Geography Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2773
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Item Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico(Springer Nature, 2018-02-21) Urbazaev, Mikhail; Thiel, Christian; Cremer, Felix; Dubayah, Ralph; Migliavacca, Mirco; Reichstein, Markus; Schmullius, ChristianeInformation on the spatial distribution of aboveground biomass (AGB) over large areas is needed for understanding and managing processes involved in the carbon cycle and supporting international policies for climate change mitigation and adaption. Furthermore, these products provide important baseline data for the development of sustainable management strategies to local stakeholders. The use of remote sensing data can provide spatially explicit information of AGB from local to global scales. In this study, we mapped national Mexican forest AGB using satellite remote sensing data and a machine learning approach. We modelled AGB using two scenarios: (1) extensive national forest inventory (NFI), and (2) airborne Light Detection and Ranging (LiDAR) as reference data. Finally, we propagated uncertainties from field measurements to LiDAR-derived AGB and to the national wall-to-wall forest AGB map. The estimated AGB maps (NFI- and LiDAR-calibrated) showed similar goodness-of-fit statistics (R2, Root Mean Square Error (RMSE)) at three different scales compared to the independent validation data set. We observed different spatial patterns of AGB in tropical dense forests, where no or limited number of NFI data were available, with higher AGB values in the LiDAR-calibrated map. We estimated much higher uncertainties in the AGB maps based on two-stage up-scaling method (i.e., from field measurements to LiDAR and from LiDAR-based estimates to satellite imagery) compared to the traditional field to satellite up-scaling. By removing LiDAR-based AGB pixels with high uncertainties, it was possible to estimate national forest AGB with similar uncertainties as calibrated with NFI data only. Since LiDAR data can be acquired much faster and for much larger areas compared to field inventory data, LiDAR is attractive for repetitive large scale AGB mapping. In this study, we showed that two-stage up-scaling methods for AGB estimation over large areas need to be analyzed and validated with great care. The uncertainties in the LiDAR-estimated AGB propagate further in the wall-to-wall map and can be up to 150%. Thus, when a two-stage up-scaling method is applied, it is crucial to characterize the uncertainties at all stages in order to generate robust results. Considering the findings mentioned above LiDAR can be used as an extension to NFI for example for areas that are difficult or not possible to access.Item TOWARDS OBJECT-BASED EVALUATION OF INDIVIDUAL FIRES AT GLOBAL SCALES(2019) Humber, Michael Laurence; Justice, Christopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Fire is a complex biophysical variable that has shaped the land surface for over 400 million years and continues to play important roles in landscape management, atmospheric emissions, and ecology. Our understanding of global fire patterns has improved dramatically in recent decades, coincident with the rise of systematic acquisition and development of global thematic products based on satellite remote sensing. Currently, there are several operational algorithms which map burned area, relying on coarse spatial resolution sensors with high temporal frequencies to identify fire-affected surfaces. While wildfires have been analyzed over large areas at the pixel level, object-based methods can provide more detailed attributes about individual fires such as fire size, severity, and spread rate. This dissertation evaluates burned area products using object-based methods to quantify errors in burn shapes and to extract individual fires from existing datasets. First, a wall-to-wall intercomparison of four publicly available burned area products highlights differences in the spatial and temporal patterns of burning identified by each product. The results of the intercomparison show that the MODIS Collection 6 MCD64A1 Burned Area product mapped the most burned area out of the four products, and all products except the Copernicus Burnt Area product showed agreement with regard to temporal burning patterns. In order to determine the fitness of the MCD64A1 product for mapping fire shapes, a framework for evaluating the shape accuracy of individual fires was developed using existing object-based metrics and a novel metric, the “edge error”. The object-based accuracy assessment demonstrated that MCD64A1 preserves the fire shape well compared to medium resolution data. Based on this result, an algorithm for extracting individual fires from MCD64A1 data was developed which improves upon existing algorithms through its use of an uncertainty-based approach rather than empirically driven approaches. The individual fires extracted by this algorithm were validated against medium resolution data in Canada and Alaska using object-based metrics, and the results indicate the algorithm provides an improvement over similar datasets. Overall, this dissertation demonstrates the capability of coarse resolution burned area products to accurately identify individual fire shapes and sizes. Recommendations for future work include improving the quality assessment of burned area products and continuing research into identifying spatiotemporal patterns in fire size distributions over large areas.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 Parameterized and Machine Learning Methods for Estimating Evapotranspiration from Satellite Data(2019) Carter, Corinne Minette; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The studies in this dissertation present evaluation of and improvement to parametric and machine learning regression methods for estimating evapotranspiration from remote sensing. It includes three main parts. The first part is an assessment of parametric regression methods for obtaining evapotranspiration from vegetation index and other variables. It was found that including more variables tends to improve results, but the form of the regression formula does not make a large difference. Algorithm performance is not as good for wetland and agricultural sites as for other land cover types. Re-training of algorithms for those surface type results in some improvement. The second part consists of an evaluation of ten machine learning techniques for retrieval of evapotranspiration from surface radiation and several other variables. It is found that the best results are obtainable using all available input variables to train the bootstrap aggregation tree, random kernel, and two- and three- hidden layer neural network algorithms. Performance is again found to be weaker for wetland and agricultural surface types than for other surface types. However, separate training of the machine learning algorithms with data from those surface types does not significantly improve performance. The third part consists of further refinement to the machine learning algorithms and application of the bootstrap aggregation tree method to generate evapotranspiration maps of the continental United States for 2012. It is found that separating snow and non-snow data points improves performance. Performance for all tested algorithms was similar against the validation data set, but best for the bootstrap aggregation tree using an independent test data set. Monthly mean maps of the continental United States are generated for the drought year 2012 using the bootstrap aggregation tree. Evapotranspiration levels are lower than those shown in comparison data sets for the growing season in the eastern United States, resulting from a low bias at high evapotranspiration values. Retraining with the training data set weighted towards higher evapotranspiration values reduces this discrepancy but does not eliminate it. It is clear that machine learning evapotranspiration algorithm results have a significant dependence on training data set composition.Item Local Information Landscapes: Theory, Measures, and Evidence(2019) Lee, Myeong; Butler, Brian S; Geography/Library & Information Systems; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)To understand issues about information accessibility within communities, research studies have examined human, social, and technical factors by taking a socio-technical view. While this view provides a profound understanding of how people seek, use, and access information, this approach tends to overlook the impact of the larger structures of information landscapes that constantly shape people’s access to information. When it comes to local community settings where local information is embedded in diverse material entities such as urban places and technical infrastructures, the effect of information landscapes should be taken into account in addition to particular strategies for solving information-seeking issues. However, characterizing the information landscape of a local community at the community level is a non-trivial problem due to diverse contexts, users, and their interactions with each other. One way to conceptualize local information landscapes in a way that copes with the complexity of the interplay between information, contexts, and human factors is to focus on the materiality of information. By focusing on the material aspects of information, it becomes possible to understand how local information is provided to social entities and infrastructures and how it exists, forming structures at the community level. Through an extensive literature review, this paper develops a theory of local information landscapes (LIL Theory) to better conceptualize the community-level, material structure of local information. Specifically, the LIL theory adapts a concept of the virtual as an ontological view of the interplay between technical infrastructures, spaces, and people as a basis for assessing and explaining community-level structures of local information. By complementing existing theories such as information worlds and information grounds, this work provides a new perspective on how information deserts manifest as a material pre-condition of information inequality. Using this framework, an empirical study was conducted to examine the explicit effects of information deserts on other community characteristics. Specifically, the study aims to provide an initial assessment of LIL theory by examining how the fragmentation of local information, a form of information deserts, is related to important community characteristics such as socio-economic inequality, deprivation, and community engagement. Building upon previous work in sociology and political science, this study shows that the fragmentation of local information (1) is shaped by socio-economic deprivation/inequality that is confounded with ethnoracial heterogeneity, (2) the fragmentation of local information is highly correlated to people's community gatherings, (3) the fragmentation of local information moderates the effects of socio-economic inequality on cultural activity diversity, and (4) the fragmentation of local information mediates the relationship between socio-economic inequality and community engagement. By making use of three local event datasets over 20 months in 14 U.S. cities (about two million records) and over 3 months in 28 U.S. cities (about 620K records), respectively, this study develops computational frameworks to operationalize information deserts in a scalable way. This dissertation provides a theorization of community-level information inequality and computational models that support the quantitative examination of it. Further theorizations of the conceptual constructs and methodological improvements on measurements will benefit information policy-makers, local information system designers, and researchers who study local communities with conceptual models, vocabularies, and assessment frameworks.Item CHARACTERIZING HYDROLOGICAL PROCESSES WITHIN THE DATA-SCARCE ENVIRONMENT OF THE CONGO BASIN(2019) Munzimi, Yolande; Hansen, Matthew C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Congo Basin in Africa is the world’s second largest river basin. Centrally located and with the greatest water resources in Africa, the basin is a vital resource for water and energy supply for a continent with increasing needs for safe water and energy. The Congo Basin’s streams and rivers could be impacted by human activities in the region, notably by land cover and land use change (LCLUC) considering the strong interactions between hydrology and ecosystem processes in the humid tropics. It could impact flow discharge downstream Congo River and hydropower potential at the Inga hydroelectric site, the largest such installation in Africa, located 150km upstream from the river’s mouth. The seasonal rainfall regime, to which the Congo River owes its regular flow regime, play an important role in mediating freshwater resources. An improvement to our baseline information on the Congo’s rainfall and streamflow dynamics allows for a greater quantitative understanding of the basin’s hydrology, necessary for the current and future management of Congo Basin water resources. The hydrometeorological observation network in the Congo Basin is very limited, and this environment of scarce ground data necessitates the use of remotely-sensed data for hydrological modeling. This dissertation reports the use of hydrological modeling supported by remotely-sensed data to 1) characterize precipitation and climate in the Congo Basin, 2) characterize daily streamflow across the basin, 3) assess the hydrological response to LCLUC, including the additional response caused by climatic feedbacks following LCLUC. The study uses rainfall gauge data within the Democratic Republic of Congo (DRC) to re-calibrate a TRMM science product. It then describes a physically-based parameterization of a semi-distributed hydrological model, augmented with a spatially-distributed calibration that enables the model to simulate hydrologic processes in the Congo Basin, including the slowing effect of the basin’s central wetlands, the Cuvette Centrale. Model simulations included scenarios of 25% to 100% conversion of the Basins forest cover to agricultural mosaic and compared simulated flows to those of the current baseline conditions. The dissertation also reports on the estimated impacts of the hydrological response to LCLUC on the river’s hydropower potential. Re-calibration of TRMM improved rainfall accuracy at the gauges by 15% and correctly captured important rainfall patterns such as the ones representative of the highland climate. Model calibration of daily streamflow resulted in a model with high predictive power (Nash–Sutcliffe coefficient of efficiency of 0.70) when compared to Kinshasa gauge downstream Congo River, near its outlet. Model shows realistic seasonal and spatial patterns that can be explained by the ITCZ-driven rainfall patterns in the Congo Basin. Models of the direct effects alone of 25% to 100% forest conversion produce increases in peak flows of 7% to 8%, respectively, relative to the baseline, and decreases in low flow of 1% and 6%, for 75% and 100% forest conversion respectively, relative to the baseline. However, 25% and 50% forest conversion produce increases in low flows of 3% and 1% respectively indicating a possible sensitivity of the hydrological response to the spatial variability of forest conversion. Models of the combined direct and indirect effects of 25% to 100% conversion produce decreases in peak flows of 7% to 5% respectively and decreases in low flow of 8% to 11% respectively. Model estimates of the impacts on hydropower potential range from 11% decrease during dry season to 10% increase during rainy season, with greater impacts (year-round decrease) for increasing LCLUC models including indirect effect. The modeled loss in hydropower potential during dry season reaches -5,797 MW corresponding to the hydropower potential of countries such as Zambia or Angola and of grand projects such as the Grand Ethiopian Renaissance Dam. The dissertation has showed the adequacy of TRMM precipitation products for Congo Basin rainfall regime representation and daily flow estimation particularly in capturing the timing and the seasonality of the flow. The results of these modeling efforts can be useful in research and decision-making contexts and validate the application of satellite-based hydrologic models driven for large, data-scarce river systems such as the Congo Basin by producing reliable baseline information. We recommend a prioritization of further data collection and more gauges installation required to enable further satellite-derived data calibration and models simulations. Likewise, the results from LCLUC analysis support the need for field campaigns to better understand sub-watersheds responses and to improve the calibration of currently used simulation models.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 Developing an optimization approach for estimating incident solar radiation at earth surface from multiple satellite data(2019) Zhang, Yi; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Surface incident shortwave radiation (ISR) is a crucial parameter in the land surface radiation budget. Many reanalysis, observation-based, and satellite-derived global radiation products have been developed but often have insufficient accuracy and spatial resolution for many applications. In this dissertation, I propose an optimization-based method (OB-Algorithm) based on a radiative transfer model for estimating surface ISR from Moderate Resolution Imaging Spectroradiometer (MODIS) Top of Atmosphere (TOA) observations by optimizing the surface and atmospheric variables with a cost function. This approach consisted of two steps: retrieving surface bidirectional reflectance distribution function parameters, aerosol optical depth (AOD), and cloud optical depth (COD); and subsequently calculating surface ISR. I also adapted the algorithm to VIIRS data and performed global validation with 34 Baseline Surface Radiation Network (BSRN) sites for both instantaneous and daily mean ISR. Researches on estimating daily and diurnal ISR was also made on Advanced Himawari Imager (AHI) and Advanced Baseline Imager (ABI). Geostationary satellites capture diurnal ISR variation better than polar-orbiting ones, especially in cloudy cases. Validation against measurements at seven Surface Radiation Budget Network (SURFRAD) sites resulted in an R2 of 0.91, a bias of -6.47 W/m2, and a root mean square error (RMSE) of 84.17 W/m2 for the instantaneous results. Validation at eight high-latitude snow-covered Greenland Climate Network (GC-Net) sites resulted in an R2 of 0.86, a bias of -21.40 W/m2, and an RMSE of 84.77 W/m2. These validation results show that the proposed method is much more accurate than the previous studies (usually with RMSEs of 80-150W/m2). The VIIRS ISR results at seven SURFRAD showed RMSEs of 83.76 W/m2 and 27.78 W/m2 for instantaneous and daily ISR, respectively at SURFRAD sites. Results at 34 BSRN sites showed RMSEs of 106.68 W/m2 and 32.76 W/m2 for instantaneous and daily ISR, respectively at BSRN sites. Validation of instantaneous and daily AHI ISR at eight OzFlux sites shows an R2 of 0.93, a bias of 0.52 W/m2 and an RMSE of 106.52 W/m2 for instantaneous results and an R2 of 0.95, a bias of -0.12 W/m2 and an RMSE of 22.49 W/m2 for daily mean ISR. Validation of instantaneous and daily ABI ISR at seven SURFRAD sites shows an R2 of 0.93, a bias of 8.71 W/m2 and an RMSE of 102.30 W/m2 for instantaneous results and an R2 of 0.95, a bias of -2.38W/m2 and an RMSE of 27.17 W/m2 for daily mean ISR. Ten years of optimization-based ISR products (OB-Product) in the Amazon region from MODIS data were produced and compared with existing products. The inter-comparison with CERES-SYN and GLASS ISR products show similar spatial and temporal patterns among the three datasets. Validation of my product at BSRN sites in this area provides an improved accuracy compared to GLASS product and a significant finer resolution compared to CERES-SYN product.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.