Geography Theses and Dissertations

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Characterizing the Multi-scale Post-fire Forest Structural Change in North American Boreal Forests using Air- and Space-borne Lidar Observations
(2024) Feng, Tuo; Duncanson, Laura; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Wildfire is the dominant stand-replacing disturbance regime in boreal North America, shaping the pattern, structure and composition of forested landscapes. Forest losses and gains through wildfires are two linked ecological processes, despite their varied functionalities in terrestrial carbon budgets. Combustion of forest biomass through wildfires results in the release of terrestrial carbon, whereas subsequent forest recovery process would re-sequestrate atmospheric CO2 back to the plants, and therefore at least partially offsets fire-induced carbon emissions. However, the magnitude of forest carbon fluxes and its association with wildfires is highly uncertain, especially under the context of large anomalies in fire regimes during the past few decades due to climate change. To fill the knowledge gaps, this dissertation focuses on integrations of air- and space-borne Light Detection and Ranging (lidar) to assess the magnitudes of forest structure and Aboveground Biomass Density (AGBD) changes with respect to wildfires. This dissertation starts with a systematic evaluation of multi-resolution Ice, Cloud and land Elevation Satellite -2 (ICESat-2) terrain and canopy height estimates over boreal North America. As one of the first ICESat-2 validation studies, this work demonstrates ICESat-2 as a suitable platform for large-scale terrain and canopy height measurements, and further provides a suite of standards for ICESat-2 data filtering over boreal forests. Thereafter, I analyze magnitude of forest structure and AGBD changes through wildfire events with multi-temporal airborne lidar and Landsat. This study establishes quantitative linkages between multispectral and structural measurements of fire effects on forest damage, and further reveals burn severity levels, pre-fire forest structure and fire-return intervals as dominant drivers for the magnitude of forest damage through fires. Finally, this dissertation investigates continental-scale forest recovery rate through a full-collection of high-resolution ICESat-2 observations, Landsat-based disturbance history and multi-decadal climatology records. The forest recovery rates under different warming trend are found to be converging over the past few decades, demonstrated as the growth rate of forests across high-latitudinal North gradually approaching their counterparts over Southern boreal zones. This work further reveals a positive effect of growing season warming on forest deciduousness shift, and concludes that regions with warming and associated increase in deciduous compositions would experience greatest growth rate acceleration. This dissertation leverages the potential of multi-sourced remote sensing datasets to assess spatial extents, magnitudes, and underlying drivers of forest carbon feedbacks to climate change and wildfires over North American boreal ecosystem.
DEEP LEARNING APPROACHES FOR ESTIMATING AND FORECASTING SURFACE DOWNWARD SHORTWAVE RADIATION FROM SATELLITE DATA
(2024) Li, Ruohan; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Surface downward shortwave radiation (DSR) designates solar radiation with a wavelength from 300 to 4000 nm received at the Earth’s surface. DSR plays a pivotal role in the surface energy and radiation budget, serving as the primary driver for hydrological, ecological, and biogeochemical cycles (Liang et al., 2010, 2019), and the important input for various earth models (Huang et al., 2019; Liang et al., 2010; Stephens et al., 2012). Given the rising demand for renewable energy, as well as accelerated advancements in solar energy technologies on both utility-scale and residential scale, the precision and resolution in estimating and forecasting DSR have become indispensable for planning and administering solar power plants (Gueymard, 2014; Jiang et al., 2019). This dissertation delves into the potential of integrating deep learning with satellite observations to address the deficiencies in current DSR estimation and forecasting methods, aiming to cater to the evolving needs of solar radiation estimation. The research begins by examining current DSR satellite products, emphasizing their limitations, particularly concerning spatial resolution and performance in snowy, cloudy, and high-latitude areas. In such regions, challenges arise from the degradation of radiative transfer models, band saturation, the pronounced effects of 3D cloud dynamics, and temporal resolution constraints (Li et al., 2021). Identifying these gaps, the study introduces the concept of transfer learning to tackle cases where physical methods degrade and limited training data is available. By combining data from physical simulations and ground observations, the proposed models enhance both the accuracy and adaptability of DSR predictions on a global scale. The investigation further reveals the influence of training data volume on model performance, illustrating how transfer learning can ameliorate these effects (Li et al., 2022). Moreover, the dissertation compares the application of DenseNet, Gated Recurrent Unit (GRU), and a hybrid of Convolutional Neural Network (CNN) and GRU (CNNGRU) to geostationary satellite data, achieving precise and timely DSR estimates. These models underscore their prowess in tackling 3D cloud effects and reducing dependency on additional data sources by the spatial and temporal structure of DL (Li et al., 2023b). Finally, the dissertation introduces the SolarFormer, a space-time transformer neural network adept at forecasting solar radiation up to three hours in advance at 15-minute intervals. By harnessing solely geostationary satellite imagery without the need for ground measurements, this model facilitates expansive DSR predictions, which are crucial for optimizing solar energy distribution at both utility and micro scales. This chapter also highlights the Transformer model's potential for extended forecasting due to its computational and memory efficiency.
TOPOLOGY-BASED INDIVIDUAL TREE MAPPING FROM LIDAR POINT CLOUDS
(2024) Xu, Xin; De Floriani, Leila LDF; Iuricich, Federico FI; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Light Detection and Ranging (LiDAR) techniques have dramatically enhanced our ability to characterize forest structures remotely by acquiring 3D point cloud samplings of forest shapes. Extracting individual trees from the forests plays a critical role in the automated processing pipeline of forest point cloud analysis. However, there is still a lack of automated, efficient, and easy-to-use approaches available to identify and extract individual trees in a forest point cloud. This is mainly due to inconsistent point cloud quality, diverse forest structure, and complicated plant morphology. Most existing methods require intensive parameter tuning, time-consuming user interactions, and external information (i.e., allometric function). In this dissertation, we consider the problem of extracting single-tree point clouds from input forest point clouds. We propose two novel Topology-based Tree Segmentation (TTS) approaches, namely TTS-ALS and TTS-TLS, for airborne and terrestrial laser scanning data analysis, respectively. TTS algorithms are plug-and-play by nature and controlled by at most one parameter, ensuring user-friendliness. The implemented TTS software tools can extract single trees from 3D point clouds on various forest types, including conifer trees, broadleaf deciduous forests, and evergreen subtropical trees. Compared to state-of-the-art software tools, TTS tools achieve more accurate stem localization and tree extraction results on a broad set of forest types and point densities. Further experiments show that point normalization, one preprocessing step before TTS, slightly affects the TTS-ALS's performance of detected tree locations while strongly influencing tree crowns. Compared to TTS-ALS, TTS-TLS segmentation accuracy is more sensitive to normalized points. However, TTS-TLS can effectively limit errors introduced by the preprocessing step in local regions and maintain consistent results across entire areas. Because of their reliability and generality, TTS approaches are promising for ample usage of forest LiDAR point clouds in forestry and ecology studies, such as automated forest inventory generation from point clouds. Additionally, our extra research includes a novel building footprint delineation method for ALS point clouds and a comprehensive review of tree reconstruction methods tailored to single-tree point clouds, enhancing the breadth and depth of our contribution.
INTEGRATED MONITORING OF DISTURBANCE AND FOREST ATTRIBUTES
(2024) Lu, Jiaming; Huang, Chengquan; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Forests provide numerous ecosystem services and are shaped by historical disturbance events. The intensity of disturbance significantly influences the post-disturbance forest structure, species composition, and subsequent forest regrowth. Under the influence of anthropogenic activities and climate change, disturbance regime has undergone unprecedent changes and is subsequently affecting a suite of interrelated forest attributes that are critical in understanding forests dynamics. Historical large-scale disturbance intensity information is needed for understanding the change in disturbance regimes and create linkage to forest dynamics, but such dataset was not available. Forest attributes can be estimated from the spectral information of remote sensing imagery; however, inconsistency exists among the developed product, and the usage of the dataset is limited by accuracy. To fill the research gaps, this dissertation aims to develop a framework that integrates the historical disturbance and the inter-relationship between forest attributes to provide more consistent, and likely more accurate forest attribute estimations. Age, a key attribute that can be the determinant of many ecosystem processes and tree/forest stand develop stage, was selected as the prototype attribute to study. The dissertation started by producing the first set of annual forest disturbance intensity map products quantifying thepercentage of basal area removal (PBAR) at the 30-m resolution for the CONUS from 1986 to 2015, by integrating field plot measurements collected by the Forest Inventory and Analysis program with time series Landsat observations. Compared to other published disturbance products, the maps derived through this study can provide the unique thematic (intensity) information on forest disturbances, precise details critical for understanding forest dynamics across CONUS over multiple decades. The dissertation then proceeded to quantify individual tree age. The tree age was estimated for every tree in the FIA database (over 10 million trees) across the US from our modeling approach that had higher accuracies than existing studies. The developed tree age dataset allows better characterization of tree age distribution, which is important for understanding the disturbance history, functioning, and growth vigor of forest ecosystems. With the disturbance intensity and tree age dataset, the dissertation was able to develop an integrated modeling approach for the forest age mapping. The forest age and complexity maps were produced for 2015 and 2005. The combination of the two metrics should provide a more comprehensive characterization of the forest development condition. The maps provide valuable information for knowing forest conditions, estimating forest growth and carbon sequestration potential, understanding the relationship between age and other forest attributes, evaluating forest health, and planning sustainable forest management practices. This modeling framework developed by the dissertation will enhance the ability to retrieve forest attributes in a broader scale so that with the remote sensing observation, we can not only provide spatially explicit forest structure information, but also review forest status over the decades. Furthermore, when combined with the ecosystem models, these estimations will provide a better prediction for future vegetation and climate dynamics.
Advances in Mapping Forest Biomass and Old-Growth Conditions Using Waveform Lidar
(2023) Bruening, Jamis; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne waveform lidar sys- tem that has transformed scientific understanding of the world’s forests through billions of pre- cise measurements of ecosystem structure. Relative to forest processes that operate on decadal to millennial timescales, the four year period during which GEDI collected these measurements is short, and GEDI’s ability to analyze how forest structure changes over time is mostly unproven. However, fusion efforts that integrate GEDI data with forest inventory measurements and ecosys- tem models hold immense potential for discovery. In this dissertation, I explore the limitations and capabilities of GEDI data for inference into structural and successional dynamics within east- ern US forests. First, I used a forest gap model to quantify uncertainty in biomass predictions for individual GEDI waveforms, and discovered a relationship between biomass uncertainty and successional stage. Next, I investigated uncertainties and errors in large-scale GEDI biomass estimates relative to unbiased estimates from the US forest inventory. I developed a novel mod- eling framework based on fusion between GEDI and the US forest inventory data that corrected these errors, and I produced unbiased and precise maps of forest biomass for the continental US. Lastly, I assessed GEDI’s ability to identify and map different types of old-growth forests, and discovered that GEDI can detect some old forests more effectively than others. This research identified key limitations associated with using GEDI to study forest dynamics, and I leveraged these discoveries to develop new ways of using GEDI data for ecological and successional in- ference. These discoveries will inform new uses of GEDI data and its integration with inventory data and ecosystem modeling to better characterize changes within forest ecosystems.
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.
A SYNTHETIC APERTURE RADAR (SAR)-BASED GENERALIZED APPROACH FOR SUNFLOWER MAPPING AND AREA ESTIMATION
(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.
MULTISCALE, MULTITEMPORAL ASSESSMENT OF CHIMPANZEE (Pan troglodytes) HABITAT USING REMOTELY SENSED DATASETS
(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.
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.
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.
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.
REVEALING VARIATIONS IN ENVIRONMENTAL INEQUALITY FROM PRODUCTION- AND CONSUMPTION-BASED PERSPECTIVES
(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.
CYCLING AROUND THE CLOCK: MODELING BIKE SHARE TRIPS AS HIGH-FREQUENCY SPATIAL INTERACTIONS
(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.
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.
CHANGES OF CLIMATE ZONES AND THEIR IMPLICATIONS FOR BIODIVERSITY
(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.
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.
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.
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.
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.
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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