Geography Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2773
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Item 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.Item FUSING GEDI LIDAR AND TANDEM-X INSAR OBSERVATIONS FOR IMPROVED FOREST STRUCTURE AND BIOMASS MAPPING(2018) Qi, Wenlu; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The upcoming NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission presents an unprecedented opportunity to advance current global biomass estimates. However, gaps are expected between GEDI’s ground tracks, requiring the development of fusion-based methodologies to contiguously map forest biomass at satisfactory resolutions and accuracies. This dissertation is built on the complementary advantages of observations from GEDI and DLR’s TerraSAR-X/TanDEM-X (TDX)) Interferometric Synthetic Aperture Radar (InSAR) mission. To meet the goal of mapping forest structure and biomass contiguously and accurately, three types of fusion strategies have been investigated. First, a simulated GEDI-derived digital terrain model (DTM) was utilized to improve height estimation from TDX. Forest heights were initially derived from TDX coherence alone as a baseline using the widely used Random Volume over Ground (RVoG) scattering model. Here, assumptions about RVoG parameters – extinction coefficient (σ) and ground-to-volume amplitude ratio (µ) – were made. Using an external DTM derived from simulated GEDI lidar data, RVoG model was used to calculate spatially varied σ values and derived forest heights with better accuracy. TDX forest height estimation was further improved with the aid of simulated GEDI-derived DTM and canopy heights. The additional use of simulated GEDI canopy heights as RVoG input not just refined σ but also enabled the estimation of µ. Based on these parameters, forest heights were improved across three different forest types; biases were reduced from 1.7–3.8 m using only simulated GEDI DTMs to -0.9–1.1 m by using both simulated GEDI DTMs and canopy heights. Finally, wall-to-wall TDX heights were used to improve biomass estimates from simulated GEDI data over three contrasting forest types. When using simulated GEDI sampled observations alone, uncertainties were estimated statistically to be 9.0–19.9% at 1 km. These were improved to 5.2–11.7% at the same resolution by upscaling simulated GEDI footprint biomass with TDX heights. The GEDI/TDX data fusion also enabled the generation of biomass maps at a fine spatial resolution of 100 m, with uncertainties estimated to be 6.0–14.0%. Through the exploration of these fusion strategies, it has been demonstrated that a fusion-based mapping method could realize the generation of forest biomass products from GEDI with unprecedented resolutions and accuracies, while taking advantage of global seamless observations from TDX.Item Improved quantification of forest cover change and implications for the carbon cycle(2015) Song, Xiaopeng; Townshend, John R; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Changes in forest cover significantly affect the global carbon cycle, the hydrological cycle and biodiversity richness. This dissertation explores the potential of satellite-derived land cover datasets in quantifying changes in global forest cover and carbon stock. The research involved the following three components: 1) improving forest cover characterization, 2) developing advanced methods for detecting forest cover change (FCC) and 3) estimating the amount and trend of forest carbon change. The first component sought to improve global forest cover characterization through data fusion. Multiple global land cover maps have been generated, which collectively represent our current best knowledge of global land cover, but substantial discrepancies were found in their depiction of forest. I demonstrated that the extent and density of forest cover could be much better characterized by integrating existing datasets. However, these independent map products cannot be directly compared to quantify FCC, because post-classification change detection requires significant consistency in land cover definition, satellite data source and classification procedure. The yearly vegetation continuous field (VCF) product derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) provides a prototype that fulfills such requirement. The second component was intended to explore the features of this time series dataset in change analysis. A new algorithm called VCF-based Change Analysis was developed that can explicitly characterize the timing and intensity of FCC. The efficiency and robustness of this algorithm stem from two realistic assumptions—the spatial rarity and the temporal continuity of land cover change/modification. The developed method was applied to continental scales for mapping forest disturbance hotspots. The third component of the research combined MODIS-based deforestation indicators, a Landsat sample and a biomass dataset to estimate annual carbon emissions from deforestation with a regional focus on the Amazon basin. I found that deforestation emissions varied considerably not only across regions but also from year to year. Moreover, deforestation has been progressively encroaching into higher biomass lands in the Amazon interior. These observed deforestation and emission dynamics are expected to provide scientific support to policies on reducing emissions from deforestation and forest degradation (REDD+). The generated panel data are also of great value for evaluating forest protection policies.Item LINKING ALLOMETRIC SCALING THEORY WITH LIDAR REMOTE SENSING FOR IMPROVED BIOMASS ESTIMATION AND ECOSYSTEM CHARACTERIZATION(2015) Duncanson, Laura; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Accurate quantification of forest carbon stocks and fluxes is critical for the successful modeling and mitigation of climate change. This research focuses on forest carbon stock quantification, both in terms of testing emerging remote sensing approaches to forest carbon modeling, and examining allometric equations used to estimate biomass stocks in field plots. First, we test controversial theoretical predictions of forest allometry through the mapping of the allometric variability using field plots across the U.S. we find that there is considerable variability in forest allometry across space, largely driven by local environment and life history. However, in tall forests, allometries tend to converge toward theoretical predictions, suggesting that theory may be a useful constraint on allometry in certain forests. Second, we shift to an analysis of empirical allometries by developing an algorithm to extract individual crown information from forest systems and using it for biomass mapping and allometric equation testing. Third, we test whether individual tree structure bolsters biomass modeling capabilities in comparison to tradition, plot-aggregated LiDAR metrics. As part of this analysis we also test an allometric scaling-based approach to biomass mapping. We find that individual tree-level structure only improves biomass models when there is considerable spatial heterogeneity in the forest. Also, allometric scaling-based only worked in one study site, and failed in the other two sites because there was little or no relationship between basal area and maximum canopy height. Finally, we applied LiDAR datasets to an analysis of the effects of sample size on empirical allometry development. We found that small samples sizes tend to result in an under sampling of large stems, which yields a more linear fit than the true allometry. An assessment of the potential carbon implications of this problem yielded site-level biomass predictions with biases of 10-178%. We suggest that empirical allometric equations developed on small sample sizes, as applied in the U.S., yield potentially large errors in biomass and therefore require careful reassessment. In combination with our findings regarding the spatial variability of forest allometry, we believe that the limiting factor to forest carbon estimation is the use of allometric equations.Item MAPPING FOREST STRUCTURE AND HABITAT CHARACTERISTICS USING LIDAR AND MULTI-SENSOR FUSION(2011) Swatantran, Anuradha; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation explored the combined use of lidar and other remote sensing data for improved forest structure and habitat mapping. The objectives were to quantify aboveground biomass and canopy dynamics and map habitat characteristics with lidar and /or fusion approaches. Structural metrics from lidar and spectral characteristics from hyperspectral data were combined for improving biomass estimates in the Sierra Nevada, California. Addition of hyperspectral metrics only marginally improved biomass estimates from lidar, however, predictions from lidar after species stratification of field data improved by 12%. Spatial predictions from lidar after species stratification of hyperspectral data also had lower errors suggesting this could be viable method for mapping biomass at landscape level. A combined analysis of the two datasets further showed that fusion could have considerably more value in understanding ecosystem and habitat characteristics. The second objective was to quantify canopy height and biomass changes in in the Sierra Nevada using lidar data acquired in 1999 and 2008. Direct change detection showed overall statistically significant positive height change at footprint level (ΔRH100 = 0.69 m, +/- 7.94 m). Across the landscape, ~20 % of height and biomass changes were significant with more than 60% being positive, suggesting regeneration from past disturbances and a small net carbon sink. This study added further evidence to the capabilities of waveform lidar in mapping canopy dynamics while highlighting the need for error analysis and rigorous field validation Lastly, fusion applications for habitat mapping were tested with radar, lidar and multispectral data in the Hubbard Brook Experimental Forest, New Hampshire. A suite of metrics from each dataset was used to predict multi-year presence for eight migratory songbirds with data mining methods. Results showed that fusion improved predictions for all datasets, with more than 25% improvement from radar alone. Spatial predictions from fusion were also consistent with known habitat preferences for the birds demonstrating the potential of multi- sensor fusion in mapping habitat characteristics. The main contribution of this research was an improved understanding of lidar and multi-sensor fusion approaches for applications in carbon science and habitat studies.