Geography Theses and Dissertations

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    Land Use in Charles County
    (1962) Langen, John S.; Van Royen, W.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, MD)
    The land use of Charles County does not basically differ from that in the past. Land in forest and land in farms are the two categories of land utilization. The great demand for tobacco on the overseas markets in the early days of the county's history, led to the introduction of this crop. Because of the favorable climate and soils, tobacco became soon the mainstay of the county's economy, a situation which still exists today. The purpose of the thesis was to determine which geographical factors and others accounted for the use of the land. In addition to field work, use was made of detailed statistical data. It was found, that the county could be divided into three sections. In the western section, land in forest was the dominating land use form. In the central section, land in forest and land in farms were about equal in areal extent, whereas in the eastern section, land in farms dominated. The reason was that soils in the western part became exhausted, and a shift to the eastern section took place. Landforms contributed much to the distribution of land in crops, especially for tobacco. Recently, a change in the use of the land is taking place. The encroachment of the Washington Metropolitan area, and the building of a major highway, connecting the North with the South, have induced farmers to sell their lands, which are converted into residential areas.
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    (2023) KHAN, MOHAMMAD ABDUL QADIR; Skakun, Sergii; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The effectiveness of remote sensing-based supervised classification models in crop type mapping and area estimation is contingent upon the availability of sufficient and high-quality calibration or training data. The current challenge lies in the absence of field-level crop labels, impeding the advancement of training supervised classification models. To address the needs of operational crop monitoring there is a pressing demand for the development of generalized classification models applicable for various agricultural areas and across different years, even in the absence of calibration data. This dissertation aims to explore the potential of the C-band Sentinel-1 Synthetic Aperture Radar (SAR) capabilities for building generalized crop type models with a specific focus on identifying and monitoring sunflower crop in Eastern Europe. Globally, the sunflower ranks as the fourth most important oilseed crop and stands out as the most profitable and economically significant oilseed crop. It is extensively cultivated for the production of vegetable oil, biodiesel, and animal feed with Ukraine and Russia as the largest producer and exporter in the world. In the first step, this study explores the interaction of Sentinel-1 (S1) SAR signal with sunflower to identify and monitor phenological stages of sunflower. The analysis examines SAR backscattering coefficients and polarizations in Vertical-Horizontal (VH), Vertical-Vertical (VV) and VH/VV ratio, highlighting differences between ascending and descending orbits due to sunflower directional behavior caused by heliotropism. Based on the unique SAR-based signature of sunflower the study introduces a generalized model for sunflower identification and mapping which is applicable across time and space. It was observed that the model based on features acquired from S1-based descending orbits outperforms the one based on ascending orbit because of the sunflower’s directional behavior: user’s accuracy (UA) of 96%, producer’s accuracy (PA) of 97% and F-score of 97% (descending) compared to UA of 90%, PA of 89% and F-score of 90% (ascending). This model was generalized and validated for selected sites in Ukraine, France, Hungary, Russia and USA. When the model is generalized to other years and regions it yields an F-score of > 77% for all cases, with F-score being the highest (>91%) for Mykolaiv region in Ukraine. The generalized approach to map sunflower was applied to assess the impact of the Russian full-scale invasion of Ukraine on national sunflower planted areas. The sunflower planted areas and corresponding changes in 2021 and 2022 were estimated using a sample-based approach for area estimation. Sunflower area was estimated at 7.10±0.45 million hectares (Mha) in 2021 which was further reduced to 6.75±0.45 Mha in 2022 representing a 5% decrease. The findings suggest spatial shifts in sunflower cultivation after the Russian invasion from southern/south-eastern Ukraine under Russian controlled to south-central region under Ukrainian control. The first objective of this dissertation highlights the difference of ascending and descending S1 orbits for sunflower monitoring due to its directional behavior, an aspect not fully researched and documented previously. The implemented generalized approach based on sunflower phenology proves to be an accurate and space-time generalized classifier, reducing time, cost and resources for operational sunflower mapping for large areas. Also, the disparity between sample-based area estimates and SAR-based mapped areas caused due to speckle were substantially reduced emphasizing the role of S1/SAR in global food security monitoring.
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    (2023) Jantz, Samuel M; Hansen, Matthew C; Geography/Library & Information Systems; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    All four sub-species of our closest living relative, the chimpanzee, are listed as endangered by the International Union for the Conservation of Nature (IUCN), and their populations continue to decline due to human activities. Effective conservation efforts require information on their population status and distribution. Traditional field surveys are expensive and impractical for covering large areas at regular time intervals, making it difficult to track population trends. Given that chimpanzees occupy a large range (2.3 x 106 km2), new cost-effective methods and data are needed to provide relevant information on population status and trends across large geographic and time scales. The objective of this dissertation is to help fill this gap by leveraging freely available and regularly updated remotely sensed datasets to map and monitor chimpanzee habitat across their range. This research begins by first producing annual forest cover and change maps for the Greater Gombe (GGE) and Greater Mahale ecosystems (GME) in western Tanzania using Landsat phenological metrics and machine learning methods. Canopy cover was predicted at 30-meter resolution and the Cumulative Sums (CuSum) algorithm was applied to the canopy cover time series to detect forest loss and gain events between 2000-2020. An accuracy assessment showed the CuSum algorithm was able to detect forest loss well but had more difficulty detecting gradual forest gain events. A total of 276,000 ha (+/- 27,000 ha) of gross forest loss was detected between 2000 and 2020 in the GGE and GME; however, loss was not spread equally among the two ecosystems. The results show widespread forest loss in the GME, contrasted with net forest cover gain in the GGE. Next, the annual forest cover maps, and additional derived variables, were used to train an ensemble model to predict the relative encounter rate of chimpanzee nest sightings in the GGE and GME. Model output exhibited a strong linear relationship to chimpanzee abundances and population density estimated from a recent ground survey, enabling model output to be linearly transformed into population estimates. The model predicted the two ecosystems harbor just over 3,000 individuals, which agrees with the upper limit of population estimates from ground surveys. Most importantly, the model can be applied to annually updated variables enabling the detection of potential population shifts caused by changes in landscape condition. Model output indicates a possible population reduction in portions of the GME, while the GGE is predicted to have increased its ability to sustain a larger population. Finally, Random Forests regression was used to relate predictor variables, primarily derived from Landsat data to a coarse resolution, range-wide habitat suitability map enabling the prediction of habitat suitability at 30 meter resolution. The model showed good agreement with the calibration data; however, there was considerable variation in predictive capability among the four chimpanzee sub-species. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. The model can detect changes in suitability to support monitoring and conservation planning across the chimpanzee range. Results from this dissertation highlight the promise of integrating continuously updated satellite data into habitat suitability models to detect changes through time and inform conservation efforts for chimpanzees at multiple scales.
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    Spatiotemporal Analysis of Vehicle Mobility Patterns using Machine Learning Approaches
    (2023) Zhu, Guimin; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Vehicle mobility is important to a diverse range of disciplines, e.g., geography, transportation, and public health. Machine Learning algorithms have been applied in geospatial analysis related to vehicle mobility and travel pattern research, which provided researchers with more flexibility and capabilities for complex mobility pattern analyses. This dissertation aims to explore how different Machine Learning models (e.g., regression and clustering) can be applied to enhance the interpretability of vehicle mobility patterns by conducting explanatory analyses on factors that may impact different mobility patterns (i.e., trip volume changes and travel times) over space and time (e.g., different stages of the COVID-19 Pandemic at regional and nationwide scales). In this dissertation, three studies were undertaken to investigate the spatiotemporal trends of vehicle trip changes and travel behaviors, using passively-collected mobile device data. The first study examined mobility patterns over different time periods during the summer 2020 when COVID-19 cases were spiking in Florida(locations with large numbers of vulnerable individuals) and analyzed a set of underlying drivers for mobility and how these factors changed over time using Machine Learning approaches. The second study investigated changing mobility patterns across the U.S. during 2021 when COVID-19 vaccinations were becoming available to understand whether changing vaccination rates led to a change in the rate of trips using Machine Learning clustering methods. The third study investigated reasons impacting travel times for two origin-destination pairs using a Machine Learning approach to better understand how different factors can affect travel times over different trip purposes and different trip lengths in Maryland. The contributions of this dissertation are that it provided new insights into how different types of mobility patterns evolved over space and time, especially during a major public health crisis, and the results are useful for policy and planning implications for local and regional officials, e.g., mobility restriction measurements, decision support for economic recovery, and public health strategies. The integration of diverse data sources (e.g., passively-collected mobility data and other mobility data from different public and private sources) and the utilization of multiple Machine Learning models enhanced the interpretability of vehicle mobility patterns.
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    Land Tenure, Property Ownership, and Home Mortgages in the Late Nineteenth Century: A Case Study of Baltimore's Germans
    (1976) Vill, Martha J.; Groves, Paul A.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, MD)
    During the late nineteenth century the rapidly expanding urban population of the United States created an increased demand for housing. At the same time, mortgage money for the finance of home purchases was in short supply because of the availability of more lucrative investment opportunities elsewhere and because there were legal restrictions on the power of banks to lend money on real estate . Recent literature has emphasized the importance of property ownership among different components of the population, including immigrant groups. Little attention has been paid to the process of property acquisition or to the patterns of land tenure which resulted. An immigrant population, handicapped in numerous ways, was likely to have limited access to available mortgage financing, thereby limiting its ability to purchase property. Yet, the literature suggests that immigrants actively acquired property. This study examines some preliminary ideas about tenure patterns and home mortgages within immigrant residential areas, using a sample of Baltimore's Germans as a case study. The argument presented is that housing acquisition was facilitated by the activities of the immigrants themselves. In view of the restrictions on the supply of mortgage money, financing for property purchases had to come from sources independent of the city's major financial institutions, and the immigrants had to generate their own sources of capital. It was expected that tenants and landlords would have common national origins, another reflection of the immigrants' reliance on members of their own group for housing. Another expectation of the study was that Germans of different origins in Germany would exhibit different tenure patterns. Arguing that the term "German" was an imprecise indicator of national origins, and that the residential patterns of immigrants from different parts of Germany were distinct, it was expected that this diversity would also find expression in tenure patterns. The selection of the sample areas in the study was, therefore, conditioned by the need to isolate areas inhabited by Germans of diverse origins. Land tenure, property ownership, and relationships between landlords and tenants were analyzed. The hoped for differences in rates of property ownership did not materialize, and home ownership was not systematically related to age, income, or family employment. The findings do indicate, however, that home ownership was within the grasp of people with relatively low income. The mechanism which enabled home purchasers to obtain mortgages was the building and loan associations which were organized and directed by men whose origins, occupations, and residences reflected those of the associations' clientele. Thus, the hypothesis that immigrants generated their own mortgage funds was confirmed. The findings of the study concerning landlords and tenants further substantiate the argument that the provision of housing was accomplished by the immigrants themselves. Landlords' residences were close to the properties they rented, and there was a marked tendency for tenants to rent from landlords who shared their German origins.
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    Quantifying the impact of remotely sensed photosynthetically active radiation retrievals on empirical crop models in the United States
    (2023) Brown, Meredith Guenevere Longshore; Skakun, Sergii; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Photosynthetically active radiation (PAR), is an essential component for life onEarth and one of the essential climate variables. Due to the differences in biochemistry, cell structure, and photosynthetic pathways, different plant species absorb PAR with varying efficiency and have evolved to thrive in different conditions, such as direct, intense sunlight or indirect, diffuse light conditions. Ground-based measurements allow for direct estimation of PAR; however, those are available in select locations, e.g. through the Surface Radiation Budget (SURFRAD) Network. Remote sensing-based methods, on the other hand, enable spatially explicit estimates of PAR on a regular basis. Current methods and models for satellite-based PAR retrievals require many ancillary atmospheric datasets as well as a large computing infrastructure. PAR, as one of the parameters influencing plant productivity, has not been previously used in the empirical crop yields and as such can lead to better satellite-based yield estimates. Having the advantages of spatially explicit PAR estimates, spatial and temporal patterns of the PAR can reveal differences in the land uses and the level of crop productivity. Therefore, the overarching goal of my dissertation is to advance the science of satellite-based PAR estimation and agricultural applications. This is done through the use of machine-learning models to reduce data input requirements for PAR estimation from daily Moderate Resolution Imaging Spectroradiometer (MODIS) acquisitions and by incorporating PAR into the empirical crop yield models over the US. In order to obtain satellite-based PAR estimates without the need for ancillary atmospheric data, I developed an empirical approach making use of machine learning methods as an efficient way to capture the non-linear relationship between top of atmosphere radiance and PAR at the surface. I found that the bootstrap aggregated decision tree (Bagged Tree), Gaussian Process Regression (GPR), and Multilayer Perceptron (MLP) yielded the best results with minimal input and training data requirements with an R2 of 0.77, 0.78, and 0.78 respectively, and a relative RMSE of 22-23%. While these results underperform compared with the look up table (LUT) approach, it does not require the same atmospheric parameters as input, such as atmospheric water vapor, aerosol optical depth, and others that might not be available in near real time or are only available at coarser spatial resolution. I incorporated MODIS-based PAR estimates into empirical corn and soybean yield models over the US. By explicitly adding PAR into the crop yield models, I found a maximum R2 of 0.81 and 0.80 for corn and soybean, respectively, whereas models that do not include PAR showed a maximum R2 of 0.60 for corn and soybean. By adding PAR directly into the empirical yield model and demonstrating additional explained variability, I show that my model is in closer agreement with process-based models than previous empirical models. I found that MODIS- derived coefficient of absorption of PAR (αPAR), which corresponds to the plant canopy chlorophyll content (CCC) and consequently productivity, corresponds to the ground-based αPAR measurements. Specifically, I found that for the US-Ne sites of corn and soybean fields in Eastern Nebraska R2 was 0.97 and RMSE was 1.34 (11%) when comparing MODIS-derived αPAR with the in situ measurements. I also found that the relationships between MODIS-based αPAR and CCC for corn and soybean corresponded to the ones obtained from in situ data. The relationships between αPAR and CCC for corn and soybean are distinct due to the different photosynthetic pathways of corn (C4) and soybean (C3), differences in cell structure, and chloroplast distribution between the two crops. Crop yield and productivity are also related to CCC, meaning αPAR can be used as a crop specific indicator of yield. Through this research, I have demonstrated the added value of incorporating PAR directly into crop yield models, by improving crop yield estimates over empirical models based on vegetation indices or surface reflectance alone. The research also provides the basis for further work using crop specific measures of the absorption of PAR into the same empirical models at large spatial scales that were previously impractical due to the spatial discrepancies between in situ- and MODIS- derived measurements.
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    (2023) Hu, Guangxiao; Sun, Laixiang; Feng, Kuishuang; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Toxic chemicals pose significant threats to ecosystems, climate change, and human health. Unfortunately, pollution inequality is pervasive in the United States, with a disproportionate exposure of racial/ethnic minorities and low-socioeconomic groups to toxic releases. This inequality is especially pronounced in Houston, Texas. Moreover, income inequality has widened over decades, and the distribution of toxic releases has changed over time in the USA. To better understand the problem of pollution inequality, it is necessary to investigate the embodied toxic release in final demand by states and income groups. Notably, pollution inequality is non-uniform across regions and over time in the USA. The relationship between socioeconomic development and toxic risk necessitates analysis to comprehend the resultant health outcomes at different spatial scales and locations. Further, identifying the spatial heterogeneity of the association between environmental hazards and socioeconomic indicators is critical for addressing environmental inequality. Investigating the spatiotemporal heterogeneity of the impact of racial disparities and socioeconomic development on toxic risk can reveal disparities between regions and trends in pollution inequality. This study employs an extended U.S. multi-regional input-output (MRIO) model with toxic chemical release data to analyze the inter-regional transfer of embodied toxic release between states and their unequal distribution between income groups from a consumption-based perspective. Additionally, this study analyzes the spatial non-stationarity in the associations between toxic chemical hazard risk and community characteristics of census block groups in Texas, USA, for 2017 using a multiscale geographically weighted regression (MGWR). Further, this study uses Houston, a city with a history of segregation and discrimination and a diverse racial/ethnic makeup, as an example to analyze the spatiotemporal heterogeneity of the impact of racial disparities and socioeconomic development on toxic risk using geographical and temporal weighted regression (GTWR) models. The study's outcomes are instrumental in determining whether pollution inequality has improved or worsened. Results indicate that non-metallic and metallic products manufacturing sectors are crucial for interregional flows of embodied toxic release from the Great Lake Region to Southeast, Mid-Atlantic, and Northeast regions, and are the most important sectors for most states from the consumption-based perspective. The findings also highlight the significance of identifying the spatial patterns of the association between toxic chemical hazard risks and community characteristics at the census block group level to address environmental inequality.
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    (2023) Liu, Zheng; Oshan, Taylor; Geography/Library & Information Systems; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Spatial interactions provide insights into urban mobility that reflects urban livability. A range of traditional and modern urban mobility models have been developed to analyze and model spatial interaction. The study of bike-sharing systems has emerged as a new area of research, offering expanded opportunities to understand the dynamics of spatial interaction processes. This dissertation proposes new methods and frameworks to model and understand the high-frequency changes in the spatial interaction of a bike share system. Three challenges related to the spatial and temporal dynamics of spatial interaction within a bike share system are discussed via three studies: 1) Predicting spatial interaction demand at new stations as part of system infrastructure expansion; 2) Understanding the dynamics of determinants in the context of the COVID-19 pandemic; and 3) Detecting events that lead to changes in the spatial interaction process of bike share trips from a model-based proxy. The first study proposes a hybrid strategy to predict 'cold start' trips by comparing flow interpolation and spatial interaction methods. The study reveals 'cold start' stations with different classifications based on their locations have different best model choices as a hybrid strategy for the research question. The second study demonstrates a disaggregated comparative framework to capture the dynamics of determinants in bike share trip generation before, during, and after the COVID-19 lockdown and to identify long-term bike share usage behavioral changes. The third study investigates an event detection approach combining martingale test and spatial interaction model with specification evaluation from simulated data and explorative examination from bike share datasets in New York City, Washington, DC, and San Francisco. Results from the study recognize events from exogenous factors that induced changes in spatial interactions which are critical for model evaluation and improvement toward more flexible models to high-frequency changes. The dissertation elaborated and expanded the spatial interaction model to more effectively meet the research demands for the novel transportation mode of bike-share cycling in the context of a high-frequency urban environment. Taken as a whole, this dissertation contributes to the field of transportation geography and geographic information science and contributes methods toward the creation of improved transport systems for more livable cities.
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    Estimation and Spatiotemporal Analysis of All-sky Land Surface Temperature from Multiple Satellite Data
    (2023) Jia, Aolin; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The daily surface temperature variability, characterizing the dispersion of day-to-day temperature anomalies, is a fundamental aspect of the climate. It can be represented by the temperature standard deviation in a week. Studies reveal that daily temperature variability is a critical determinant of societal and natural outcomes, such as public health, crop yield, economic growth, etc. Although the overall warming trend is now well established in the scientific community, previous studies have shown little consensus about changes in daily temperature variability over the globe in recent decades; this is due to limited simulation accuracy and in-situ measurement distribution. Therefore, it is urgently needed to generate a reliable, global, long-term, observation-derived, daily temperature dataset in order to analyze variability changes and potential driving factors. The Advanced Very High-Resolution Radiometer (AVHRR) data provide an exceptional chance to record long-term land surface temperature (LST) over the entire globe. However, the AVHRR LST suffers from two restrictions: cloud contamination and orbital drift. Accordingly, we develop a surface energy balance (SEB)-based algorithm to recover the LST under clouds, and a two-step method to correct the artificial spurious temperature variation due to orbital drift. In the SEB method, 1) the hypothetical LST of missing pixels is first reconstructed by assimilating dispersed clear-sky retrievals into a continuous LST time-evolving model built by reanalysis data, and 2) the reconstructed LST is then corrected by superposing the cloud effect, estimated by satellite radiation products based on SEB theory. The two-step correction includes 1) calibrating the systematic bias of diurnal temperature cycles (DTCs) simulated from reanalysis data using satellite product climatology, 2) correcting the calibrated DTCs in detail by historical AVHRR LSTs during the years 1981-2021, and averaging the corrected DTCs to get daily mean LSTs. Global, 5-km, all-sky, daily mean LSTs from 1982 to 2021 are produced for the daily variability analysis. In order to mitigate the impact of orbital drift, the SEB method is examined by MODIS and VIIRS LST products. Ground validation suggests that the cloudy-sky VIIRS LST exhibits a root mean square error (RMSE) of 3.54 K, a bias of −0.36 K, and R2 of 0.94, comparable to the accuracy of clear-sky LST and the MODIS results. Thus, the algorithm is sensor independent and also works for AVHRR data. To obtain satellite-derived DTC climatology for calibrating simulated DTCs, an optimization module is created to extend the feasibility of the SEB method at night. By collecting clear-sky LSTs from geostationary satellite sensors and two MODIS sensors, global, hourly, 5 km, all-sky LSTs from 2011 to 2021 are produced. The overall RMSE of the hourly LSTs is 3.38 K, with a bias of −0.53 K based on 197 global sites. Finally, after integrating the SEB method and two-step correction method, the target AVHRR LST is recovered with an RMSE of 1.97 K over the globe and few biases. Spatiotemporal analysis of the AVHRR LST suggests that the globally averaged daily LST variability does not have a significant trend from 1982 to 2021 under the global warming background, whereas it showed diverse variation both regionally and seasonally. A significant decrease/increase is detected at high/low latitudes, which matches previous simulation conclusions. However, contrary to the simulation, it reveals significant variability increases in the mid-latitudes, such as the western US, the Mediterranean Basin, and northern China. Historical auxiliary observations indicate that the variability decrease at high-latitudes is driven by downward longwave (DLW) radiation. Arctic amplification mitigates cold temperature anomalies at high latitudes in winter. The enhanced atmospheric convection in the tropics causes the increasing variability of cloud cover and downward shortwave radiation (DSR), and the LST variability has also increased. Climate internal variability, DLW, and DSR all show considerable impact at mid-latitudes. This study proposed innovative cloud-sky LST estimation and orbital drift correction methods. The first global, all-sky, 5-km, daily mean LST product (1982 - 2021) was generated, which shows great potential for long-term energy budget and hydrological cycling analysis. Furthermore, the study fills the knowledge gap about the unknown daily temperature variability trend over the globe and provides an attribution based on historical observations, which will assist the community in understanding the mechanism of high-frequency temperature change, improving model prediction, and coordinating resources for extreme weather adaptation.
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    (2022) Cui, Diyang; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Climate change is driving biodiversity redistribution on Earth, undermining the effectiveness of protected areas (PAs) in conserving global biodiversity. Managing the consequences of biodiversity redistribution and promoting effective conservation necessitates a better understanding of climate shift patterns and species’ ability to track changing climates. Recent studies assessing the effects of climate change on biodiversity have increasingly used velocity metrics to represent climate shifts over space and time. Velocity based on a single climate variable or climate space identified using statistically combined multivariate indices may not be related to biomes or ecosystems and lacks the potential to conduct risk evaluation for biodiversity. The widely used Köppen–Geiger classification scheme provides an effective way to characterize bioclimatic conditions by incorporating multiple climatic indicators and biological information, thus can be a new direction for developing velocity metrics and supporting the development of species distribution models (SDMs). To identify research gaps, this dissertation research first reviews recent detection and assessment studies on past and future projected climate zone changes. Previous studies have shown that accelerated global warming since the 1980s has resulted in changes in climate zones that have been observed over 5% of the global land area. Tropical and arid climate zones are expected to expand into mid and high latitudes, while polar climates are shifting poleward and upward, leading to significant area shrinkage. Given the need for improved historical and future global climate maps with long-term temporal coverage and accurate depiction of fine-grained bioclimatic conditions in climate change studies, the study creates a set of 1 km Köppen-Geiger climate classification maps (KGClim) for six historical periods in 1979–2013 and four future periods in 2020–2099 under RCP2.6, 4.5, 6.0, and 8.5. The new maps offer higher classification accuracy than existing datasets and demonstrate the ability to capture recent and future projected changes in distribution of climate zones. Using the new KGClim dataset, this dissertation calculates the velocity of climate zone shifts to assess exposure risks of global PAs and examines the spatial patterns of near-, mid- and long-term climate shifts projected based on different emission pathways. Based on the findings, under RCP8.5, 38% of global protected land could undergo climate zone shifts at accelerating rates for the remainder of this century. Furthermore, global protected lands are experiencing novel (8% of global protected land) and disappearing (7%) climates, shifts of climates outside current PA networks (8%), and transition to human-dominated land use (6%). The fine-scale velocity metrics reveal spatiotemporal patterns of climate shifts and biodiversity redistribution, which can inform adaptive conservation planning to address the ongoing biodiversity crisis and achieve future conservation goals.
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    Tracking the dynamics of the opioid crisis in the United States over space and time
    (2022) Xia, Zhiyue; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Millions of adolescents and adults in the United States suffer from drug problems such as substance use disorder, referring to clinical impairments including mental illnesses and disabilities caused by drugs. The Substance Abuse and Mental Health Services Administration reported the estimated number of illicit drug users increased to 59.3 million in 2020, or 21.4% of the U.S. population, which made drug misuse one of the most concerning public health issues. Opioids are a category of drugs that can be highly addictive, including heroin and synthetic drugs such as fentanyl. Centers for Disease Control and Prevention (CDC) indicated that about 74.8% of drug overdose deaths involved opioids in 2020. The opioid crisis has hit American cities hard, spreading across the U.S. beginning with the west coast, and then expanding to heavily impact the central, mid-Atlantic, and east coast of the U.S. as well as states in the southeast. In this dissertation, I work on three studies to track the dynamics of the opioid crisis in the U.S. over space and time from a geographic perspective using spatiotemporal data science methods including clustering analysis, time-series models and machine learning approaches. The first study focused on the geospatial patterns of illicit drug-related activities (e.g., possession, delivery, and manufacture of opioids) in a typical U.S. city (Chicago as a case study area). By analyzing more than 52,000 reported drug activities, I built a data-driven machine learning model for predicting opioid hot zones and identifying correlated built environment and sociodemographic factors that drove the opioid crisis in an urban setting. The second study of my dissertation is to analyze the opioid crisis in the context of the global pandemic of SARS-CoV-2 (COVID-19). In 2020, COVID-19 outbroke and affected hundreds of millions of people across the globe. The COVID-19 pandemic is also impacting the community of opioid misusers in the U.S. The major research objective of Study 2 is to understand how the opioid crisis is impacted by the COVID-19 pandemic and to find neighborhood characteristics and economic factors that have driven the variations before and during the pandemic. Study 3 focuses on analyzing the crisis risen by synthetic opioids (including fentanyl) that are more potent and dangerous than other drugs. This study analyzed the geographic patterns of synthetic opioids spreading across the U.S. between 2013 and 2020, a period when synthetic opioids rose to be a major risk factor for public health. The significance of this dissertation is that the three studies investigate the opioid crisis in the U.S. in a comprehensive manner and these studies can facilitate public health stakeholders with effective decision making on healthcare planning relating to drug problems. Tracking the dynamics of the opioid crisis by drug type, including modeling and predicting the geographic patterns of opioid misuse involving particular opioids (e.g, heroin and synthetic opioids), can provide an important basis for applying further treatment services and mitigation efforts, and also be useful for assessing current services and efforts.
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    Forest Loss Trajectories and Palm Oil Extent in Indonesia
    (2022) Parker, Diana; Hansen, Matthew; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Tropical forests provide critically important ecosystem services, and they are particularly important for their levels of biodiversity and for the carbon that they store. Yet despite global efforts to slow or halt deforestation, natural forests in the tropics continue to be cleared, primarily for agricultural expansion. Indonesia contains the world’s third largest humid tropical forest area, and for much of the past several decades has experienced alarmingly high rates of deforestation. This began to change in 2017, when deforestation rates dropped precipitously and have since remained low. To better understand how this recent trend compares to historical deforestation patterns, this study used a sample-based approach to estimate annual primary forest loss in Indonesia over a 30-year period, from 1991-2020. Since 1990, Indonesia has lost 28.4 (standard error of +/-0.7) Mha of primary forest – roughly one quarter of its total primary forest area in 1990. One fifth of this area (19.7% +/-1) was cleared during a single two-year period, 1997 and 1998, when millions of hectares of primary forest were burned during a severe El Niño event. I also tracked land use after forest clearing to better understand what drives deforestation in Indonesia and found that more than half of all forests were left idle after clearing, often for years at a time. While some of this was caused by forest fires, like those that occurred during the 1997/98 El Niño event, the majority, 8.5 (+/-0.4) Mha, was actively cleared. Large areas of actively and fire-cleared land remained unused at the end of the study period (4 +/-0.3 and 4.8 +/-0.3 Mha, respectively). However, by 2020, an estimated 40.7% (+/-1.7) of initially unproductive land had also been converted to productive land uses, primarily palm oil production, which covered 16 (+/-0.5) Mha of land in Indonesia in 2020. This included 2.5 (+/- 0.2) Mha of land used to cultivate oil palms that directly replaced primary forests and another 5.3 (+/-0.3) Mha that expanded into previously forested areas one or more years after forest conversion. In the last few years of the study, my sample-derived estimates also confirmed a decline in deforestation after 2016, which had previously been seen in forest loss estimates derived from map pixel counting. From 2017-2020 Indonesia experienced the lowest rates of primary forest clearing observed during the study period. This drop in deforestation occurred after years of increasingly tight restrictions related to primary forest conversion, peatland use, and palm oil expansion, and during a period of heightened public concern about deforestation and land fires following the 2015 El Niño event. It also occurred during a time when palm oil prices were relatively low, and after millions of hectares of idle land had been intentionally created, a phenomenon that is likely closely tied to speculation and land banking. This study provides the most detailed information currently available about historic deforestation trends and land use trajectories after forest clearing in Indonesia, shedding new light on forest change patterns and providing a dataset that could potentially be used in future studies, including for econometric research to quantify the extent to which political and economic factors may have influenced land cover change.
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    Socioeconomic Impacts of Policy Interventions in the Food-Energy-water Nexus
    (2022) Kumar, Ipsita; Sun, Laixiang; Feng, Kuishuang; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The food-energy-water (FEW) nexus is considered essential for human survival and critical for the achievement of the Sustainable Development Goals. However, pressures on each component of the nexus are growing as a result of population and economic growth. The FEW nexus can also be affected by competition for limited land, climate change, and demand and supply changes. Although government policies targeting one of the components of the nexus will directly affect the others, they are still not accounting for the interconnectedness of all three. The dissertation, through three essays seeks to understand how government policies would affect the FEW nexus, focusing on Thailand or Brazil. The first essay assesses challenges with crop residue burning in Thailand. Additionally, the essay highlights policies implemented that target residue burning or its use and the potential solutions through crop residue use. The second essay examines specific policies on crop residue burning and renewable energy (RE) production to understand their impacts on sustainability. An extended input-output model is run to using policy scenarios for the future to gauge its impacts on total output, gross value added, employment, labor income, key input use, land use, water use and CO2 emissions on Thailand and Northeast Thailand. The final essay explores food and energy security given water supply limitations as water availability greatly impacts availability of food and energy. It uses a region in Sao Paulo, Brazil, where RE policies and other interventions have helped make ethanol production and use cost effective. A model is developed to maximize profits while optimally allocating water to food, energy and municipal water. The study looks at a normal rainfall year, and also runs a future demand change scenario. The dissertation concludes by detailing the challenges that exist, future potential for the FEW nexus policies, limitations and uncertainties. The dissertation establishes that given the interlinked nature of the FEW nexus, policies need to be implemented to account for all three components. The first essay shows that over time, an increasing number of policies in Thailand target crop residue burning through controlling burning or its use in RE production. Although these policies have been implemented, there are still shortcomings in the policy targets for biomass use, and in the large water use by the sector, as highlighted in essay 1 and 2. Essay 2 also demonstrates social, economic and environmental benefits of using crop residue for RE through employment generated, labor income increases, and CO2 emission reduction in Thailand and Northeast Thailand. We also see increasing competition for land for energy, with sugarcane potentially overtaking rice in Northeast Thailand. In essay 3, we see that while Brazil has implemented sound policies on RE, there are water security challenges, and competition between food, energy and municipal water supply. We see that the current infrastructure cannot satisfy future demand, leading to competing demands and equity challenges. Finally, in the conclusion, the research highlights uncertainties about future demand, water supply, technology, price, etc. along with potential policies.
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    Multispectral satellite remote sensing approaches for estimating cover crop performance in Maryland and Delaware
    (2022) THIEME, ALISON; Justice, Chris; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Winter cover crops encompass a range of species planted in late summer and fall for a variety of reasons relating to soil health, nutrient retention, soil compaction, biotic diversity, and erosion prevention. As agricultural intensification continues, the practice of winter cover cropping remains a crucial practice to reduce leaching from agricultural fields. Maryland and Delaware both incentivize cover cropping to meet water quality objectives in the Chesapeake Bay Watershed. These large-scale programs necessitate methods to evaluate cover crop performance over the landscape. Cover crop quantity and quality was measured at 2,700 locations between 2006-2021 with a focus on fields planted to four cereal species: wheat, rye, barley, and triticale. Samples were GPS located and timed with satellite remote sensing observations from SPOT 4, SPOT 5, Landsat 5, Landsat 7, Landsat 8, or Sentinel-2. When paired imagery at 10-30 m spatial resolution , there is a strong relationship between the normalized difference vegetation index (NDVI) and percent ground cover (R2=0.72) as well as NDVI and biomass (as high as R2=0.77). There is also a strong relationship between Δ Red Edge (a combination of 740 nm and 783 nm bands) and nitrogen content (R2=0.75). These equations were applied to Harmonized Landsat Sentinel-2 products and used to estimate cover crop aboveground biomass in ~300,000 ha of Maryland Department of Agricultures and ~60,000 ha of Delaware Association of Conservation Districts enrolled fields from 2019-2021 and grouped by agronomic method. Wintertime and springtime cover crop biomass varied based on planting date, planting method, species, termination date, and termination method. Early planted fields had higher wintertime biomass while fields that delayed termination had higher springtime biomass. Triticale had consistently higher biomass while wheat had the lowest biomass. Fields planted using a drill followed by light tillage or no-till drill had higher biomass, likely due to the better seed-to-soil contact. Fields that were taken to harvest or terminated for on farm use (roller crimped, green chopped) also had higher springtime biomass than other termination methods. Incentives can be used to encourage specific agronomic methods and these findings can be used to inform adaptive management in the Mid-Atlantic Region.
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    Washington, D.C. and the Growth of Its Early Suburbs : 1860-1920
    (1980) Levy, Anneli Moucka; Groves, Paul A.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md)
    During the nineteenth century, the North American city greatly changed in size and internal structure. With the introduction of mass transportation, large scale suburbanization took place as one aspect of this change. Members of the evolving middle class not only wished to escape the pollution and congestion of the urban core, but also believed strongly in a 'rural ideal,' translated into a 'suburban ideal.' Urban changes and suburban growth were especially pronounced in industrial cities, and descriptions of conditions in these cities identify the accepted model of the spatial configuration of the metropolis existed in 1920. Examination of the growth of Washington D. C. between the Civil War and World War I indicates that the city shared few of the characteristics of the accepted urban model. Nevertheless, it exhibited distinct suburban movement connected with three major transport modes, including the steam railroad. The belief in the 'suburban ideal' was broadly based in Washington and therefore much variation was found among the city's suburban communities, even among those associated with the same transportation mode. Furthermore, in contrast to the suburban model, conditions in the suburban areas often did not compare favorably with those in the city. Even so, the suburbanization process accelerated from small beginnings, so that by 1920 the city displayed the local variant of the typical star-shaped pattern.
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    Effectively evaluating environmental, social, and economic outcomes in voluntary sustainability programs: Lessons from Laos
    (2022) Traldi, Rebecca; Silva, Julie A; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Voluntary sustainability programs (VSPs) are a subset of environmental interventions which rely on participants’ willingness to engage, rather than mandatory regulation. VSPs have been a central component of sustainable development and environmental mitigation strategies for decades, with significant investments from nongovernmental organizations (NGOs), multilaterals, and the private sector. VSPs typically aim to positively influence environmental, economic, and social outcomes, although program-specific priorities often result in an uneven focus across these three domains (also known as the three pillars of sustainability). Despite their popularity, questions regarding the value of VSPs remain unanswered. Assessments of VSPs typically do not eliminate rival explanations for program outcomes when evaluating their successes and failures, thus limiting our understanding of their effectiveness.This dissertation addresses this gap by investigating socioeconomic and environmental outcomes for agriculture and forestry VSPs. Mixed methods including systematic review, inverse probability-of-treatment weighted regression (IPWR), and inequality and polarization decomposition provide insights both at a global level, and for two national case studies in Lao People’s Democratic Republic (hereafter Laos). A wide range of datasets inform the analysis, including nationally representative poverty and expenditure surveys and land-use land cover estimates derived from remotely sensed imagery. By exploring a variety of VSPs – including agricultural and forestry voluntary sustainability standards and sustainable development projects – the study acknowledges the context-specific nature of VSP impact, while also drawing generalizable insights relevant for different types of interventions. The research findings presented in this dissertation elucidate the degree to which VSPs deliver on stated goals and objectives. First, a systematic literature review reveals that the evidence base for VSP impact remains limited, with some geographies, sustainability outcomes, and project types receiving more inquiry and evaluation than others. Second, an IPWR analysis suggests that agriculture and forestry VSPs have achieved some success in generating positive outcomes – specifically, for poverty and forest cover. However, variations in project focus and design bring different results. For example, food security and livelihoods programs which prioritize local socioeconomic well-being can generate significant co-benefits for environmental outcomes, and resource management projects can positively impact forest cover. Conversely, the forest management projects considered here do not achieve significant benefits for poverty or forest cover – presumably due to challenges like land tenure insecurity, insufficient participant incentives, and persistent drivers of deforestation (illegal logging, large-scale concessions). Finally, an assessment of economic inequality and polarization associated with the Laos rubber boom demonstrates the importance of assessing how VSPs influence economic inequality. It also indicates that VSPs must address inequality’s systemic drivers – including dispossession from land and forest resources, lacking worker protections, livelihood vulnerability, and barriers for smallholders – to maximize potential benefits. Overall, this dissertation research provides an example of how evidence synthesis, quasi-experimental methods, and consideration of economic, social, and environmental sustainability can deepen our understanding of VSPs.
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    Demand-Driven Climate Mitigation in the United States: Challenges and Opportunities to Reduce Carbon Footprints from Households and State-Level Actors
    (2022) Song, Kaihui; Baiocchi, Giovanni; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Subnational and non-governmental actors have great potential to push for bolder climate actions to limit the global average temperature increase to 1.5 degrees Celsius above pre-industrial levels. A consistent and accurate quantification of their GHG emissions is an important prerequisite for the success of such efforts. Although an increasing number of subnational actors have developed their climate mitigation plans with medium- or long- term goals, whether these progressive commitments can yield effectiveness as planned still remains unclear. This dissertation research focuses on two large groups of climate mitigation actors in the U.S. – households and state-level actors – to improve the understanding of potential mitigation challenges and shed light on climate policies. This dissertation consists of three principle essays. The first essay reveals a key challenge of emission spillover among state-level collective mitigation efforts in the U.S. It quantifies consumption-based GHG emissions at the state level and analyzes emissions embodied in interstate and international trade. By analyzing major emission transfers between states from critical sectors, this essay proposed potential policy strategies for effective climate mitigation collaboration. The second essay addresses unequal household consumption and associated carbon footprints in the U.S., with a closer look at different contributions across income groups to the national peak-and-decline trend in the U.S. This analysis further analyzes changes in consumption patterns of detailed consumed products by income groups. The third essay proposed a framework to link people’s needs and behaviors to their consumption and associated carbon footprints. This framework, built on existing models that connect carbon footprints with consumer behaviors, extends to people’s needs with simulation over time. Such an extension provides a better understanding of carbon footprints driven by various needs in the context of real-world decision-making. Based on this framework, this essay selects a basket of behavioral changes driven by changing fundamental human needs and analyzes associated carbon footprints. The dissertation identifies opportunities and challenges in demand-driven climate mitigation in the U.S. Its findings provide implications for effective climate actions from state-level actors and households.
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    (2022) Sauer, Jeffery Charles; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The United States continues to endure the Opioid Overdose Crisis. Yet the burden of the crisis is not experienced uniformly across the United States. The discipline of geography offers a framework and spatial analysis methodology that are direct ways to investigate placed-based differences in opioid-related outcomes, exposures, and proxy measures. This dissertation combines the contemporary frameworks of health geography and geographic information science to provide novel studies on both the geographic patterns in opioid-related health measures at different scales across the United States as well as the actual spatial analytic methods that provide evidence on the Opioid Overdose Crisis. Three main research objectives are addressed over the course of the dissertation: 1) Model the space-time risk of heroin-, methadone-, and cocaine-involved emergency department visits in the greater Baltimore metropolitan area from January 2016 to December 2019 at the Zip Code Tabulation Area-level; 2) Estimate the local and neighboring relationship between prescription opioid volume and treatment admissions involving a prescription opioid across the United States from 2006 to 2014 at the county-level; and 3) Investigate and provide a framework as to how geographic information science has been used to provide knowledge over the duration of the crisis from 1999 to 2021. The first study demonstrates how a recently proposed spatio-temporal Bayesian model can produce disease risk surfaces for opioid-related health outcomes in data constrained scenarios. The second study executes spatial lag of X models on a nationwide prescription opioid distribution dataset, allowing for estimates on the relationship between neighboring prescription opioid volume and nonfatal treatment admissions involving a prescription opioid at the county-level. The third and final study of the dissertation developed and implemented a scoping review methodology, ultimately analyzing the study design and geographical elements of 231 peer-reviewed publications using geographic information science on research questions related to the crisis. Examination of the geographical components of these studies reveals a lack of evidence available at sub-state scales and in the Midwest, north Rocky Mountains, and non-continental United States. Several important future research directions - such as geographic meta-analyses and geographical machine learning - are identified. Taken as a whole, the dissertation provides a contemporary geographical framework to understand the ongoing United States Opioid Overdose Crisis.
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    (2022) Xin, Yu; Sun, Laixiang; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Palm oil is the world's most widely used edible oil, and Indonesia has been the largest producer since 2007 and now makes up around 58% of the global market. The oil palm production has benefited the economic growth and lifted the living standards of local people in Indonesia, but this gain is often at the cost of replacing tropical forest, destructing peatland, inducing greenhouse gas (GHG) emissions, and reducing biodiversity. The expansion of oil palm plantation in Indonesia is bound to increase as the global demands continue to grow. The challenge of meeting the increased demand for oil palm products while effectively protecting tropical forest and its ecosystem services is an important tradeoff issue for both scientists and policymakers. However, little is known on the expansion patterns of oil palm in Indonesia, especially the underlying drivers with temporal and spatial details. To effectively address the knowledge gaps and deal with the challenges, this dissertation aims to first characterize the historical patterns driven by the variations in the benefits and costs of oil palm expansion across space and over time. It then projects the possible future spatial patterns and estimates the potential loss of land with high environmental values in order to meet the future global demand for oil palm products. This dissertation consists of three principle essays. The first essay identifies the major land sources of oil palm expansion in Indonesia with temporal details, and reveals the joint role of biophysical and socioeconomic drivers in shaping the spatial patterns of oil palm expansion by employing spatial panel models at the regency level. The second essay focuses on the temporal dynamics of the biophysical and socioeconomic drivers and the timing of estate crop (mainly oil palm) expansion by using Cox proportional hazard models (CPHMs) and their extensions with time-variant effects at the 1km × 1km grid level. It also explores the role of land use and land cover change (LCLUC) trajectory hopping in estate crop expansion into natural forest by introducing multi-state survival analysis to land-use science. The third essay projects the export demand for oil palm products from Indonesia by 2050 under different global trade scenarios with generalized geo-economic gravity models, and quantifies the possible tradeoffs between oil palm expansion and environmental conservation by allocating the projected demand to 1km × 1km grids across Indonesia applying parametric survival analysis. This study indicates that oil palm expansion in Indonesia has been strongly stimulated by the export value of oil palm products and prefers land with good biophysical suitability and infrastructure accessibility. As land resources become more limited, the effects of socioeconomic factors decrease following the ‘pecking order’ sequence, and the plantation expands into remote but fertile areas with high conversion costs or legal barriers. The degraded land surpassed natural forest and became the major direct land source of oil palm expansion in recent years, but degraded land had increasingly served as a land banking mechanism and a clearing-up tactic. This LCLUC trajectory hopping mechanism has made the protected area (PA) designations and sustainable development requirements become less and less effective in protecting tropical natural forest. Lowland secondary forest and peatland are the high-environmental-value (HEV) areas with the highest risks of conversion to oil palm plantation. To cope with the LCLUC trajectory hopping mechanism, Indonesia needs to have well-designed and fully enforced policies which limit/ban expansion into protected areas, peatland conversion, and deforestation of both primary and secondary forest. The country also needs more effective economic compensation mechanisms to promote more environment-friendly oil palm plantation. In this way, it is possible for Indonesia to maintain its leading position in oil palm production and exportation, while enhancing its role in environmental protection, such as climate change mitigation and biodiversity conservation. This dissertation improves our understanding of oil palm expansion in Indonesia by integrating economic science theory, advanced econometric techniques, and the best available remote-sensing data. It adds to the existing literature on analyzing the impacts of human behaviors on LCLUC at various spatial and temporal scales, especially from a longitudinal perspective.
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    (2021) Gong, Weishu; Huang, Chengquan; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Current estimation of the Earth’s carbon budget contains large uncertainties, with the largest ones in its terrestrial components. With an overarching goal to improve the understanding of carbon budget at regional to global scales, this study aimed to: 1. Develop a grid-based carbon accounting (GCA) model for estimating carbon fluxes from forest disturbance, tested over North Carolina; 2. Develop a consistent timber product output (TPO) record for a globally important timber production region, including seven states in the southeast U.S.; and 3. Further improve the GCA model based on results from objectives 1 and 2, and use it to derive carbon source/sink estimates for all forest land in North Carolina.The results show that several inputs/parameters such as pre-disturbance carbon density, disturbance intensity, allocation of removed carbon among slash and different wood product pools, and forest growth rates could have large impact on carbon estimates. The total emission between 1986 and 2010 from logging over North Carolina was reduced by one third and two thirds, respectively, when remote sensing-based disturbance intensity and biomass data were used to replace parameter values inherited from the original bookkeeping carbon accounting (BCA) model, and was reduced by over 70% when both were used. Use of the TPO data derived in Chapter 3 to partition the removed carbon among slash and different wood product pools resulted in noticeably higher emission estimates than those derived using the partitioning ratios provided by the original BCA model. In addition, without considering legacy effect from wood products harvested before 1986, the emission value derived using the prompt release method was 50% higher than that derived using the delayed release method. This study addresses multiple sources of uncertainties related to the terrestrial carbon budget. The TPO study demonstrated an approach for deriving consistent TPO records for large timber production regions. The GCA model produced state level carbon estimates comparable to those reported by the U.S. Forest Service while providing critical spatial details needed to support carbon management and advance forest-driven climate change mitigation initiatives.