@Article{CassolAraMorCarShi:2021:QuAdLa,
               author = "Cassol, Henrique Lu{\'{\i}}s Godinho and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Moraes, Elisabete Caria and 
                         Carreira, Jo{\~a}o Manuel de Brito and Shimabukuro, Yosio 
                         Edemir",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {University of Sheffield} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Quad-pol Advanced Land Observing Satellite / Phased Array L-band 
                         Synthetic Aperture Radar-2 (ALOS/PALSAR-2) data for modelling 
                         secondary forest above-ground biomass in the central Brazilian 
                         Amazon",
              journal = "International Journal of Remote Sensing",
                 year = "2021",
               volume = "42",
               number = "13",
                pages = "4989--5013",
             abstract = "Secondary forests (SFs) are one of the major carbons sinks in the 
                         Neotropics due to the rapid carbon assimilation in their 
                         above-ground biomass (AGB). However, the accurate contribution of 
                         SFs to the carbon cycle is a great challenge because of the 
                         uncertainty in AGB estimates. In this context, the main objective 
                         of this study is to explore full polarimetric Advanced Land 
                         Observing Satellite/Phased Array L-band Synthetic Aperture Radar-2 
                         (ALOS/PALSAR-2) data to model SFs AGB in the Central Amazon. We 
                         carried out the forest inventory in 2014, measuring 23 field 
                         plots. Supplementary land-use classification history was used to 
                         create 120 additional independent sample plots by adjusting growth 
                         curves using SFs age and previous land-use intensity from field 
                         plots and literature database. Multiple Linear Regression (MLR) 
                         analysis was performed to select the best model by corrected 
                         weighted Akaike Information Criterion (AICw) and validated by the 
                         leave-one-out bootstrapping method. The best-fitted model has six 
                         parameters and explained 65% of the above-ground biomass 
                         variability. The prediction error was of Root Mean Square Error of 
                         the Prediction (RMSEP) = 8.8 ± 3.0 tonnes ha\−1 (8.8%). The 
                         most explanatory variables for modelling secondary forest AGB were 
                         those that result from multiple scattering (Shannon Entropy), 
                         volumetric scattering (Bhattacharya decomposition), and 
                         double-bounce scattering (ratio VV/HH, vertically transmitted and 
                         received polarization/horizontally transmitted and received 
                         polarization). Including past-use of SF areas in the model with 
                         the Landsat time series classification, as the frequency of clear 
                         cuts and the number of years of active land-use before 
                         abandonment, the MLR has increased by 10%, achieving 71% of the 
                         variability explained by the model. The uncertainty report showed 
                         that ground truth AGB estimation (inventory, allometry, and plot 
                         expansion factors) might represent 50% of the errors in the 
                         modelling estimation. In contrast, Synthetic Aperture Radar (SAR) 
                         inversion models (SAR error and regression) have achieved 20%. The 
                         results showed that additional information on secondary forest 
                         land-use history could improve the performance of AGB recovery 
                         models, as well as can be used to expand the sampling units on 
                         tropical forests.",
                  doi = "10.1080/01431161.2021.1903615",
                  url = "http://dx.doi.org/10.1080/01431161.2021.1903615",
                 issn = "0143-1161",
           targetfile = "godinhocassol2021.pdf",
        urlaccessdate = "03 maio 2024"
}