10 research outputs found
Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks
Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+. Though broad scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass (AGB) at spatial grains ranging from 5 to 250m (0.025–6.25 ha), and we evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that the spatial sampling error in AGB is large for standard plot sizes, averaging 46.3% for 0.1 ha subplots and 16.6% for 1 ha subplots. Topographically heterogeneous sites showed positive spatial autocorrelation in AGB at scales of 100m and above; at smaller scales, most study sites showed negative or nonexistent spatial autocorrelation in AGB. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGB leads to a substantial “dilution” bias in calibration parameters, a bias that cannot be removed with current statistical methods. Overall, our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise
Recommended from our members
Rate of tree carbon accumulation increases continuously with tree size
Forests are major components of the global carbon cycle, providing
substantial feedback to atmospheric greenhouse gas concentrations¹.
Our ability to understand and predict changes in the forest carbon
cycle—particularly net primary productivity and carbon storage—increasingly relies on models that represent biological processes
across several scales of biological organization, from tree leaves to
forest stands[superscript 2,3]. Yet, despite advances in our understanding of productivity
at the scales of leaves and stands, no consensus exists about
the nature of productivity at the scale of the individual tree[superscript 4–7], in
part because we lack a broad empirical assessment of whether rates
of absolute tree mass growth (and thus carbon accumulation) decrease,
remain constant, or increase as trees increase in size and age. Here we
present a global analysis of 403 tropical and temperate tree species,
showing that for most species mass growth rate increases continuously
with tree size. Thus, large, old trees do not act simply as senescent
carbon reservoirs but actively fix large amounts of carbon
compared to smaller trees; at the extreme, a single big tree can add
the same amount of carbon to the forest within a year as is contained
in an entire mid-sized tree. The apparent paradoxes of individual
tree growth increasing with tree size despite declining leaf-level[superscript 8–10]
and stand-level¹⁰ productivity can be explained, respectively, by
increases in a tree’s total leaf area that outpace declines in productivity
per unit of leaf area and, among other factors, age-related
reductions in population density. Our results resolve conflicting
assumptions about the nature of tree growth, inform efforts to understand
and model forest carbon dynamics, and have additional implications
for theories of resource allocation¹¹ and plant senescence¹²
Recommended from our members
Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks
Advances in forest carbon mapping have the potential
to greatly reduce uncertainties in the global carbon
budget and to facilitate effective emissions mitigation strategies
such as REDDC (Reducing Emissions from Deforestation
and Forest Degradation). Though broad-scale mapping
is based primarily on remote sensing data, the accuracy of
resulting forest carbon stock estimates depends critically on
the quality of field measurements and calibration procedures.
The mismatch in spatial scales between field inventory plots
and larger pixels of current and planned remote sensing products
for forest biomass mapping is of particular concern, as
it has the potential to introduce errors, especially if forest
biomass shows strong local spatial variation. Here, we used
30 large (8-50 ha) globally distributed permanent forest plots
to quantify the spatial variability in aboveground biomass
density (AGBD in Mgha⁻¹) at spatial scales ranging from
5 to 250 m (0.025-6.25 ha), and to evaluate the implications
of this variability for calibrating remote sensing products using
simulated remote sensing footprints. We found that local
spatial variability in AGBD is large for standard plot sizes,
averaging 46.3% for replicate 0.1 ha subplots within a single
large plot, and 16.6% for 1 ha subplots. AGBD showed
weak spatial autocorrelation at distances of 20-400 m, with
autocorrelation higher in sites with higher topographic variability
and statistically significant in half of the sites. We further
show that when field calibration plots are smaller than
the remote sensing pixels, the high local spatial variability in
AGBD leads to a substantial “dilution” bias in calibration parameters,
a bias that cannot be removed with standard statistical
methods. Our results suggest that topography should be
explicitly accounted for in future sampling strategies and that
much care must be taken in designing calibration schemes if
remote sensing of forest carbon is to achieve its promise
Individuals who do and do not perceive difficulties adhering to a diet for diabetes mellitus, their quality of life and glycaemic control
Opinion regarding the successful management of insulin dependent diabetes mellitus (IDDM) has identified nutrition as a key player. Whilst important, diet has also been highlighted as one of the most difficult aspects of the regimen, by both individuals with IDDM and health workers. Current dietetic recommendations for the nutritional management of individuals with IDDM include, the normalisation of plasma glucose and the promotion of patient well being. This study aimed to determine if any significant difference in quality of life (QOL) and glycaemic control existed between groups of individuals with IDDM, who perceive their diet difficult to adhere to and those who perceive adherence easy
Recommended from our members
HarmonMarkForestryRateTreeCarbon.pdf
Forests are major components of the global carbon cycle, providing
substantial feedback to atmospheric greenhouse gas concentrations¹.
Our ability to understand and predict changes in the forest carbon
cycle—particularly net primary productivity and carbon storage—increasingly relies on models that represent biological processes
across several scales of biological organization, from tree leaves to
forest stands[superscript 2,3]. Yet, despite advances in our understanding of productivity
at the scales of leaves and stands, no consensus exists about
the nature of productivity at the scale of the individual tree[superscript 4–7], in
part because we lack a broad empirical assessment of whether rates
of absolute tree mass growth (and thus carbon accumulation) decrease,
remain constant, or increase as trees increase in size and age. Here we
present a global analysis of 403 tropical and temperate tree species,
showing that for most species mass growth rate increases continuously
with tree size. Thus, large, old trees do not act simply as senescent
carbon reservoirs but actively fix large amounts of carbon
compared to smaller trees; at the extreme, a single big tree can add
the same amount of carbon to the forest within a year as is contained
in an entire mid-sized tree. The apparent paradoxes of individual
tree growth increasing with tree size despite declining leaf-level[superscript 8–10]
and stand-level¹⁰ productivity can be explained, respectively, by
increases in a tree’s total leaf area that outpace declines in productivity
per unit of leaf area and, among other factors, age-related
reductions in population density. Our results resolve conflicting
assumptions about the nature of tree growth, inform efforts to understand
and model forest carbon dynamics, and have additional implications
for theories of resource allocation¹¹ and plant senescence¹²
Recommended from our members
HarmonMarkForestryRateTreeCarbon_SupplementaryInformation.pdf
Forests are major components of the global carbon cycle, providing
substantial feedback to atmospheric greenhouse gas concentrations¹.
Our ability to understand and predict changes in the forest carbon
cycle—particularly net primary productivity and carbon storage—increasingly relies on models that represent biological processes
across several scales of biological organization, from tree leaves to
forest stands[superscript 2,3]. Yet, despite advances in our understanding of productivity
at the scales of leaves and stands, no consensus exists about
the nature of productivity at the scale of the individual tree[superscript 4–7], in
part because we lack a broad empirical assessment of whether rates
of absolute tree mass growth (and thus carbon accumulation) decrease,
remain constant, or increase as trees increase in size and age. Here we
present a global analysis of 403 tropical and temperate tree species,
showing that for most species mass growth rate increases continuously
with tree size. Thus, large, old trees do not act simply as senescent
carbon reservoirs but actively fix large amounts of carbon
compared to smaller trees; at the extreme, a single big tree can add
the same amount of carbon to the forest within a year as is contained
in an entire mid-sized tree. The apparent paradoxes of individual
tree growth increasing with tree size despite declining leaf-level[superscript 8–10]
and stand-level¹⁰ productivity can be explained, respectively, by
increases in a tree’s total leaf area that outpace declines in productivity
per unit of leaf area and, among other factors, age-related
reductions in population density. Our results resolve conflicting
assumptions about the nature of tree growth, inform efforts to understand
and model forest carbon dynamics, and have additional implications
for theories of resource allocation¹¹ and plant senescence¹²
Influence of climatic conditions, topography and soil attributes on the spatial distribution of site productivity index of the species rich forests of Jalisco, Mexico
Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks
Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha-1) at spatial scales ranging from 5 to 250 m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise. © Author(s) 2014