55 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 (8â50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass (AGB) at spatial grains ranging from 5 to 250m (0.025â6.25 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 âdilutionâ 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
The global abundance of tree palms
Aim Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change. Location Tropical and subtropical moist forests. Time period Current. Major taxa studied Palms (Arecaceae). Methods We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., â„10 cm diameter at breast height) abundance relative to coâoccurring nonâpalm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of longâterm climate stability. Lifeâform diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many nonâtree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of aboveâground biomass, but the magnitude and direction of the effect require additional work. Conclusions Tree palms are not only quintessentially tropical, but they are also overwhelmingly Neotropical. Future work to understand the contributions of tree palms to biomass estimates and carbon cycling will be particularly crucial in Neotropical forests
The global abundance of tree palms
Aim: Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change.
Location: Tropical and subtropical moist forests.
Time period: Current.
Major taxa studied: Palms (Arecaceae).
Methods: We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., â„10 cm diameter at breast height) abundance relative to coâoccurring nonâpalm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure.
Results: On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of longâterm climate stability. Lifeâform diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many nonâtree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of aboveâground biomass, but the magnitude and direction of the effect require additional work.
Conclusions: Tree palms are not only quintessentially tropical, but they are also overwhelmingly Neotropical. Future work to understand the contributions of tree palms to biomass estimates and carbon cycling will be particularly crucial in Neotropical forests
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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 Mghaâ»Âč) 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 âdilutionâ 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
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