@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 = "13 maio 2024"
}