College of Behavioral & Social Sciences

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Author: search.filters.author.Duncanson, Laura

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    Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling
    (Wiley, 2023-04-04) Ma, Lei; Hurtt, George; Tang, Hao; Lamb, Rachel; Lister, Andrew; Chini, Louise; Dubayah, Ralph; Armston, John; Campbell, Elliott; Duncanson, Laura; Healey, Sean; O'Neil-Dunne, Jarlath; Ott, Lesley; Poulter, Benjamin; Shen, Quan
    Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.
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    Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA
    (Springer Nature, 2015-08-16) Huang, Wenli; Swatantran, Anu; Johnson, Kristofer; Duncanson, Laura; Tang, Hao; O’Neil Dunne, Jarlath; Hurtt, George; Dubayah, Ralph
    Continental-scale aboveground biomass maps are increasingly available, but their estimates vary widely, particularly at high resolution. A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making. To this end, we compare four continental-scale maps with a recent high-resolution lidar-derived biomass map over Maryland, USA. We conduct detailed comparisons at pixel-, county-, and state-level. Spatial patterns of biomass are broadly consistent in all maps, but there are large differences at fine scales (RMSD 48.5–92.7 Mg ha−1). Discrepancies reduce with aggregation and the agreement among products improves at the county level. However, continental scale maps exhibit residual negative biases in mean (33.0–54.6 Mg ha−1) and total biomass (3.5–5.8 Tg) when compared to the high-resolution lidar biomass map. Three of the four continental scale maps reach near-perfect agreement at ~4 km and onward but do not converge with the high-resolution biomass map even at county scale. At the State level, these maps underestimate biomass by 30–80 Tg in forested and 40–50 Tg in non-forested areas. Local discrepancies in continental scale biomass maps are caused by factors including data inputs, modeling approaches, forest/non-forest definitions and time lags. There is a net underestimation over high biomass forests and non-forested areas that could impact carbon accounting at all levels. Local, high-resolution lidar-derived biomass maps provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale maps produced in carbon monitoring systems.
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    Implications of allometric model selection for county-level biomass mapping
    (Springer Nature, 2017-10-18) Duncanson, Laura; Huang, Wenli; Johnson, Kristofer; Swatantran, Anu; McRoberts, Ronald E.; Dubayah, Ralph
    Carbon accounting in forests remains a large area of uncertainty in the global carbon cycle. Forest aboveground biomass is therefore an attribute of great interest for the forest management community, but the accuracy of aboveground biomass maps depends on the accuracy of the underlying field estimates used to calibrate models. These field estimates depend on the application of allometric models, which often have unknown and unreported uncertainties outside of the size class or environment in which they were developed. Here, we test three popular allometric approaches to field biomass estimation, and explore the implications of allometric model selection for county-level biomass mapping in Sonoma County, California. We test three allometric models: Jenkins et al. (For Sci 49(1): 12–35, 2003), Chojnacky et al. (Forestry 87(1): 129–151, 2014) and the US Forest Service’s Component Ratio Method (CRM). We found that Jenkins and Chojnacky models perform comparably, but that at both a field plot level and a total county level there was a ~ 20% difference between these estimates and the CRM estimates. Further, we show that discrepancies are greater in high biomass areas with high canopy covers and relatively moderate heights (25–45 m). The CRM models, although on average ~ 20% lower than Jenkins and Chojnacky, produce higher estimates in the tallest forests samples (> 60 m), while Jenkins generally produces higher estimates of biomass in forests < 50 m tall. Discrepancies do not continually increase with increasing forest height, suggesting that inclusion of height in allometric models is not primarily driving discrepancies. Models developed using all three allometric models underestimate high biomass and overestimate low biomass, as expected with random forest biomass modeling. However, these deviations were generally larger using the Jenkins and Chojnacky allometries, suggesting that the CRM approach may be more appropriate for biomass mapping with lidar. These results confirm that allometric model selection considerably impacts biomass maps and estimates, and that allometric model errors remain poorly understood. Our findings that allometric model discrepancies are not explained by lidar heights suggests that allometric model form does not drive these discrepancies. A better understanding of the sources of allometric model errors, particularly in high biomass systems, is essential for improved forest biomass mapping.
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    LINKING ALLOMETRIC SCALING THEORY WITH LIDAR REMOTE SENSING FOR IMPROVED BIOMASS ESTIMATION AND ECOSYSTEM CHARACTERIZATION
    (2015) Duncanson, Laura; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Accurate quantification of forest carbon stocks and fluxes is critical for the successful modeling and mitigation of climate change. This research focuses on forest carbon stock quantification, both in terms of testing emerging remote sensing approaches to forest carbon modeling, and examining allometric equations used to estimate biomass stocks in field plots. First, we test controversial theoretical predictions of forest allometry through the mapping of the allometric variability using field plots across the U.S. we find that there is considerable variability in forest allometry across space, largely driven by local environment and life history. However, in tall forests, allometries tend to converge toward theoretical predictions, suggesting that theory may be a useful constraint on allometry in certain forests. Second, we shift to an analysis of empirical allometries by developing an algorithm to extract individual crown information from forest systems and using it for biomass mapping and allometric equation testing. Third, we test whether individual tree structure bolsters biomass modeling capabilities in comparison to tradition, plot-aggregated LiDAR metrics. As part of this analysis we also test an allometric scaling-based approach to biomass mapping. We find that individual tree-level structure only improves biomass models when there is considerable spatial heterogeneity in the forest. Also, allometric scaling-based only worked in one study site, and failed in the other two sites because there was little or no relationship between basal area and maximum canopy height. Finally, we applied LiDAR datasets to an analysis of the effects of sample size on empirical allometry development. We found that small samples sizes tend to result in an under sampling of large stems, which yields a more linear fit than the true allometry. An assessment of the potential carbon implications of this problem yielded site-level biomass predictions with biases of 10-178%. We suggest that empirical allometric equations developed on small sample sizes, as applied in the U.S., yield potentially large errors in biomass and therefore require careful reassessment. In combination with our findings regarding the spatial variability of forest allometry, we believe that the limiting factor to forest carbon estimation is the use of allometric equations.