Geography Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/2773

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Revealing the Impact of Sea Level Rise on Coastal Forest Structure in the U.S. Mid-Atlantic Using Lidar
(2024) Powell, Elisabeth Brighton; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Global vegetation regimes are undergoing notable changes from the effects of climate change. Coastal ecosystems, in particular are undergoing extensive shifts largely due to the compounding effects of sea level rise (SLR) and intense storm surges, with the Eastern coast of North America experiencing accelerated impacts due to land subsidence and the weakening of the Gulf Stream. The interplay of SLR, land subsidence, and weakened Gulf Stream exacerbates these impacts, altering the zonation from salt-tolerant marshes to upland forests. As tidal flooding increases and extends into the upland forest, elevated water and salinity levels trigger changes in ecosystem function, leading to gradual forest mortality and conversion to marshes, known as coastal transgression. However, understanding how increased tidal flooding affects forest structure and its regional variability remains limited. By leveraging lidar technology from air, land, and space, this dissertation investigates changes in low-lying forest structure induced by SLR and coastal storms, comprising three complementary studies focused on the Delaware Bay Estuary and the broader U.S. Mid-Atlantic region. First, I used multi-temporal airborne Lidar data and high-resolution imagery classify areas of rapid forest loss likely driven from episodic events in the along the Delaware Bay coast. Next, I investigated these areas of forest dieback using ground-based terrestrial laser scanning (TLS) to examine the subtle changes in forest vertical stratification from initial degradation associated with due to inundation. Lastly, I used spaceborne lidar observations to assesses the impact and extent of tidal flooding impact on forest vertical structure across the Mid-Atlantic. These studies revealed variable responses in forest structure along the forest-marsh ecotone to not only improve the delineation of the migrating forest boundary, but to also quantify the extent of degradation linked to inundation, highlighting the roles of topography and tidal influence in facilitating or resisting forest conversion into marshes. The findings of this dissertation accentuate the importance of monitoring forest structural dynamics to detect early signs of upland marsh expansion, essential for assessing changes to the overall coastal carbon sink, which underpins effective natural resource management and restoration efforts.
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.
Efficient terrain analysis of point cloud datasets on a decomposition-based data representation
(2024) Song, Yunting; De Floriani, Leila; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
With a modern focus on LiDAR (Light Detection and Ranging) technologies, which generate precise three-dimensional measurements from the Earth’s surface, the amount of spatial data in the form of massive point clouds has dramatically increased. This dissertation addresses the problem of direct terrain analysis using large LiDAR point clouds without interpolating them into gridded Digital Elevation Models (DEMs). Unlike gridded DEMs, Triangulated Irregular Networks (TINs) maintain full information of point clouds and can represent terrains with variable resolution. When using TINs to represent large terrains, the major challenges are the high storage and time costs. To address these, this dissertation introduces a family of decomposition-based data structures, named Terrain trees family, for encoding TINs. A Terrain tree employs a nested subdivision strategy, partitioning the domain of the triangle mesh into several leaf blocks. Each leaf block contains the minimum amount of information required for extracting all connectivity relations that are needed for TIN navigation and terrain analysis. A new library for terrain analysis, the Terrain trees library (TTL), is developed based on the Terrain trees. Performance evaluation of TTL shows that a Terrain tree can encode the same terrain with ~36% less storagethan the state-of-art, compact data structure while maintaining good computing performance in extracting connectivity relations. Despite the highly efficient data structure, managing large TINs on local machines remains challenging, particularly for complex analyses or simulations. Mesh simplification methods are commonly applied to reduce TIN sizes to enable downstream processing. However, these simplification methods can modify the topology of the underlying terrain in an uncontrolled manner, which affects the results of terrain analysis applications. To address this issue, a topology-aware mesh simplification method based on Terrain trees is proposed. A parallel version of this simplification method is also developed, which simplifies different leaf blocks at the same time using a shared-memory implementation. A leaf-locking strategy is employed to avoid conflicts among leaf blocks during parallel computing. TTL and the topology-aware mesh simplification methods on Terrain trees effectively lower the memory and time requirements for terrain analysis on TINs. This dissertation demonstrates the effectiveness of TIN-based models in real-world applications using sea ice topography as an example. Studying sea ice topography is crucial as it enhances our ability to monitor sea ice volume changes and comprehend sea ice processes. Besides, timely and precise assessments of sea ice dynamics are critical in the context of climate change and its impacts on polar regions. TIN-based surface models are employed to represent the sea ice surface, and methods are developed for extracting important sea ice topographic features, such as density, regions without measurements, roughness, and pressure ridge structures, from TINs.
VISUALIZATION, DATA QUALITY, AND SCALE IN COMPOSITE BATHYMETRIC DATA GENERALIZATION
(2024) Dyer, Noel Matarazza; De Floriani, Leila; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Contemporary bathymetric data collection techniques are capable of collecting sub-meterresolution data to ensure full seafloor-bottom coverage for safe navigation as well as to support other various scientific uses of the data. Moreover, bathymetry data are becoming increasingly available. Datasets are compiled from these sources and used to update Electronic Navigational Charts (ENCs), the primary medium for visualizing the seafloor for navigation purposes, whose usage is mandatory on Safety Of Life At Sea (SOLAS) regulated vessels. However, these high resolution data must be generalized for products at scale, an active research area in automated cartography. Algorithms that can provide consistent results while reducing production time and costs are increasingly valuable to organizations operating in time-sensitive environments. This is particularly the case in digital nautical cartography, where updates to bathymetry and locations of dangers to navigation need to be disseminated as quickly as possible. Therefore, this dissertation covers the development of cartographic constraint-based generalization algorithms operating on both Digital Surface Model (DSM) and Digital Cartographic Model (DCM) representations of multi-source composite bathymetric data to produce navigationally-ready datasets for use at scale. Similarly, many coastal data analysis applications utilize unstructured meshes for representing terrains due to the adaptability, which allows for better conformity to the shoreline and bathymetry. Finer resolution along narrow geometric features, steep gradients, and submerged channels, and coarser resolution in other areas, reduces the size of the mesh while maintaining a comparable accuracy in subsequent processing. Generally, the mesh is constructed a priori for the given domain and elevations are interpolated to the nodes of the mesh from a predefined digital elevation model. These methods can also include refinement procedures to produce geometrically correct meshes for the application. Mesh simplification is a technique used in computer graphics to reduce the complexity of a mesh or surface model while preserving features such as shape, topology, and geometry. This technique can be used to mitigate issues related to processing performance by reducing the number of elements composing the mesh, thus increasing efficiency. The primary challenge is finding a balance between the level of generalization, preservation of specific characteristics relevant to the intended use of the mesh, and computational efficiency. Despite the potential usefulness of mesh simplification for reducing mesh size and complexity while retaining morphological details, there has been little investigation regarding the application of these techniques specifically to Bathymetric Surface Models (BSMs), where additional information such as vertical uncertainty can help guide the process. Toward this effort, this dissertation also introduces a set of experiments that were designed to explore the effects of BSM mesh simplification on a coastal ocean model forced by tides in New York Harbor. Candidate vertices for elimination are identified using a given local maximum distance between the original vertices of the mesh and the simplified surface. Vertex removal and re-triangulation operations are used to simplify the mesh and are paired with an optional maximum triangle area constraint, which prevents the creation of new triangles over a specified area. A tidal simulation is then performed across the domain of both the original (un-simplified) and simplified meshes, while comparing current velocities, velocity magnitudes, and water levels over time at twelve representative locations in the Harbor. It was demonstrated that the simplified mesh derived from using even the strictest parameters for the mesh simplification was able to reduce the overall mesh size by approximately 26.81%, which resulted in a 26.38% speed improvement percentage compared to the un-simplified mesh. Reduction of the overall mesh size was dependent on the parameters for simplification and the speed improvement percentage was relative to the number of resulting elements composing the simplified mesh.
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.
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.

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