Advanced Modeling Using Land-use History and Remote Sensing to Improve Projections of Terrestrial Carbon Dynamics

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dc.contributor.advisorHurtt, Georgeen_US
dc.contributor.authorMa, Leien_US
dc.contributor.departmentGeographyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2021-07-07T05:47:26Z
dc.date.available2021-07-07T05:47:26Z
dc.date.issued2021en_US
dc.description.abstractQuantifying, attributing, and projecting terrestrial carbon dynamics can provide valuable information in support of climate mitigation policy to limit global warming to 1.5 °C. Current modeling efforts still involve considerable uncertainties, due in part to knowledge gaps regarding efficient and accurate scaling of individual-scale ecological processes to large-scale dynamics and contemporary ecosystem conditions (e.g., successional states and carbon storage), which present strong spatial heterogeneity. To address these gaps, this research aims to leverage decadal advances in land-use modeling, remote sensing, and ecosystem modeling to improve the projection of terrestrial carbon dynamics at various temporal and spatial scales. Specifically, this research examines the role of land-use modeling and lidar observations in determining contemporary ecosystem conditions, especially in forest, using the latest land-use change dataset, developed as the standard forcing for CMIP6, and observations from both airborne lidar and two state-of-the-art NASA spaceborne lidarmissions, GEDI and ICESat-2. Both land-use change dataset and lidar observations are used to initialize a newly developed global version of the ecosystem demography (ED) model, an individual-based forest model with unique capabilities to characterize fine-scale processes and efficiently scale them to larger dynamics. Evaluations against multiple benchmarking datasets suggest that the incorporation of land-use modeling into the ED model can reproduce the observed spatial pattern of vegetation distribution, carbon dynamics, and forest structure as well as the temporal dynamics in carbon fluxes in response to climate change, increased CO2, and land-use change. Further, the incorporation of lidar observations into ED, largely enhances the model’s ability to characterize carbon dynamics at fine spatial resolutions (e.g., 90 m and 1 km). Combining global ED model, land-use modeling and lidar observation together can has great potential to improve projections of future terrestrial carbon dynamics in response to climate change and land-use change.en_US
dc.identifierhttps://doi.org/10.13016/jwzo-23dg
dc.identifier.urihttp://hdl.handle.net/1903/27310
dc.language.isoenen_US
dc.subject.pqcontrolledEnvironmental scienceen_US
dc.subject.pqcontrolledClimate changeen_US
dc.subject.pqcontrolledBiogeochemistryen_US
dc.subject.pquncontrolledCarbon Cycleen_US
dc.subject.pquncontrolledCarbon Sequestrationen_US
dc.subject.pquncontrolledEcosystem Demographyen_US
dc.subject.pquncontrolledEcosystem Modelingen_US
dc.subject.pquncontrolledLand Useen_US
dc.subject.pquncontrolledLidaren_US
dc.titleAdvanced Modeling Using Land-use History and Remote Sensing to Improve Projections of Terrestrial Carbon Dynamicsen_US
dc.typeDissertationen_US

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