Biology
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Item The Impact of Marsh Sill Living Shorelines on Coastal Resilience and Stability: Insights from Maryland's Chesapeake Bay and Coastal Bays(2024) Sun, Limin; Nardin, William WN; Palinkas, Cindy CP; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Climate change and coastal urbanization are accelerating the demand for strategies to reduce shoreline erosion and enhance coastal resilience to storms and sea-level rise. Generally adverse ecological impacts of hardened infrastructure (e.g., seawalls, revetments, and dikes) have led to growing interest in alternative solutions. Living shorelines, increasingly recognized as sustainable Natural and Nature-Based Features (NNBFs; or Nature-Based Solutions (NBSs)) for their dual benefits of stabilizing shorelines while preserving or restoring coastal habitats, represent a promising approach to shoreline stabilization. Marsh sill living shorelines (created marshes with adjacent rock sills) have been extensively constructed in the Chesapeake Bay, notably in Maryland. Despite their popularity, significant uncertainties remain regarding their effectiveness and resiliency, especially during high-energy events. This dissertation investigates the dynamics of marsh sill living shorelines in Maryland’s Chesapeake Bay and Coastal Bays, aiming to fill knowledge gaps and inform effective shoreline stabilization strategies. First, the dissertation examines marsh boundary degradation into open water during high-energy conditions, contrasting mechanisms between living shorelines and natural marshes. Field surveys and numerical modeling reveal that while natural marshes experience erosion through undercutting and slumping at the scarp toe, living shorelines degrade primarily through open-water conversion at the marsh boundary behind rock sills. Differences in sediment characteristics and vegetation between the two ecosystems drive variations in marsh boundary stability between them. Next, the study assesses the impacts of rock sill placement on sediment dynamics and shoreline stability, highlighting the role of tidal gaps in enhancing sediment flux to the marsh and increasing vertical accretion during high-energy events. Numerical modeling demonstrates that while continuous sills mitigate erosion at the marsh edge of living shorelines, they diminish sediment deposition on the marsh platform compared to segmented sills with tidal gaps. Finally, the research identifies key factors driving marsh boundary degradation that are needed to assess the stability of marsh sill living shorelines. Analysis of eco-geomorphic features and hydrodynamics across 18 living shoreline sites reveals that metrics such as the Unvegetated/Vegetated Ratio (UVVR) and sediment deposition rate often used to assess the resilience of natural marshes also apply to the created marshes of living shorelines. Multivariate analyses further reveal that the Relative Exposure Index (REI) and Gap/Rock (G/R) ratio are crucial predictors of shoreline stability in marsh sill living shorelines, and thus should be particularly considered in shoreline design. By integrating remote sensing, field observations, and numerical modeling, this dissertation advances the understanding of sediment dynamics and stability in living shorelines and provides actionable insights for effective shoreline design and management to promote coastal resilience.Item INCORPORATING UNOCCUPIED AIRCRAFT SYSTEMS (UAS) AND EARTH OBSERVING SATELLITES TO ENHANCE ENVIRONMENTAL REMOTE SENSING OF CHESAPEAKE BAY(2023) Windle, Anna; Silsbe, Greg; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Environmental remote sensing is the science of monitoring physical, chemical, and biological characteristics of the Earth through space and time, and from a distance, by measuring how these environments interact with electromagnetic energy, or more simply through changes in color. This dissertation leverages in situ, satellite, and unoccupied aircraft system (UAS, drones) data to enhance the efficacy of environmental remote sensing in Chesapeake Bay. Satellite data consists of distinct contributions of the surface under observation and the intervening atmosphere. Atmospheric correction (AC) processors seek to isolate the surface signal, and while several variants exist, their accuracy varies widely in optically complex coastal waters. Chapter 2 is a statistical evaluation of four common AC variants applied to data collected by the most recent operational ocean color sensor, the Ocean Land Color Instrument (OLCI) onboard Copernicus Sentinel-3A and -3B satellites. Remote sensing reflectance (Rrs), the product of AC processors from which a suite of water quality metrics is then derived, was obtained from each AC variant and matched in space and time with in situ Rrs data collected in the Chesapeake Bay. AC results varied widely, and the most statistically robust was a neural-net based algorithm (Case 2 Regional Coast Color, C2RCC). The resultant shape and magnitude of Rrs (e.g. color) is governed by the type and concentration of optically active constituents (OACs), namely phytoplankton pigments, chromophoric dissolved organic matter, inorganic sediment, and water itself. In coastal waters where OACs are dynamic and vary independently from each other, deriving accurate water quality metrics remains an open challenge. Chapter 3 applies a spectral clustering classification of OLCI Rrs data (2016-2022) and identifies the fifteen most dominant optical water types (OWTs) of Chesapeake Bay. OWTs were matched in space and time with Chesapeake Bay water quality monitoring data, and a statistical evaluation demonstrates how water quality data are constrained within and across OWTs. In contrast to earth-observing satellites, UAS equipped with optical sensors offer on-demand, highly resolved data. Aquatic UAS applications are in their infancy, and the critical removal of light reflected directly off the skin of water has received little attention in the literature. Chapter 4 proposes four different approaches to remove direct surface reflectance from UAS imagery and evaluates each against in situ Rrs data. The most accurate method is a simple empirical model that exploits measurements in the infrared where water strongly absorbs light; applying this model permits high resolution water quality retrievals with only modest uncertainty. Chapter 5 uses UAS imagery to monitor a wetland restoration site in the Chesapeake Bay across seasons and years. A supervised random forest model is developed with UAS data and used to classify species-specific marsh vegetation with 97-99% accuracy. Vegetation classification maps were compared to as-built planting plans to delineate instances of significant marsh migration. Chapter 6 summarizes how the environmental remote sensing methods used in this dissertation can contribute to a better understanding of coastal research, monitoring, and management by addressing challenges, gaps, and potential solutions at various scales.Item Phenology of cyanobacterial blooms in three catchments of the Laurentian Great Lakes(2020) Wynne, Timothy; Hood, Raleigh R; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation discusses the cyanobacterial bloom phenology in three anthropogenically impacted regions of the Great Lakes: western Lake Erie, Saginaw Bay, and Green Bay. A detection algorithm was applied to ocean color satellite imagery, and a timeseries was constricted from each of the basins using either data from the MODIS sensor (Saginaw Bay), the MERIS sensor (Green Bay), or a combination of the two (western Lake Erie). The sensors have a high temporal resolution, collecting imagery several times a week. The algorithm used, the Cyanobacterial Index (CI), was applied to the imagery. The CI imagery was then sampled into fifteen 10-day composites throughout the bloom season (defined here as June 1 – October 31). Each of the five months will have three composites (each spanning ~10 days). From this point the bloom climatology is shown and the variability of each region is addressed. The interannual variability of the cyanobacterial blooms can be low (factor of ~2 in Saginaw Bay) or high (differing by a factor of ~20 in Green Bay and western Lake Erie). Various ancillary datasets describing the physical environment of each region were assembled including: field data, modeled data, remotely sensed data, or some combination therein. Impacts of associated cyanobacterial biotoxins were addressed and statistical models were formulated to explain any variability. The dissertation will also cross compare the three basins with one another in an effort to determine the similarities as well as differences among the regions. Management recommendations are given at the end of each of the three subsequent chapters to deter potential detrimental impacts of the blooms and their associated toxins.Item Assessing the influence of abiotic factors and leaf-level properties on the stability of growing-season canopy greenness in a deciduous forest(2016) Cunningham, Vanessa M.; Nelson, David M; Elmore, Andrew J; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Maps depicting spatial pattern in the stability of summer greenness could advance understanding of how forest ecosystems will respond to global changes such as a longer growing season. Declining summer greenness, or “greendown”, is spectrally related to declining near-infrared reflectance and is observed in most remote sensing time series to begin shortly after peak greenness at the end of spring and extend until the beginning of leaf coloration in autumn,. Understanding spatial patterns in the strength of greendown has recently become possible with the advancement of Landsat phenology products, which show that greendown patterns vary at scales appropriate for linking these patterns to proposed environmental forcing factors. This study tested two non-mutually exclusive hypotheses for how leaf measurements and environmental factors correlate with greendown and decreasing NIR reflectance across sites. At the landscape scale, we used linear regression to test the effects of maximum greenness, elevation, slope, aspect, solar irradiance and canopy rugosity on greendown. Secondly, we used leaf chemical traits and reflectance observations to test the effect of nitrogen availability and intrinsic water use efficiency on leaf-level greendown, and landscape-level greendown measured from Landsat. The study was conducted using Quercus alba canopies across 21 sites of an eastern deciduous forest in North America between June and August 2014. Our linear model explained greendown variance with an R2=0.47 with maximum greenness as the greatest model effect. Subsequent models excluding one model effect revealed elevation and aspect were the two topographic factors that explained the greatest amount of greendown variance. Regression results also demonstrated important interactions between all three variables, with the greatest interaction showing that aspect had greater influence on greendown at sites with steeper slopes. Leaf-level reflectance was correlated with foliar δ13C (proxy for intrinsic water use efficiency), but foliar δ13C did not translate into correlations with landscape-level variation in greendown from Landsat. Therefore, we conclude that Landsat greendown is primarily indicative of landscape position, with a small effect of canopy structure, and no measureable effect of leaf reflectance. With this understanding of Landsat greendown we can better explain the effects of landscape factors on vegetation reflectance and perhaps on phenology, which would be very useful for studying phenology in the context of global climate changeItem SOCIAL STRUCTURE OF ASIAN ELEPHANTS (ELEPHAS MAXIMUS) IN SRI LANKA(2015) Samy, Julie Marie; Wilkinson, Gerald S; Thompson, Katerina; Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Asian elephants (Elephas maximus) are critically endangered and live in fragmented populations spread across 13 countries. Yet in comparison to the African savannah elephant (Loxodonta africana), relatively little is known about the social structure of wild Asian elephants because the species is mostly found in low visibility habitat. A better understanding of Asian elephant social structure is critical to mitigate human-elephant conflicts that arise due to increasing human encroachments into elephant habitats. In this dissertation, I examined the social structure of Asian elephants at three sites: Yala, Udawalawe, and Minneriya National Parks in Sri Lanka, where the presence of large open areas and high elephant densities are conducive to behavioral observations. First, I found that the size of groups observed at georeferenced locations was affected by forage availability and distance to water, and the effects of these environmental factors on group size depended on site. Second, I discovered that while populations at different sites differed in the prevalence of weak associations among individuals, a core social structure of individuals sharing strong bonds and organized into highly independent clusters was present across sites. Finally, I showed that the core social structure preserved across sites was typically composed of adult females associating with each other and with other age-sex classes. In addition, I showed that females are social at all life stages, whereas males gradually transition from living in a group to a more solitary lifestyle. Taking into consideration these elements of Asian elephant social structure will help conservation biologists develop effective management strategies that account for both human needs and the socio-ecology of the elephants.Item The Effect of Hurricane Sandy on New Jersey Atlantic Coastal Marshes Evaluated with Satellite Imagery(2015) Roman, Diana Marie; Kearney, Michael S; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Hurricane Sandy, one of several large extratropical hurricanes to impact New Jersey since 1900, produced some of the most extensive coastal destruction within the last fifty years. Though the damage to barrier islands from Sandy has been well-documented, the effect of Sandy on the New Jersey coastal marshes has received little attention. The objective of this analysis, based on twenty Landsat Thematic Mapper (TM) data sets collected between 1984 and 2011 as well as three Landsat 8 Operational Land Imager (OLI) images collected between 2013 and 2014 was to determine the effect of Hurricane Sandy on the New Jersey Atlantic coastal marshes. Image processing was performed using ENVI (Environment for Visualizing Images) image analysis software with the NDX model (Rogers and Kearney, 2004). The study area was limited to the marshes located in Landsat Path 14 and Rows 32 and 33 which are the northern and southern parts of New Jersey. Validation was achieved through field work (visual estimation of vegetation density and cover) and through a marsh vegetation biomass study at five locations. Two Spot 5 data sets, covering the study area, but with a 10 m spatial resolution were used to estimate land loss between October 13, 2012 (before Sandy) and December 30, 2012 (after Sandy and after vegetation senescence occurred). Results support the conclusion that the marshland area was stable between 1984 and 2006 with only minor inter-annual variation, but has decreased steadily in the density of vegetation coverage since 2007 from different impacts including Hurricane Sandy. Hurricane Sandy caused the greatest damage to low-lying marshes located on islands close to where landfall occurred.Item Comparing SSM/I Snow Depth Estimates to In-Situ and Interpolated Multi-Source Measurements(2011) Chin-Murray, Susan Amee; Brubaker, Kaye L; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Spaceborne remote sensing data from the Special Sensor Microwave Imager (SSM/I) have been used for several decades to estimate snow depth over large regions. The SSM/I snow depth accuracy is not well quantified in non-uniform terrain. In this study, SSM/I snow depth estimates for the Columbia River Basin and surroundings in the Western USA and Canada are compared with in-situ manual snow-course measurements and interpolated snow water equivalent from the National Operational Hydrologic Remote Sensing Center. Snow depth is estimated for 25-km pixels from SSM/I brightness temperatures with the widely used Chang algorithm, adjusted for canopy cover. Interactive Data Language and ESRI ArcGIS are used to generate maps and time-series graphs, and to analyze the agreement between SSM/I snow depth and the other data sources. Measures of agreement are cross-tabulated with quantitative landscape descriptors, including: mean pixel elevation, elevation standard deviation (a measure of terrain complexity), and evergreen canopy cover.Item EXURBAN DEVELOPMENT: QUANTIFICATION, FORECAST, AND EFFECTS ON BIRD COMMUNITIES(2011) Suarez-Rubio, Marcela; Lookingbill, Todd R; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Rural landscapes in the United States have changed dramatically in recent decades due to the rapid development of private rural lands into low-density residential exurban development. This land conversion is a rising cause of concern due to its potential effects on biodiversity and ecosystem processes. Although exurbanized area is thought to have a significant increase in eastern deciduous forests, a rigorous assessment of exurban trends, drivers, and ecological consequences has yet to be undertaken. First, I develop a novel analytic approach to identify exurban areas and assess how much land has been converted to exurban development in the Mid-Atlantic region. The approach describes mixed pixels containing exurban development as a combination of land covers and uses decision-tree classification and morphological spatial pattern analysis to further separate exurban development from other forest disturbing events. The results indicate that exurban development is a pervasive and fast-growing form of land use in the region. Second, I evaluate the effectiveness of two contrasting modeling approaches in capturing exurban growth at a local and county scale. Exurban growth was effectively captured by the spatially-explicit econometric model at both scales and the pattern-based model only performed well at the county scale. Thus, pattern-based models like SLEUTH can forewarn potential coarse-scale losses of natural resources in exurban areas, but are less useful at finer scale or for assessing potential impacts of implementing land-use policies. Third, I assess whether exurban development degrades avian breeding territories over time and forest birds' response to those changes. I conclude that exurban development is degrading breeding habitats by reducing forest cover and increasing habitat fragmentation. Forest birds exhibited a threshold response to deteriorating breeding habitats in the vicinity of breeding territories and adjacent foraging areas being forest specialists the most sensitive group. To avoid the likelihood of sudden bird population declines amongst further habitat loss and fragmentation, a synergy among land managers, planners, and decision-makers will become increasingly important to mitigate the impacts of exurban development in the Mid-Atlantic region.