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@Article{GonçalvesTLAWBSG:2017:FiMeEr,
               author = "Gon{\c{c}}alves, Fabio and Treuhaft, Robert and Law, Beverly and 
                         Almeida, Andr{\'e} and Walker, Wayne and Baccini, Alessandro and 
                         Santos, Jo{\~a}o Roberto dos and Gra{\c{c}}a, Paulo",
          affiliation = "{Canopy Remote Sensing Solutions} and {California Institute of 
                         Technology} and {Oregon State University} and {Universidade 
                         Federal de Sergipe (UFSE)} and {Woods Hole Research Center} and 
                         {Woods Hole Research Center} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas da 
                         Amaz{\^o}nia (INPA)}",
                title = "Estimating aboveground biomass in tropical forests: Field methods 
                         and error analysis for the calibration of remote sensing 
                         observations",
              journal = "Remote Sensing",
                 year = "2017",
               volume = "9",
               number = "1",
             keywords = "Allometry, Amazon, Error propagation, Forest inventory, 
                         ICESat/GLAS, Uncertainty.",
             abstract = "Mapping and monitoring of forest carbon stocks across large areas 
                         in the tropics will necessarily rely on remote sensing approaches, 
                         which in turn depend on field estimates of biomass for calibration 
                         and validation purposes. Here, we used field plot data collected 
                         in a tropical moist forest in the central Amazon to gain a better 
                         understanding of the uncertainty associated with plot-level 
                         biomass estimates obtained specifically for the calibration of 
                         remote sensing measurements. In addition to accounting for sources 
                         of error that would be normally expected in conventional biomass 
                         estimates (e.g., measurement and allometric errors), we examined 
                         two sources of uncertainty that are specific to the calibration 
                         process and should be taken into account in most remote sensing 
                         studies: the error resulting from spatial disagreement between 
                         field and remote sensing measurements (i.e., co-location error), 
                         and the error introduced when accounting for temporal differences 
                         in data acquisition. We found that the overall uncertainty in the 
                         field biomass was typically 25% for both secondary and primary 
                         forests, but ranged from 16 to 53%. Co-location and temporal 
                         errors accounted for a large fraction of the total variance (<65%) 
                         and were identified as important targets for reducing uncertainty 
                         in studies relating tropical forest biomass to remotely sensed 
                         data. Although measurement and allometric errors were relatively 
                         unimportant when considered alone, combined they accounted for 
                         roughly 30% of the total variance on average and should not be 
                         ignored. Our results suggest that a thorough understanding of the 
                         sources of error associated with field-measured plot-level biomass 
                         estimates in tropical forests is critical to determine confidence 
                         in remote sensing estimates of carbon stocks and fluxes, and to 
                         develop strategies for reducing the overall uncertainty of remote 
                         sensing approaches.",
                  doi = "10.3390/rs9010047",
                  url = "http://dx.doi.org/10.3390/rs9010047",
                 issn = "2072-4292",
             language = "en",
           targetfile = "goncalves_estimating.pdf",
        urlaccessdate = "26 nov. 2020"
}


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