         policies for the conservation and protection of these 
  conference-location = "Posadas, Argentina",
      conference-year = "01-05 oct.",
             language = "en",
           targetfile = "pereira_training.pdf",
        urlaccessdate = "09 maio 2021"