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@Article{CassolCaMoArSiQuSh:2019:ReSeFo,
               author = "Cassol, Henrique Luis Godinho and Carreiras, Jo{\~a}o Manuel de 
                         Brito and Moraes, Elisabete Caria and Arag{\~a}o, Luiz Eduardo 
                         Oliveira e Cruz de and Silva, Camila Val{\'e}ria de Jesus and 
                         Quegan, Shaun and Shimabukuro, Yosio Edemir",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {National 
                         Centre for Earth Observation (NCEO)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Lancaster University} and {National Centre 
                         for Earth Observation (NCEO)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Retrieving secondary forest aboveground biomass from polarimetric 
                         ALOS-2 PALSAR-2 data in the Brazilian Amazon",
              journal = "Remote Sensing",
                 year = "2019",
               volume = "11",
               number = "1",
                month = "jan.",
             keywords = "backscattering, L-band, SAR polarimetry, microwave, 
                         Chapman-Richards model, tropical forest.",
             abstract = "Secondary forests (SF) are important carbon sinks, removing CO2 
                         from the atmosphere through the photosynthesis process and storing 
                         photosynthates in their aboveground live biomass (AGB). This 
                         process occurring at large-scales partially counteracts C 
                         emissions from land-use change, playing, hence, an important role 
                         in the global carbon cycle. The absorption rates of carbon in 
                         these forests depend on forest physiology, controlled by 
                         environmental and climatic conditions, as well as on the past land 
                         use, which is rarely considered for retrieving AGB from remotely 
                         sensed data. In this context, the main goal of this study is to 
                         evaluate the potential of polarimetric (quad-pol) ALOS-2 PALSAR-2 
                         data for estimating AGB in a SF area. Land-use was assessed 
                         through Landsat time-series to extract the SF age, period of 
                         active land-use (PALU), and frequency of clear cuts (FC) to 
                         randomly select the SF plots. A chronosequence of 42 SF plots 
                         ranging 328 years (20 ha) near the Tapaj{\'o}s National Forest in 
                         Par{\'a} state was surveyed to quantifying AGB growth. The 
                         quad-pol data was explored by testing two regression methods, 
                         including non-linear (NL) and multiple linear regression models 
                         (MLR).We also evaluated the influence of the past land-use in the 
                         retrieving AGB through correlation analysis. The results showed 
                         that the biophysical variables were positively correlated with the 
                         volumetric scattering, meaning that SF areas presented greater 
                         volumetric scattering contribution with increasing forest age. 
                         Mean diameter, mean tree height, basal area, species density, and 
                         AGB were significant and had the highest Pearson coefficients with 
                         the Cloude decomposition (3), which in turn, refers to the 
                         volumetric contribution backscattering from cross-polarization 
                         (HV) ( = 0.570.66, p-value < 0.001). On the other hand, the 
                         historical use (PALU and FC) showed the highest correlation with 
                         angular decompositions, being the Touzi target phase angle the 
                         highest correlation (Fs) ( = 0.37 and = 0.38, respectively). The 
                         combination of multiple prediction variables with MLR improved the 
                         AGB estimation by 70% comparing to the NL model (R2 adj. = 0.51; 
                         RMSE = 38.7 Mg ha\􀀀1) bias = 2.1 37.9 Mg ha\􀀀1 
                         by incorporate the angular decompositions, related to historical 
                         use, and the contribution volumetric scattering, related to forest 
                         structure, in the model. The MLR uses six variables, whose 
                         selected polarimetric attributes were strongly related with 
                         different structural parameters such as the mean forest diameter, 
                         basal area, and the mean forest tree height, and not with the AGB 
                         as was expected. The uncertainty was estimated to be 18.6% 
                         considered all methodological steps of the MLR model. This 
                         approach helped us to better understand the relationship between 
                         parameters derived from SAR data and the forest structure and its 
                         relation to the growth of the secondary forest after deforestation 
                         events.",
                  doi = "10.3390/rs11010059",
                  url = "http://dx.doi.org/10.3390/rs11010059",
                 issn = "2072-4292",
             language = "en",
           targetfile = "cassol_retrieving.pdf",
        urlaccessdate = "28 nov. 2020"
}


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