Geography Theses and Dissertations

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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.
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.
FOREST CHANGE AND OIL PALM EXPANSION IN INDONESIA: BIOPHYSICAL AND SOCIOECONOMIC ANALYSIS
(2022) Xin, Yu; Sun, Laixiang; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Palm oil is the world's most widely used edible oil, and Indonesia has been the largest producer since 2007 and now makes up around 58% of the global market. The oil palm production has benefited the economic growth and lifted the living standards of local people in Indonesia, but this gain is often at the cost of replacing tropical forest, destructing peatland, inducing greenhouse gas (GHG) emissions, and reducing biodiversity. The expansion of oil palm plantation in Indonesia is bound to increase as the global demands continue to grow. The challenge of meeting the increased demand for oil palm products while effectively protecting tropical forest and its ecosystem services is an important tradeoff issue for both scientists and policymakers. However, little is known on the expansion patterns of oil palm in Indonesia, especially the underlying drivers with temporal and spatial details. To effectively address the knowledge gaps and deal with the challenges, this dissertation aims to first characterize the historical patterns driven by the variations in the benefits and costs of oil palm expansion across space and over time. It then projects the possible future spatial patterns and estimates the potential loss of land with high environmental values in order to meet the future global demand for oil palm products. This dissertation consists of three principle essays. The first essay identifies the major land sources of oil palm expansion in Indonesia with temporal details, and reveals the joint role of biophysical and socioeconomic drivers in shaping the spatial patterns of oil palm expansion by employing spatial panel models at the regency level. The second essay focuses on the temporal dynamics of the biophysical and socioeconomic drivers and the timing of estate crop (mainly oil palm) expansion by using Cox proportional hazard models (CPHMs) and their extensions with time-variant effects at the 1km × 1km grid level. It also explores the role of land use and land cover change (LCLUC) trajectory hopping in estate crop expansion into natural forest by introducing multi-state survival analysis to land-use science. The third essay projects the export demand for oil palm products from Indonesia by 2050 under different global trade scenarios with generalized geo-economic gravity models, and quantifies the possible tradeoffs between oil palm expansion and environmental conservation by allocating the projected demand to 1km × 1km grids across Indonesia applying parametric survival analysis. This study indicates that oil palm expansion in Indonesia has been strongly stimulated by the export value of oil palm products and prefers land with good biophysical suitability and infrastructure accessibility. As land resources become more limited, the effects of socioeconomic factors decrease following the ‘pecking order’ sequence, and the plantation expands into remote but fertile areas with high conversion costs or legal barriers. The degraded land surpassed natural forest and became the major direct land source of oil palm expansion in recent years, but degraded land had increasingly served as a land banking mechanism and a clearing-up tactic. This LCLUC trajectory hopping mechanism has made the protected area (PA) designations and sustainable development requirements become less and less effective in protecting tropical natural forest. Lowland secondary forest and peatland are the high-environmental-value (HEV) areas with the highest risks of conversion to oil palm plantation. To cope with the LCLUC trajectory hopping mechanism, Indonesia needs to have well-designed and fully enforced policies which limit/ban expansion into protected areas, peatland conversion, and deforestation of both primary and secondary forest. The country also needs more effective economic compensation mechanisms to promote more environment-friendly oil palm plantation. In this way, it is possible for Indonesia to maintain its leading position in oil palm production and exportation, while enhancing its role in environmental protection, such as climate change mitigation and biodiversity conservation. This dissertation improves our understanding of oil palm expansion in Indonesia by integrating economic science theory, advanced econometric techniques, and the best available remote-sensing data. It adds to the existing literature on analyzing the impacts of human behaviors on LCLUC at various spatial and temporal scales, especially from a longitudinal perspective.
Advanced Modeling Using Land-use History and Remote Sensing to Improve Projections of Terrestrial Carbon Dynamics
(2021) Ma, Lei; Hurtt, George; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Quantifying, attributing, and projecting terrestrial carbon dynamics can provide valuable information in support of climate mitigation policy to limit global warming to 1.5 °C. Current modeling efforts still involve considerable uncertainties, due in part to knowledge gaps regarding efficient and accurate scaling of individual-scale ecological processes to large-scale dynamics and contemporary ecosystem conditions (e.g., successional states and carbon storage), which present strong spatial heterogeneity. To address these gaps, this research aims to leverage decadal advances in land-use modeling, remote sensing, and ecosystem modeling to improve the projection of terrestrial carbon dynamics at various temporal and spatial scales. Specifically, this research examines the role of land-use modeling and lidar observations in determining contemporary ecosystem conditions, especially in forest, using the latest land-use change dataset, developed as the standard forcing for CMIP6, and observations from both airborne lidar and two state-of-the-art NASA spaceborne lidarmissions, GEDI and ICESat-2. Both land-use change dataset and lidar observations are used to initialize a newly developed global version of the ecosystem demography (ED) model, an individual-based forest model with unique capabilities to characterize fine-scale processes and efficiently scale them to larger dynamics. Evaluations against multiple benchmarking datasets suggest that the incorporation of land-use modeling into the ED model can reproduce the observed spatial pattern of vegetation distribution, carbon dynamics, and forest structure as well as the temporal dynamics in carbon fluxes in response to climate change, increased CO2, and land-use change. Further, the incorporation of lidar observations into ED, largely enhances the model’s ability to characterize carbon dynamics at fine spatial resolutions (e.g., 90 m and 1 km). Combining global ED model, land-use modeling and lidar observation together can has great potential to improve projections of future terrestrial carbon dynamics in response to climate change and land-use change.
LONG-TERM IMPACTS OF AMAZON FOREST DEGRADATION ON CARBON STOCKS AND ANIMAL COMMUNITIES: COMBINING SOUND, STRUCTURE, AND SATELLITE DATA
(2020) Rappaport, Danielle I; Dubayah, Ralph; Morton, Douglas; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The Amazon forest plays a vital role in the Earth system, yet forest degradation from logging and fire jeopardizes carbon storage and biodiversity conservation along the deforestation frontier. Polices to reduce forest carbon emissions (REDD+) will fall short of their intended goals unless carbon and biodiversity losses from forest degradation can be monitored over time. Emerging remote sensing tools, lidar and ecoacoustics, provide a means to monitor carbon and biodiversity across spatial, temporal, and taxonomic scales to address data gaps on species distributions and time-scales for recovery. This dissertation draws from a novel multi-sensor perspective to characterize the long-term ecological legacy of Amazon forest degradation across a 20,000 km2 landscape in Mato Grosso, Brazil. It combines high-density airborne lidar, 1100 hours of acoustic surveys, and annual time series of Landsat data to pursue three complementary studies. Chapter 2 establishes the bedrock of the investigation by using fine-scale measurements of structure sampled across a large diversity of degraded forests to model the initial loss and time-dependent recovery of carbon stocks and habitat structure following fire and logging. Chapter 3 models the interactions between sound and structure to predict acoustic community variation, and to account for attenuation in dense tropical forests. Lastly, Chapter 4 uses sound to go beyond structure to identify the specific degradation sequences and pseudo-taxa that give rise to variation in the ‘acoustic guild’ over time. Soundscapes reveal strong and sustained shifts in insect assemblages following fire, and a decoupling of biotic and biomass recovery following logging that defy theoretical predictions (Acoustic Niche Hypothesis). The synergies between lidar and acoustic data confirm the long-term legacy of forest degradation on both forest structure and animal communities in frontier Amazon forests. After multiple fires, forests become carbon-poor, habitats become simplified, and animal communication networks became quieter, less connected, and more homogenous. The combined results quantify large potential benefits to protecting already-burned Amazon forests from recurrent fires. This dissertation paves the way for greater integration of remote sensing and analysis tools to enhance capabilities for bringing biomass and biodiversity monitoring to scale. Building on this research with species-level and multi-temporal measurements will reduce uncertainty around the breakpoints that drive carbon and biodiversity loss following degradation.
Forest Cover Dynamics of Shifting Cultivation in the Democratic Republic of Congo
(2018) Molinario, Giuseppe Maria; Hansen, Matthew C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This dissertation is focused on contextualizing spatio-temporally forest cover loss in the DRC for the period 2000-2015 as it relates to the shifting cultivation dynamic and the rural complex mosaic. Impacts of forest loss on forest ecosystems, carbon release and biodiversity habitat differ depending on where and when it occurs relative to the rural complex. This was done by mapping the rural complex and disaggregating forest cover loss due to cyclical, livelihood shifting cultivation within three areas: 1) the baseline established rural complex (ERC) for 2000 and new 2000-2015 primary forest loss occurring as either 2) rural complex expansion (RCE) or 3) isolated forest perforations (IFP) further into core forest. Finally the influence of large-scale commercial land uses on forest cover loss is also assessed, from a spatial perspective. Between 2000 and 2010 the rural complex grew by 10% from 12% to 13% of the DRC’s land area, at an average yearly rate of 1%, while perforated forest grew by 74%, from 0.8% to 1.5% of DRC’s land area in 2010 at an average yearly rate of 0.7%. Core forest decreased by -3.8% at an average yearly rate of -0.4% per year, from 38% to 36.6% of the 2010 land area. Of particular concern is the nearly doubling of perforated forest, representing greater spatial intrusion of forest clearing within core forest areas. The land cover and land use (LCLU) components of the ERC were estimated by photo-interpreting high resolution imagery selected using a simple random sampling scheme. In the ERC 76% of land was already actively used for shifting cultivation. Therefore, together with remnant patches of primary forest (11%), an estimated 87% of the ERC was available for future shifting cultivation. Assuming a 4.6% clearing rate, this allowed estimating a ~18 year reuse rate of land in the ERC. Only 2% of the ERC area was occupied by large-scale commercial land use. This led to positing that commercial land uses might be more prevalent further away from settlements into core forest, where lower population density leads to less competition for natural resources. This hypothesis was tested by extending the probabilistic sampling analysis to new primary forest cover loss occurring outside of the ERC during the period 2000-2015. The map of the rural complex developed in Chapter 2 was validated, confirming larger proportions of primary forest and smaller proportions of shifting cultivation further away from the ERC and into core forest areas. LCLU proportions were established for both the RCE and IFP areas. Finally a concentric buffer distance analysis around sample points was used to quantify large-scale commercial land uses at the landscape scale, such as logging, mining and plantations that might be influencing shifting cultivation-driven forest cover loss. In the RCE the proportion of commercial land use was 0.4%, whereas it was 0.5% in IFPs; less than the proportion of commercial land use found in the ERC (2%). At the same time, results of the concentric buffer distance analysis show that 12% of sample points in the RCE and 9% of sample points in the IFP had commercial land uses within 5km. Commercial land uses are possibly more prevalent closer to the ERC because while there is more competition for land, there are also roads and communities that allow for the transportation of goods and provide labor. These results support the conclusion that large scale LCLU change dynamics in the DRC, such as commercial operations for export, are currently dwarfed by the reliance of rural populations on shifting cultivation. The vast majority of forest cover loss in the DRC remains due to smallholder farming not associated with commercial land uses. However, large-scale agroindustry or resource extraction activities lead to increased forest loss as their worker populations and communities rely on shifting cultivation for food, materials and energy. The spatial analysis of the rural complex allows us to peer into the future of forests in the DRC, as where isolated perforations lead, the rural complex soon follows and as the rural complex expands, so do commercial land uses.
QUANTIFYING VULNERABILITY OF PENINSULAR MALAYSIA’S TIGER LANDSCAPE TO FUTURE FOREST LOSS
(2018) Shevade, Varada; Loboda, Tatiana V.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Agricultural expansion has been the dominant driver of tropical deforestation and increased consumption of commodities and resulting global trade have become distal drivers of land cover change. Habitat loss and fragmentation threaten biodiversity globally. Peninsular Malaysia, particularly, has a long history of land cover land use change and expansion of plantations like those of oil palm (Elaeis guineensis). Deforestation and plantation expansion threaten the Malayan tiger (Panthera tigris jacksonii), a critically endangered subspecies of the tiger endemic to the Malay Peninsula. Conservation of tigers and their long-term viability requires not only the protection of habitat patches but also maintenance of corridors connecting habitat patches. The goal of this dissertation was to understand patterns of recent forest loss and conversions, determine the drivers of these changes, and model future forest loss and changes to landscape connectivity for tigers. Satellite remote sensing data were used to map and estimate the extent of forest loss and forest conversions to plantations within Peninsular Malaysia. Mapped forest conversions to industrial oil palm plantations were used to model the factors influencing such conversions and the constraints to recent and future conversions. Finally, the mapped forest loss was used to model the deforestation probability for the region and develop scenarios of future forest loss. This study indicates that despite the history of land cover change and an extensive area under plantations, natural forest loss has continued within Peninsular Malaysia with about half of the cleared forests being converted to plantations. Proximity to pre-existing oil palm plantations is the most important determinant of forest conversions to oil palm. Such conversions are increasingly in more marginal lands indicating that biophysical suitability alone cannot determine where future conversions might take place. Forest conversions to oil palm plantations within the region are more constrained by accessibility to infrastructure rather than biophysical suitability for oil palm. The projected patterns of loss indicate lowland forests along the southeastern coast and in the center of the Peninsula are most vulnerable to future loss. This projected loss will likely reduce the connectivity between forest patches further isolating tiger populations in the southern part of the Peninsula. This study demonstrates the continued pressure on Peninsular Malaysia’s forests, the potential impact of persistent deforestation on forest connectivity, and draws attention to the need for conservation and restoration of forest linkages to ensure viability of the remaining Malayan tiger population.
CHARACTERIZING RICE RESIDUE BURNING AND ASSOCIATED EMISSIONS IN VIETNAM USING A REMOTE SENSING AND FIELD-BASED APPROACH
(2018) Lasko, Kristofer; Justice, Christopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Agricultural residue burning, practiced in croplands throughout the world, adversely impacts public health and regional air quality. Monitoring and quantifying agricultural residue burning with remote sensing alone is difficult due to lack of field data, hazy conditions obstructing satellite remote sensing imagery, small field sizes, and active field management. This dissertation highlights the uncertainties, discrepancies, and underestimation of agricultural residue burning emissions in a small-holder agriculturalist region, while also developing methods for improved bottom-up quantification of residue burning and associated emissions impacts, by employing a field and remote sensing-based approach. The underestimation in biomass burning emissions from rice residue, the fibrous plant material left in the field after harvest and subjected to burning, represents the starting point for this research, which is conducted in a small-holder agricultural landscape of Vietnam. This dissertation quantifies improved bottom-up air pollution emissions estimates through refinements to each component of the fine-particulate matter emissions equation, including the use of synthetic aperture radar timeseries to explore rice land area variation between different datasets and for date of burn estimates, development of a new field method to estimate both rice straw and stubble biomass, and also improvements to emissions quantification through the use of burning practice specific emission factors and combustion factors. Moreover, the relative contribution of residue burning emissions to combustion sources was quantified, demonstrating emissions are higher than previously estimated, increasing the importance for mitigation. The dissertation further explored air pollution impacts from rice residue burning in Hanoi, Vietnam through trajectory modelling and synoptic meteorology patterns, as well as timeseries of satellite air pollution and reanalysis datasets. The results highlight the inherent difficulty to capture air pollution impacts in the region, especially attributed to cloud cover obstructing optical satellite observations of episodic biomass burning. Overall, this dissertation found that a prominent satellite-based emissions dataset vastly underestimates emissions from rice residue burning. Recommendations for future work highlight the importance for these datasets to account for crop and burning practice specific emission factors for improved emissions estimates, which are useful to more accurately highlight the importance of reducing emissions from residue burning to alleviate air quality issues.
DYNAMICS OF THE SHORT TERM AND LONG TERM AVIAN DISTRIBUTIONS IN NORTH AMERICA - THE ROLES OF VEGETATION HEIGHT HETEROGENEITY AND CLIMATIC FACTORS
(2015) Huang, Qiongyu; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Understanding how biodiversity spatially distribute over both the short term and long term, and what factors are affecting the distribution, are critical for modeling the spatial pattern of biodiversity as well as for promoting effective conservation planning and practices. This dissertation aims to examine factors that influence short-term and long-term avian distribution from the geographical sciences perspective. The research develops landscape level habitat metrics to characterize forest height heterogeneity and examines their efficacies in modelling avian richness at the continental scale. Two types of novel vegetation-height-structured habitat metrics are created based on second order texture algorithms and the concepts of patch-based habitat metrics. I correlate the height-structured metrics with the richness of different forest guilds, and also examine their efficacies in multivariate richness models. The results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of two forest bird guilds. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness. The second and the third projects focus on analyzing centroids of avian distributions, and testing hypotheses regarding the direction and speed of these shifts. I first showcase the usefulness of centroids analysis for characterizing the distribution changes of a few case study species. Applying the centroid method on 57 permanent resident bird species, I show that multi-directional distribution shifts occurred in large number of studied species. I also demonstrate, plain birds are not shifting their distribution faster than mountain birds, contrary to the prediction based on climate change velocity hypothesis. By modelling the abundance change rate at regional level, I show that extreme climate events and precipitation measures associate closely with some of the long-term distribution shifts. This dissertation improves our understanding on bird habitat characterization for species richness modelling, and expands our knowledge on how avian populations shifted their ranges in North America responding to changing environments in the past four decades. The results provide an important scientific foundation for more accurate predictive species distribution modeling in future.
Estimation of Pan-Tropical Deforestation and Implications for Conservation
(2015) KIM, DOHYUNG; Townshend, John R; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Reducing tropical deforestation has been a primary focus for the implementation of policies that are aimed at biodiversity conservation, and reducing greenhouse gas emissions, as tropical forests have, biologically, the richest ecosystem on Earth, tropical deforestation is one of the largest sources of anthropogenic carbon emission into the atmosphere, and preventing it is the most inexpensive option, in order to reduce carbon emissions and conserve biodiversity. To set the effective policies and conservation plans to reduce emission from tropical deforestation, the evaluation of effectiveness of both the current and previous efforts for conservation is critical. The three studies in this dissertation describe the development of the methods to accurately monitor pan-tropical forest cover change, using satellite remote sensing data, and their integration with the econometrics approach, to evaluate the effectiveness of the tropical forest conservation practices. The dissertation contributes a method for long-term, global forest cover change estimation from Landsat, and the methods are applied to report the first, pan-tropical forest cover change trends, between the 1990s and the 2000s. The global forest cover change product from 1990 to 2000, which was produced, based on the developed methods which are evaluated to have an overall accuracy of 88%. The results demonstrate that tropical deforestation has accelerated between the 1990s and the 2000s by 62%, which contradicts the assertions of it being decelerating. The results further show that the increased deforestation rate between the 1990s and the 2000s is significantly correlated with the increases in Gross Domestic Product (GDP) growth rate, agricultural production growth, and urban population growth between the two decades. Protected Areas (PA), throughout the tropics, avoided 83,000 ± 22,000 km2 of the deforestation during the 2000s. The effectiveness of international aid can be suppressed by weak governance and the lack of forest change monitoring capacity of each country. The conclusions of this dissertation provide a historical baseline for the estimates of tropical forest cover change, and for the evaluation of effectiveness of such conservation efforts.

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