Geography Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2773
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Item Characterizing tree species diversity in the tropics using full-waveform lidar data(2019) Marselis, Suzanne; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Tree species diversity is of paramount value to maintain forest health and to ensure that forests are able to provide all vital functions, such as creating oxygen, that are needed for mankind to survive. Most of the world’s tree species grow in the tropical region, but many of them are threatened with extinction due to increasing natural and human-induced pressures on the environment. Mapping tree species diversity in the tropics is of high importance to enable effective conservation management of these highly diverse forests. This dissertation explores a new approach to mapping tree species diversity by using information on the vertical canopy structure derived from full-waveform lidar data. This approach is of particular interest in light of the recently launched Global Ecosystem Dynamics Investigation (GEDI), a full-waveform spaceborne lidar. First, successful derivation of vertical canopy structure metrics is ensured by comparing canopy profiles from airborne lidar data to those from terrestrial lidar. Then, the airborne canopy profiles were used to map five successional vegetation types in Lopé National Park in Gabon, Africa. Second, the relationship between vertical canopy structure and tree species richness was evaluated across four study sites in Gabon, which enabled mapping of tree species richness using canopy structure information from full-waveform lidar. Third, the relationship between canopy structure and tree species richness across the tropics was established using field and lidar data collected in 16 study sites across the tropics. Finally, it was evaluated how the methods and applications developed here could be adapted and used for mapping pan-tropical tree species diversity using future GEDI lidar data products.Item Quantifying the Spatial and Temporal Variation of Land Surface Warming Using in situ and Satellite Data(2019) Rao, Yuhan; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The global mean surface air temperature (SAT) has demonstrated the “unequivocal warming”. To understand the impact of the global warming, it is very important to quantify the spatial and temporal patterns of the surface air temperature change. Currently, most observational studies rely on in situ temperature measurements over the land and ocean. But the uneven and sparse nature of these temperature measurements may cause large uncertainty for the climate analysis especially at local and regional scales. With the rapid development of satellite data, it is possible to estimate spatial complete surface air temperature from satellite data using advanced statistical models. The satellite data-based estimation can serve as a better data source for local and regional climate analysis to reduce analysis uncertainty. In this dissertation, I firstly examined the uncertainty of four mainstream gridded SAT datasets over the global land area (i.e., BEST-LAND, CRU-TEM4v, NASA-GISS, NOAA-NCEI). The comprehensive assessment of these datasets concludes that different data coverage may cause remarkable differences (i.e., -0.4 ~ 0.6°C) of calculated large scale (i.e., global, hemispheric) average SAT anomaly using different datasets. Moreover, these datasets show even larger differences at regional and local scale (5°×5°). The local and regional data differences can lead to statistically significant differences on linear trends of SAT estimated using different datasets. The correlation analysis shows strong relationship between the uncertainty of estimated SAT trends and the density of in situ measurements across different regions. To reduce the uncertainty of surface air temperature data, I developed a statistical modelling framework which can estimate daily surface air temperature using remote sensing land surface temperature and radiation products. The framework uses machine learning models (i.e., rule-based Cubist regression model and multivariate adaptive regression spline) to characterize the physical difference between land surface temperature and surface air temperature by including radiation products at both surface and the top of the atmosphere. The model was firstly developed for the Tibetan Plateau using Cubist model trained with Chinese Meteorological Administration station measurements. Comprehensive evaluation show that the Cubist model can estimate the surface air temperature with nearly zero degree Celsius bias and small RMSEs between 1.6 °C ~ 2.1 °C. The estimated SAT over the entire Plateau for 2000-2015 show that the warming of the western part of the Plateau has been more prominent than the rest of the region. This result show the potential underestimation of conventional station measurements based studies because there are no station measurements to represent the rapid warming region. The machine learning model is then extended to the northern high latitudes with necessary modification to account for the regional difference of the diurnal temperature cycle as well as the large data volume of the northern high latitudes. The MARS model trained using data over the northern high latitudes from the Global Historical Climatology Network daily data archive show a reasonable model performance with the bias of around -0.2 °C and the RMSE ranging between 2.1 – 2.6 °C. Further evaluation shows that the model performs worse over permanent snow and ice surface due to the insufficient training data to represent this specific surface conditions. Overall, this research demonstrated that leveraging advanced statistical methods and satellite products can help generating high quality surface air temperature data which can provide much needed spatial details to reduce the uncertainty of local and regional climate analysis. The model developed in this research is generic and can be further extended to other regions with proper modification and training using high quality local data.Item Multi-dimensional measures of geography and the opioid epidemic: place, time and context(2019) Cao, Yanjia; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The opioid crisis has hit the United States hard in recent years. Behavioral patterns and social environments associated with opioid use and misuse vary significantly across communities. It is important to understand the geospatial prevalence of opioid overdoses and other impacts related to the crisis in order to provide a targeted response at different locations. This dissertation contributes a framework for understanding spatial and temporal patterns of drug prevalence, treatment services access and associated socio-environmental factors for opioid use and misuse. This dissertation addresses three main questions related to geography and the opioid epidemic: 1) How did drug poisoning deaths involving heroin evolve over space and time in the U.S. between 2000-2016; 2) How did access to opioid use disorder treatment facilities and emergency medical services vary spatially in New Hampshire during 2015-2016; and 3) What were the relations between socio-environmental factors and numbers of emergency department patients with drug-related health problems over space and time in Maryland during 2016-2018. For the first study, this dissertation developed a spatial and temporal data model to investigate trends of heroin mortality over a 17-year period (2000-2016). The research presented in this dissertation also involved developing a composite index to analyze spatial accessibility to both opioid use disorder treatment facilities and emergency medical services and compared these locations with the locations of deaths involving fentanyl to identify possible gaps in services. In the third study for this dissertation, I utilized socially-sensed data to identify neighborhood characteristics and investigated spatial and temporal relationships with emergency department patients with drug-related health problems admitted to the four hospitals in the western Baltimore area in Maryland during 2016 to 2018, in order to identify the dynamic patterns of the associations in terms of various socio-environmental factors.Item TELE-CONNECTING CONSUMPTION OF NATURAL RESOURCE USE AND ENVIORNMENTAL IMPACTS THROUGH (GLOBAL) SUPPLY CHAINS: APPLICATIONS OF THE MULTI-REGIONAL INPUT-OUTPUT MODEL(2019) White, David J.; Hubacek, Klaus; Feng, Kuishuang; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Natural resources are necessary inputs in production systems. In today’s globalized world, local resource consumption can impact ecosystems on a global scale. With commodities and services being traded across economic and ecosystem boundaries, natural resources are appropriated and exchanged. The finite nature of natural resources, uneven distribution in space and time, and global trends in consumption are impacting resource availability. The overuse of resources can have severe consequences on ecosystems; further degrading quality and functioning. The rise and expansion of global supply chains, with ever-increasing exchanges of intermediate goods, deepens the complexity of assessing the negative environmental impacts of trade externalities and globalization. To understand the consequences of natural resource consumption in international trade, we incorporate environmental indicators in an across-scale approach to examine and describe the spatial linkages between local consumption and environmental impacts in a meaningful and quantitative method. Applying the tele-connections concept, this research utilizes the environmentally-extended multi-regional input-output model to quantify, track, and evaluate the hidden ‘virtual’ flows of natural resources and environmental impacts across economic supply chains. This research spatially identifies and traces the major trade routes conveying environmental pressures and impacts on local ecosystems on regions of production from distant centers of consumption. Our analysis demonstrates that resource consumption and scarcity transpire differently across system boundaries with variable resource endowments. Therefore, incorporating environmental relevance across scale is critical to understanding resource consumption and scarcity. The across scale perspective provides not only novel insight into the environmental pressures facing systems, but reveals ‘hotspots’ of environmental impacts. Numerous footprint and virtual trade studies have been conducted for a particular country, region, or globally, but with little attention to the tele-connection of consumption of natural resource and environmental impacts across scale in multiple places. This research demonstrates that incorporating relevant environmental indicators and a multi-scaled approach enhances the assessment of humanity’s resource consumption and impacts on the environment.Item Forest Cover Dynamics of Shifting Cultivation in the Democratic Republic of Congo(2018) Molinario, Giuseppe Maria; Hansen, Matthew C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation is focused on contextualizing spatio-temporally forest cover loss in the DRC for the period 2000-2015 as it relates to the shifting cultivation dynamic and the rural complex mosaic. Impacts of forest loss on forest ecosystems, carbon release and biodiversity habitat differ depending on where and when it occurs relative to the rural complex. This was done by mapping the rural complex and disaggregating forest cover loss due to cyclical, livelihood shifting cultivation within three areas: 1) the baseline established rural complex (ERC) for 2000 and new 2000-2015 primary forest loss occurring as either 2) rural complex expansion (RCE) or 3) isolated forest perforations (IFP) further into core forest. Finally the influence of large-scale commercial land uses on forest cover loss is also assessed, from a spatial perspective. Between 2000 and 2010 the rural complex grew by 10% from 12% to 13% of the DRC’s land area, at an average yearly rate of 1%, while perforated forest grew by 74%, from 0.8% to 1.5% of DRC’s land area in 2010 at an average yearly rate of 0.7%. Core forest decreased by -3.8% at an average yearly rate of -0.4% per year, from 38% to 36.6% of the 2010 land area. Of particular concern is the nearly doubling of perforated forest, representing greater spatial intrusion of forest clearing within core forest areas. The land cover and land use (LCLU) components of the ERC were estimated by photo-interpreting high resolution imagery selected using a simple random sampling scheme. In the ERC 76% of land was already actively used for shifting cultivation. Therefore, together with remnant patches of primary forest (11%), an estimated 87% of the ERC was available for future shifting cultivation. Assuming a 4.6% clearing rate, this allowed estimating a ~18 year reuse rate of land in the ERC. Only 2% of the ERC area was occupied by large-scale commercial land use. This led to positing that commercial land uses might be more prevalent further away from settlements into core forest, where lower population density leads to less competition for natural resources. This hypothesis was tested by extending the probabilistic sampling analysis to new primary forest cover loss occurring outside of the ERC during the period 2000-2015. The map of the rural complex developed in Chapter 2 was validated, confirming larger proportions of primary forest and smaller proportions of shifting cultivation further away from the ERC and into core forest areas. LCLU proportions were established for both the RCE and IFP areas. Finally a concentric buffer distance analysis around sample points was used to quantify large-scale commercial land uses at the landscape scale, such as logging, mining and plantations that might be influencing shifting cultivation-driven forest cover loss. In the RCE the proportion of commercial land use was 0.4%, whereas it was 0.5% in IFPs; less than the proportion of commercial land use found in the ERC (2%). At the same time, results of the concentric buffer distance analysis show that 12% of sample points in the RCE and 9% of sample points in the IFP had commercial land uses within 5km. Commercial land uses are possibly more prevalent closer to the ERC because while there is more competition for land, there are also roads and communities that allow for the transportation of goods and provide labor. These results support the conclusion that large scale LCLU change dynamics in the DRC, such as commercial operations for export, are currently dwarfed by the reliance of rural populations on shifting cultivation. The vast majority of forest cover loss in the DRC remains due to smallholder farming not associated with commercial land uses. However, large-scale agroindustry or resource extraction activities lead to increased forest loss as their worker populations and communities rely on shifting cultivation for food, materials and energy. The spatial analysis of the rural complex allows us to peer into the future of forests in the DRC, as where isolated perforations lead, the rural complex soon follows and as the rural complex expands, so do commercial land uses.Item Landsat-based operational wheat area estimation model for Punjab, Pakistan(2018) Khan, Ahmad; Hansen, Matthew C.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Wheat in Punjab province of Pakistan is grown during the Rabi (winter) season within a heterogeneous smallholder agricultural system subject to a range of pressures including water scarcity, climate change and variability, and management practices. Punjab is the breadbasket of Pakistan, representing over 70% of national wheat production. Timely estimation of cultivated wheat area can serve to inform decision-making in managing harvests with regard to markets and food security. The current wheat area and yield reporting system, operated by the Punjab Crop Reporting Service (CRS) delivers crop forecasts several months after harvest. The delayed production data cannot contribute to in-season decision support systems. There is a need for an alternative cost-effective, efficient and timely approach on producing wheat area estimates, in ensuring food security for the millions of people in Pakistan. Landsat data, medium spatial and temporal resolutions, offer a data source for characterizing wheat in smallholder agriculture landscapes. This dissertation presents methods for operational mapping of wheat cultivate area using within growing season Landsat time-series data. In addition to maps of wheat cover in Punjab, probability-based samples of in-situ reference data were allocated using the map as a stratifier. A two-stage probability based cluster field sample was used to estimate area and assess map accuracies. The before-harvest wheat area estimates from field-based sampling and Landsat map were found to be comparable to official post-harvest data produced by the CRS Punjab. This research concluded that Landsat medium resolution data has sufficient spatial and temporal coverage for successful wall-to-wall mapping of wheat in Punjab’s smallholder agricultural system. Freely available coarse and medium spatial resolution satellite data such as MODIS and Landsat perform well in characterizing industrial cropping systems; commercial high spatial resolution satellite data are often advocated as an alternative for characterizing fine-scale land tenure agricultural systems such as that found in Punjab. Commercial 5 m spatial resolution RapidEye data from the peak of the winter wheat growing season were used as sub-pixel training data in mapping wheat with the growing season free 30 m Landsat time series data from the 2014-15 growing season. The use of RapidEye to calibrate mapping algorithms did not produce significantly higher overall accuracies ( ± standard error) compared to traditional whole pixel training of Landsat-based 30 m data. Continuous wheat mapping yielded an overall accuracy of 88% (SE = ±4%) in comparison to 87% (SE = ±4%) for categorical wheat mapping, leading to the finding that sub-pixel training data are not required for winter wheat mapping in Punjab. Given sufficient expertise in supervised classification model calibration, freely available Landsat data are sufficient for crop mapping in the fine-scale land tenure system of Punjab. For winter wheat mapping in Punjab and other similar landscapes, training data for supervised classification may be collected directly from Landsat images with probability based stratified random sampling as reference data without the need for high-resolution reference imagery. The research concluded by exploring the use of automated models in wheat area mapping and area estimation using growing season Landsat time-series data. The automated classification tree model resulted in wheat / not wheat maps with comparable accuracies compared to results achieved with traditional manual training. In estimating area, automated wheat maps from previous growing seasons can serve as a stratifier in the allocation of current season in-situ reference data, and current growing season maps can serve as an auxiliary variable in model-assisted area estimation procedures. The research demonstrated operational implementation of robust automated mapping in generating timely, accurate, and precise wheat area estimates. Such information is a critical input to policy decisions, and can help to ensure appropriate post-harvest grain management to address situations arising from surpluses or shortages in crop production.Item HIGH-VOLUME RAINFALL IMPACTS AND ADAPTATION IN THE U.S. MID-ATLANTIC UNDER CLIMATE CHANGE AND URBANIZATION(2018) Khan, Ibraheem Muhammad Pasha; Hubacek, Klaus; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Water over-abundance has negative effects on proper functioning of ecosystem services. The increase in heavy precipitation events and hence stormwater quantity, due to climate change and urbanization, is a major flooding concern. These events also affect ecosystem processes leading to soil erosion and sedimentation. This dissertation draws from different disciplines and involves quantification of hydrological extremes, assessment of stormwater management resilience and analysis of impacts on ecosystem services under anticipated future changes in the U.S. Mid-Atlantic region. In Chapter 2 of the dissertation, use of precipitation capture depth and findings of likely increase in heavy precipitation events is relevant to flooding concerns at small watershed scales (~3 km2) and are valuable planning-level information for municipal stormwater management. Estimates developed in this dissertation of changes in water volume and resultant on-site infrastructure costs can help stakeholders and managers in planning for flood mitigation and protection of ecosystem services. In addition, the use of capture depth percentiles such as d85, d90, d95, and d99, have the potential to serve as meaningful hydrologic indicators for stormwater management planning. In Chapter 3, the finding of likely higher erosion rates and sediment yield in the future is a point of concern and relevant for effective land use planning. The approach to estimate representative calibration values for sediment delivery ratio model, at small scale (~3 km2) urban watersheds, is valuable for ungauged sites replacing average or theoretical calibration values. In Chapter 4, the construction of a simple curve number watershed model with reasonably good performance and few input data needs offers a possible flow simulation tool for medium to highly impervious watersheds at small scales. Moreover, the stormwater management pathways along with cost-benefit assessment using green stormwater management practices serve as a first step to determine effectiveness of certain green practices at the watershed scale. It provides insights and help identify future research needs to fill gaps in our understanding of green stormwater management practices and how they affect ecosystem services.Item QUANTIFYING VULNERABILITY OF PENINSULAR MALAYSIA’S TIGER LANDSCAPE TO FUTURE FOREST LOSS(2018) Shevade, Varada; Loboda, Tatiana V.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Agricultural expansion has been the dominant driver of tropical deforestation and increased consumption of commodities and resulting global trade have become distal drivers of land cover change. Habitat loss and fragmentation threaten biodiversity globally. Peninsular Malaysia, particularly, has a long history of land cover land use change and expansion of plantations like those of oil palm (Elaeis guineensis). Deforestation and plantation expansion threaten the Malayan tiger (Panthera tigris jacksonii), a critically endangered subspecies of the tiger endemic to the Malay Peninsula. Conservation of tigers and their long-term viability requires not only the protection of habitat patches but also maintenance of corridors connecting habitat patches. The goal of this dissertation was to understand patterns of recent forest loss and conversions, determine the drivers of these changes, and model future forest loss and changes to landscape connectivity for tigers. Satellite remote sensing data were used to map and estimate the extent of forest loss and forest conversions to plantations within Peninsular Malaysia. Mapped forest conversions to industrial oil palm plantations were used to model the factors influencing such conversions and the constraints to recent and future conversions. Finally, the mapped forest loss was used to model the deforestation probability for the region and develop scenarios of future forest loss. This study indicates that despite the history of land cover change and an extensive area under plantations, natural forest loss has continued within Peninsular Malaysia with about half of the cleared forests being converted to plantations. Proximity to pre-existing oil palm plantations is the most important determinant of forest conversions to oil palm. Such conversions are increasingly in more marginal lands indicating that biophysical suitability alone cannot determine where future conversions might take place. Forest conversions to oil palm plantations within the region are more constrained by accessibility to infrastructure rather than biophysical suitability for oil palm. The projected patterns of loss indicate lowland forests along the southeastern coast and in the center of the Peninsula are most vulnerable to future loss. This projected loss will likely reduce the connectivity between forest patches further isolating tiger populations in the southern part of the Peninsula. This study demonstrates the continued pressure on Peninsular Malaysia’s forests, the potential impact of persistent deforestation on forest connectivity, and draws attention to the need for conservation and restoration of forest linkages to ensure viability of the remaining Malayan tiger population.Item Natural Resources, Civil Conflict, and the Political Ecology of Scale(2018) Wayland, Joshua James; Geores, Martha; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation adopts a multi-scalar and mixed methods approach to interrogate the widely observed but underdefined relationship between natural resources and civil conflict. The results of three largely independent analyses are presented, corresponding to three distinct but overlapping epistemological scales and applying analytical methods appropriate to each scale. Cross-country spatial econometric analysis concluded that interstate variation in the incidence of conflict events is explained, in part, by a resource curse mechanism, whereby economic dependence on petroleum rents undermines state capacity and democratic governance, making a state more vulnerable to conflict. The results of a subnational quantitative study of the New People’s Army insurgency in the Philippines suggest that the spatial distribution of conflict risk within countries affected by civil war can be shaped by the environmental and socioeconomic impacts of resource extraction. And, a case study of a conflict over magnetite mining in the northern Philippines found that controversial resource extraction projects can create opportunities for non-state actors to develop alliances with civilian networks, discursively rescale localized disputes over resource governance to align with broader patterns of civil violence, and propagate narrative frames justifying violent collective action. From these results, a political ecology of scale in resource-related conflicts is set forth, arguing that the scalar properties of conflict vulnerability, conflict risk, and conflict opportunity have both epistemological and ontological implications; in particular, it is proposed that extractive enclaves, by fostering overlapping and intersecting scalar configurations of economic, socio-cultural, governance, and biophysical processes, constitute ‘natural habitats’ for civil conflict in which various actors can renegotiate their relative scalar positions through discursive and violent means to achieve political objectives.Item FUSING GEDI LIDAR AND TANDEM-X INSAR OBSERVATIONS FOR IMPROVED FOREST STRUCTURE AND BIOMASS MAPPING(2018) Qi, Wenlu; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The upcoming NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission presents an unprecedented opportunity to advance current global biomass estimates. However, gaps are expected between GEDI’s ground tracks, requiring the development of fusion-based methodologies to contiguously map forest biomass at satisfactory resolutions and accuracies. This dissertation is built on the complementary advantages of observations from GEDI and DLR’s TerraSAR-X/TanDEM-X (TDX)) Interferometric Synthetic Aperture Radar (InSAR) mission. To meet the goal of mapping forest structure and biomass contiguously and accurately, three types of fusion strategies have been investigated. First, a simulated GEDI-derived digital terrain model (DTM) was utilized to improve height estimation from TDX. Forest heights were initially derived from TDX coherence alone as a baseline using the widely used Random Volume over Ground (RVoG) scattering model. Here, assumptions about RVoG parameters – extinction coefficient (σ) and ground-to-volume amplitude ratio (µ) – were made. Using an external DTM derived from simulated GEDI lidar data, RVoG model was used to calculate spatially varied σ values and derived forest heights with better accuracy. TDX forest height estimation was further improved with the aid of simulated GEDI-derived DTM and canopy heights. The additional use of simulated GEDI canopy heights as RVoG input not just refined σ but also enabled the estimation of µ. Based on these parameters, forest heights were improved across three different forest types; biases were reduced from 1.7–3.8 m using only simulated GEDI DTMs to -0.9–1.1 m by using both simulated GEDI DTMs and canopy heights. Finally, wall-to-wall TDX heights were used to improve biomass estimates from simulated GEDI data over three contrasting forest types. When using simulated GEDI sampled observations alone, uncertainties were estimated statistically to be 9.0–19.9% at 1 km. These were improved to 5.2–11.7% at the same resolution by upscaling simulated GEDI footprint biomass with TDX heights. The GEDI/TDX data fusion also enabled the generation of biomass maps at a fine spatial resolution of 100 m, with uncertainties estimated to be 6.0–14.0%. Through the exploration of these fusion strategies, it has been demonstrated that a fusion-based mapping method could realize the generation of forest biomass products from GEDI with unprecedented resolutions and accuracies, while taking advantage of global seamless observations from TDX.