Geography Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2773
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Item EXPLORING AND ASSESSING LAND-BASED CLIMATE SOLUTIONS USING EARTH OBSERVATIONS, EARTH SYSTEM MODELS, AND INTEGRATED ASSESSMENT MODELS(2024) Gao, Xueyuan; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Anthropogenic greenhouse gas (GHG) emissions have led the global mean temperature to increase by approximately 1.1 °C since the industrial revolution, resulting in mass ice sheet melt, sea level rise, and an increase in extreme climate events, and exposing natural and human systems to uncertainties and the risks of unsustainable development. Meeting the Paris Agreement’s climate goal of keeping temperature increases well below 2 °C — even 1.5 °C — will require removing CO2 from the atmosphere beyond reducing GHG emissions. Therefore, carbon dioxide removal and the sustainable management of global carbon cycles are one of the most urgent society needs and will become the major focus of climate action worldwide. However, research on carbon dioxide removal remains in an early stage with large knowledge gaps. The global potential and scalability, full climate consequences, and potential side effects of currently suggested carbon sequestration options — afforestation and reforestation, bioenergy with carbon capture and storage (BECCS), direct air carbon capture — are uncertain. Moreover, although about 120 national governments have a net-zero emission target, few have actionable plans for developing carbon dioxide removal.This dissertation examines two major categories of land-based carbon removal and sequestration methods: nature-based solutions that rely on the natural carbon uptake of the land ecosystem, and technology-based solutions, especially BECCS. These two options were investigated using four studies with satellite and in-situ observations, Earth system models (climate models), and integrated assessment models (policy models). Study 1 provides evidence that land ecosystem is an important carbon sink, Study 2 assesses the carbon sequestration potential of forest sustainable management via numerical experiments, Study 3 monitors recent tropical landscape restoration efforts, and Study 4 extends to BECCS and explores the impacts of future climate changes on its efficacy. Overall, this dissertation (1) improved monitoring, reporting, and verification of biomass-based carbon sequestration efforts using Earth observations, (2) improved projections on biomass-based carbon sequestration potential using Earth system models and socio-economic models, and (3) provided guidance on scaling up biomass-based carbon sequestration methods to address the climate crisis.Item Monitoring Aboveground Biomass in Forest Conservation and Restoration Areas Using GEDI and Optical Data Fusion(2024) Liang, Mengyu; Duncanson, Laura I; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Forests play a critical role in the global carbon cycle by sequestering carbon in the form of aboveground biomass. Area-based conservation measures, such as protected areas (PAs), are a cornerstone conservation strategy for preserving some of the world's most at-risk forest ecosystems. Beyond PAs, tree planting and forest restoration have been lauded as solutions to combat climate change and criticized as ways for polluters to offset carbon emissions. Consistent monitoring and quantification of forest restoration can impact decisions on future restoration activities. In this dissertation, I utilized a fusion of remote sensing assets and a combination of remote sensing with impact assessment techniques, to obtain objective baseline information for reconstructing past forest biomass conditions, and for monitoring and quantifying the patterns and success of forest regrowth in areas that underwent different forest management interventions. This overarching research goal is approached in three studies corresponding to chapters 2-4. In chapter 2, PAs’ effectiveness in storing biomass carbon and preserving forest structure is assessed on a regional scale using Global Ecosystem Dynamics Investigation (GEDI) lidar data in combination with a counterfactual analysis using statistical matching. This chapter provides an assessment of the reference condition of the biomass carbon storage capacity by one of the most stringent forest management means. The study finds that analyzed PAs in Tanzania possess 24.4% higher biomass densities than their unprotected counterparts and highlights that community-governed PAs are the most effective category of PAs at preserving forest structure and aboveground biomass density (AGBD). In chapter 3, empirical models are developed to link current (2019-2020) AGBD estimates from the GEDI with Landsat (2007-2019) at a regional scale. This will allow both current wall-to-wall biomass mapping and estimation of biomass dynamics across time. We demonstrate the utility of the method by applying it to quantify the AGBD dynamics associated with forest degradation for charcoal production. In chapter 4, the same modeling framework laid out in chapter 3 will be used to derive AGBD trajectories for 27 forest restoration sites across three biomes in East Africa. To assess the effectiveness of and compare Assisted Natural Regeneration (ANR) and Active Restoration (AR) in enhancing forest AGBD growth compared to natural regeneration (NR), we used staggered difference-in-difference (staggered DiD) to analyze the average annual AGBD change. We controlled for pre-intervention AGBD change rate between AR/ANR and NR and estimated the effectiveness with explicit consideration of intervention duration. This study finds that AR and ANR outperform NR during long-term restoration. Using the most suitable restoration interventions in each biome and timeframe, 4% suitable areas could enhance 2.40 ± 0.78 Gt (billion metric tons) forest carbon uptake over 30 years, equivalent to 3.6 years of African-wide emissions. Overall, this dissertation develops remote sensing methodological frameworks for using GEDI data and its fusion with Landsat time series to quantify and monitor forest AGBD. Moreover, by combining remote sensing-derived AGBD dynamics with impact assessment techniques, such as statistical matching and staggered DiD, the dissertation further assesses and compares different conservation and restoration means’ effectiveness in increasing AGBD and carbon uptake in forests. The dissertation therefore advances the applications of state-of-the-art remote sensing data and techniques for sustainably managing forests towards climate mitigation targets.Item 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.Item 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.Item 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.