Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Now showing 1 - 4 of 4
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    Advanced Modeling Using Land-use History and Remote Sensing to Improve Projections of Terrestrial Carbon Dynamics
    (2021) Ma, Lei; Hurtt, George; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Quantifying, 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.
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    Environmental Federalism: Chinese Governmental Behaviors in Pollution Regulations
    (2019) Yan, Youpei; Lichtenberg, Erik; Agricultural and Resource Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    China's economic growth has come with the cost of environmental deterioration. The economy has faced with many problems in land resource depletion and industrial pollution. I examine two policies that tackle three major environmental aspects on land, water, and air in China. All three chapters share the theme that devolution without enough oversights in environmental policies has lead to unintended consequences in practice, as local officials have their trade-offs to promote local economy and protect environment. The first chapter explores the local government's behavior in a land conservation program, which intends to reduce soil erosion by subsidizing afforestation of low productive farmland on steep slopes. Theoretically, the incentives created by the program combined with insufficient oversight have led to afforestation of highly productive farmland on level ground. With a unique land transition dataset, I show that this unintended land use effect has been substantial. This unexpected displacement of highly productive farmland represents a form of leakage that has not been fully explored in the literature. And it is problematic to a country with limited arable land relative to population size as it can negatively impact national food production targets and self-sufficiency goals. The second chapter investigates water pollution activities under China's Pollution Reduction Mandates. In response to the substantial environmental deterioration, the central government taxes firm emissions and subsidizes abatement technology installation. In theory, devolution to local governments to lower pollution and promote economic growth can create local incentives to allocate subsidies to effectively export pollution. I provide the first evidence of the magnitude of these distortions with unique firm-level pollution panel data and find evidence of water pollution exported to downstream and further away from local residences. A simulation indicates that the distortions created by local jurisdictional control harm the environment substantially: centralized allocation of subsidies could reduce total emissions by 20-30%. The third chapter keeps investigating the inter-jurisdictional pollution externalities on air pollution under the same mandates. It provides a complimentary evidence to show that local governments have incentives to promote spatial spillovers and free-ride on the downwind neighbors.
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    THE INFLUENCE OF URBAN FORM AT DIFFERENT GEOGRAPHICAL SCALES ON TRAVEL BEHAVIOR; EVIDENCE FROM U.S. CITIES
    (2016) Nasri, Arefeh; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Suburban lifestyle is popular among American families, although it has been criticized for encouraging automobile use through longer commutes, causing heavy traffic congestion, and destroying open spaces (Handy, 2005). It is a serious concern that people living in low-density suburban areas suffer from high automobile dependency and lower rates of daily physical activity, both of which result in social, environmental and health-related costs. In response to such concerns, researchers have investigated the inter-relationships between urban land-use pattern and travel behavior within the last few decades and suggested that land-use planning can play a significant role in changing travel behavior in the long-term. However, debates regarding the magnitude and efficiency of the effects of land-use on travel patterns have been contentious over the years. Changes in built-environment patterns is potentially considered a long-term panacea for automobile dependency and traffic congestion, despite some researchers arguing that the effects of land-use on travel behavior are minor, if any. It is still not clear why the estimated impact is different in urban areas and how effective a proposed land-use change/policy is in changing certain travel behavior. This knowledge gap has made it difficult for decision-makers to evaluate land-use plans and policies. In addition, little is known about the influence of the large-scale built environment. In the present dissertation, advanced spatial-statistical tools have been employed to better understand and analyze these impacts at different scales, along with analyzing transit-oriented development policy at both small and large scales. The objective of this research is to: (1) develop scalable and consistent measures of the overall physical form of metropolitan areas; (2) re-examine the effects of built-environment factors at different hierarchical scales on travel behavior, and, in particular, on vehicle miles traveled (VMT) and car ownership; and (3) investigate the effects of transit-oriented development on travel behavior. The findings show that changes in built-environment at both local and regional levels could be very influential in changing travel behavior. Specifically, the promotion of compact, mixed-use built environment with well-connected street networks reduces VMT and car ownership, resulting in less traffic congestion, air pollution, and energy consumption.
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    SHORT TERM TRAVEL BEHAVIOR PREDICTION THROUGH GPS AND LAND USE DATA
    (2015) Krause, Cory; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The short-term destination prediction problem consists of capturing vehicle Global Positioning System (GPS) traces and learning from historic locations and trajectories to predict a vehicle’s destination. Drivers have predictable trip destinations that can be estimated through probabilistic modeling of past trips. This dissertation has three main hypotheses; 1) Employing a tiered Markov model structure will permit a shorter learning period while achieving similar accuracy results, 2) The addition of derived trip purpose information will increase accuracy of the start of trip and in-route models as a whole, and 3) Similar methodologies of travel pattern inference can be used to accurately predict trip purpose and socio-economic factors. To study these concepts, a database of GPS driving traces (120 participants for 70 days) is collected. To model the user’s trip purpose, a new data source was explored: Point of Interest (POI)/land use data. An open source land use/POI dataset is merged with the GPS dataset. The resulting database includes over 20,000 trips with travel characteristics and land use/POI data. From land use/POI data, and travel patterns, trip purpose is calculated with machine learning methods. A new model structure is developed that uses trip purpose when it is available, yet falls back on traditional spatial temporal Markov models when it is not. The start of trip model has an overall increase of accuracy over other start of trip models of 2%. This comes quickly, needing only 30 days to reach this level of accuracy compared to nearly a year in many other models. When adding trip purpose and the start of trip model to in-route prediction methods, the accuracy of the destination prediction increases significantly: 15-30% improvement of accuracy over similar models between 0-50% of trip progression. Certain trips are predicted more accurately than others: work and home based trips average of 90% correct prediction, whereas shopping and social based trips hover around the 50% mark. In all, the greatest contribution of this dissertation is the trip purpose methodology addition and the tiered Markov model structure in gaining fast results in both the start of trip and in-route models.