TY - JOUR
T1 - Integrating field sampling, spatial statistics and remote sensing to map wetland vegetation in the Pantanal, Brazil
AU - Arieira, J.
AU - Karssenberg, D.
AU - de Jong, S. M.
AU - Addink, E. A.
AU - Couto, E. G.
AU - Nunes da Cunha, C.
AU - Skøien, J. O.
PY - 2010/9/1
Y1 - 2010/9/1
N2 - To improve the protection of wetlands, it is imperative to have a
thorough understanding of their structuring elements and of the
identification of efficient methods to describe and monitor them. This
article uses sophisticated statistical classification, interpolation and
error propagation techniques, in order to describe vegetation spatial
patterns, map plant community distribution and evaluate the capability
of statistical approaches to produce high-quality vegetation maps. The
approach results in seven vegetation communities with a known floral
composition that can be mapped over large areas using remotely sensed
data. The relations between remotely sensing data and vegetation
patterns, captured in four factorial axes, were formalized
mathematically in multiple linear regression models and used in a
universal kriging procedure to reduce the uncertainty in mapped
communities. Universal kriging has shown to be a valuable interpolation
technique because parts of vegetation variability not explained by the
images could be modeled as spatially correlated residuals, increasing
prediction accuracy. Differences in spatial dependence of the vegetation
gradients evidenced the multi-scale nature of vegetation communities.
Cross validation procedures and Monte Carlo simulations were used to
quantify the uncertainty in the resulting map. Cross-validation showed
that accuracy in classification varies according with the community
type, as a result of sampling density and configuration. A map of
uncertainty resulted from Monte Carlo simulations displayed the spatial
variation in classification accuracy, showing that the quality of
classification varies spatially, even though the proportion and
arrangement of communities observed in the original map is preserved to
a great extent. These results suggested that mapping improvement could
be achieved by increasing the number of field observations of those
communities with a scattered and small patch size distribution; or by
including new digital images as explanatory variables in the model. By
comparing the resulting plant community map with a flood duration map,
we verified that flooding duration is an important driver of vegetation
zonation. We discuss our study in the context of developing a mapping
approach that is able to integrate field point data and high-resolution
remote sensing images, providing new basis to map wetland vegetation and
allowing its future application in habitat management, conservation
assessment and long-term ecological monitoring in wetland landscapes.
AB - To improve the protection of wetlands, it is imperative to have a
thorough understanding of their structuring elements and of the
identification of efficient methods to describe and monitor them. This
article uses sophisticated statistical classification, interpolation and
error propagation techniques, in order to describe vegetation spatial
patterns, map plant community distribution and evaluate the capability
of statistical approaches to produce high-quality vegetation maps. The
approach results in seven vegetation communities with a known floral
composition that can be mapped over large areas using remotely sensed
data. The relations between remotely sensing data and vegetation
patterns, captured in four factorial axes, were formalized
mathematically in multiple linear regression models and used in a
universal kriging procedure to reduce the uncertainty in mapped
communities. Universal kriging has shown to be a valuable interpolation
technique because parts of vegetation variability not explained by the
images could be modeled as spatially correlated residuals, increasing
prediction accuracy. Differences in spatial dependence of the vegetation
gradients evidenced the multi-scale nature of vegetation communities.
Cross validation procedures and Monte Carlo simulations were used to
quantify the uncertainty in the resulting map. Cross-validation showed
that accuracy in classification varies according with the community
type, as a result of sampling density and configuration. A map of
uncertainty resulted from Monte Carlo simulations displayed the spatial
variation in classification accuracy, showing that the quality of
classification varies spatially, even though the proportion and
arrangement of communities observed in the original map is preserved to
a great extent. These results suggested that mapping improvement could
be achieved by increasing the number of field observations of those
communities with a scattered and small patch size distribution; or by
including new digital images as explanatory variables in the model. By
comparing the resulting plant community map with a flood duration map,
we verified that flooding duration is an important driver of vegetation
zonation. We discuss our study in the context of developing a mapping
approach that is able to integrate field point data and high-resolution
remote sensing images, providing new basis to map wetland vegetation and
allowing its future application in habitat management, conservation
assessment and long-term ecological monitoring in wetland landscapes.
U2 - 10.5194/bgd-7-6889-2010
DO - 10.5194/bgd-7-6889-2010
M3 - Article
SN - 1810-6277
VL - 7
SP - 6889
EP - 6934
JO - Biogeosciences Discussions
JF - Biogeosciences Discussions
IS - 5
ER -