@InProceedings{SantosSGTMMGK:2014:DeAbBi,
               author = "Santos, Jo{\~a}o Roberto dos and Silva, Camila Val{\'e}ria de 
                         Jesus and Galv{\~a}o, Lenio Soares and Treuhaft, Robert and Mura, 
                         Jos{\'e} Cl{\'a}udio and Madsen, Soren and Gon{\c{c}}alves, 
                         F{\'a}bio Guimar{\~a}es and Keller, Michael Maier",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and Jet Propulsion Laboratory, 
                         California Institute of Technology, 4800 Oak Grove Drive, 
                         Pasadena, CA, 91109, United States and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and Jet Propulsion Laboratory, 
                         California Institute of Technology, 4800 Oak Grove Drive, 
                         Pasadena, CA, 91109, United States and Woods Hole Research Center, 
                         149, Woods Hole Road, MA, 02540, United States and USDA Forest 
                         Service, Rio-Piedras, 100745, Puerto Rico; University of New 
                         Hampshire, Durham, NH 03824, United States",
                title = "Determining aboveground biomass of the forest successional 
                         chronosequence in a test-site of Brazilian Amazon through X- and 
                         L-band data analysis",
            booktitle = "Proceedings...",
                 year = "2014",
         organization = "International Conference on Remote Sensing and Geoinformation of 
                         the Environment (RSCy2014), 2.",
            publisher = "SPIE",
              address = "Paphos",
             keywords = "Biomass, Forestry, Interferometry, Monitoring, Regression 
                         analysis, Remote sensing, Amazon forests, Biophysical parameters, 
                         Interferometric coherence, Multivariate regression, PALSAR/ALOS, 
                         Secondary succession, TanDEM/TerraSAR-X, Volumetric scattering, 
                         Synthetic aperture radar, Biomass, Forestry, Monitoring, Radar, 
                         Regression Analysis, Remote Sensing.",
             abstract = "Secondary succession is an important process in the Amazonian 
                         region with implications for the global carbon cycle and for the 
                         sustainable regional agricultural and pasture activities. In order 
                         to better discriminate the secondary succession and to 
                         characterize and estimate the aboveground biomass (AGB), 
                         backscatter and interferometric SAR data generally have been 
                         analyzed through empirical-based statistical modeling. The 
                         objective of this study is to verify the capability of the full 
                         polarimetric PALSAR/ALOS (L-band) attributes, when combined with 
                         the interferometric (InSAR) coherence from the TanDEM-X (X-band), 
                         to improve the AGB estimates of the succession chronosequence 
                         located in the Brazilian Tapaj{\'o}s region. In order to perform 
                         this study, we carried out multivariate regression using radar 
                         attributes and biophysical parameters acquired during a field 
                         inventory. A previous floristic-structural analysis was performed 
                         to establish the chronosequence in three stages: initial 
                         vegetation regrowth, intermediate, and advanced regrowth. The 
                         relationship between PALSAR data and AGB was significant (p<0.001) 
                         and results suggested that the {"} volumetric scattering{"} ? (Pv) 
                         and {"} anisotropy{"} ? (A) attributes were important to explain 
                         the biomass content of the successional chronosequence (R2 
                         adjusted= 0.67; RMSE = 32.29 Mg.ha -1). By adding the 
                         TanDEM-derived interferometric coherence (i) into the regression 
                         modeling, better results were obtained (R2 adjusted = 0.75; RMSE = 
                         28.78Mg.ha-1). When we used both the L- and X-band attributes, the 
                         stock density prediction improved to 10.8 % for the secondary 
                         succession stands. © 2014 SPIE.",
  conference-location = "Paphos, Cyprus",
      conference-year = "apr. 7, 2014",
                  doi = "10.1117/12.2066031",
                  url = "http://dx.doi.org/10.1117/12.2066031",
                 isbn = "9781628412765",
                 issn = "0277786X",
                label = "scopus 2014-11 SantosJGTMMGK:2014:DeAbBi",
             language = "en",
           targetfile = "92291E.pdf",
               volume = "9229",
        urlaccessdate = "04 maio 2024"
}