FULL-WAVEFORM AND DISCRETE-RETURN LIDAR IN SALT MARSH ENVIRONMENTS: AN ASSESSMENT OF BIOPHYSICAL PARAMETERS, VERTICAL UNCERTATINTY, AND NONPARAMETRIC DEM CORRECTION

High-resolution and high-accuracy elevation data sets of coastal salt marsh environments are necessary to support restoration and other management initiatives, such as adaptation to sea level rise. Lidar (light detection and ranging) data may serve this need by enabling efficient acquisition of detailed elevation data from an airborne platform. However, previous research has revealed that lidar data tend to have lower vertical accuracy (i.e., greater uncertainty) in salt marshes than in other environments. The increase in vertical uncertainty in lidar data of salt marshes can be attributed primarily to low, dense-growing salt marsh vegetation. Unfortunately, this increased vertical uncertainty often renders lidar-derived digital elevation models (DEM) ineffective for analysis of topographic features controlling tidal inundation frequency and ecology. This study aims to address these challenges by providing a detailed assessment of the factors influencing lidar-derived elevation uncertainty in marshes. The information gained from this assessment is then used to: 1) test the ability to predict marsh vegetation biophysical parameters from lidar-derived metrics, and 2) develop a method for improving salt marsh DEM accuracy. Discrete-return and full-waveform lidar, along with RTK GNSS (Real-time Kinematic Global Navigation Satellite System) reference data, were acquired for four salt marsh systems characterized by four major taxa (Spartina alterniflora, Spartina patens, Distichlis spicata, and Salicornia spp.) on Cape Cod, Massachusetts. These data were used to: 1) develop an innovative combination of full-waveform lidar and field methods to assess the vertical distribution of aboveground biomass as well as its light blocking properties; 2) investigate lidar elevation bias and standard deviation using varying interpolation and filtering methods; 3) evaluate the effects of seasonality (temporal differences between peak growth and senescent conditions) using lidar data flown in summer and spring; 4) create new products, called Relative Uncertainty Surfaces (RUS), from lidar waveform-derived metrics and determine their utility; and 5) develop and test five nonparametric regression model algorithms (MARS - Multivariate Adaptive Regression, CART - Classification and Regression Trees, TreeNet, Random Forests, and GPSM - Generalized Path Seeker) with 13 predictor variables derived from both discrete and full waveform lidar sources in order to develop a method of improving lidar DEM quality. Results of this study indicate strong correlations for Spartina alterniflora (r \u3e 0.9) between vertical biomass (VB), the distribution of vegetation biomass by height, and vertical obscuration (VO), the measure of the vertical distribution of the ratio of vegetation to airspace. It was determined that simple, feature-based lidar waveform metrics, such as waveform width, can provide new information to estimate salt marsh vegetation biophysical parameters such as vegetation height. The results also clearly illustrate the importance of seasonality, species, and lidar interpolation and filtering methods on elevation uncertainty in salt marshes. Relative uncertainty surfaces generated from lidar waveform features were determined useful in qualitative/visual assessment of lidar elevation uncertainty and correlate well with vegetation height and presence of Spartina alterniflora. Finally, DEMs generated using full-waveform predictor models produced corrections (compared to ground based RTK GNSS elevations) with R2 values of up to 0.98 and slopes within 4% of a perfect 1:1 correlation. The findings from this research have strong potential to advance tidal marsh mapping, research and management initiatives

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Assessment and Correction of Lidar-derived DEMs in the Coastal Marshes of Louisiana

The onset of airborne light detection and ranging (lidar) has resulted in expansive, precise digital elevation models (DEMs). DEMs are essential for modeling complex systems, such as the coastal land margin of Louisiana. They are used for many applications (e.g. tide, storm surge, and ecological modeling) and by diverse groups (e.g. state and federal agencies, NGOs, and academia). However, in a marsh environment, it is difficult for airborne lidar to produce accurate bare-earth measurements and even accurate elevations are rarely verified by ground truth data. The accuracy of lidar in marshes is limited by the sensor’s resolution and by the laser’s ability to penetrate dense vegetation. The first objective of this work is to measure elevation using Real Time Kinematic (RTK) instruments and compare them to elevations from lidar-derived DEMs. This error evaluation (elevationDEM – elevationRTK = error) will be performed in a variety of marsh types with differing vegetation. This evaluation shows that the surveyed marshes produce minimal DEM error in relation to other published work but are still likely to result in misleading hydrodynamic and wetland modeling outcomes. The second objective is to correct lidar-derived DEMs by applying and improving upon previously published methods. The techniques will be improved through the use of additional remote sensing inputs and by understanding the ecological factors that influence the spatial and temporal distribution, composition and productivity of marsh plant species

Locality of Topographic Ground Truth Data for Salt Marsh Lidar DEM Elevation Bias Mitigation Using Machine Learning

Light detection and ranging (lidar) digital elevation models (DEMs) are crucial for modeling coastal salt marsh systems, simulating the coastal dynamics of sea level rise (SLR), and predicting storm surge inundation depth and duration. Improvements in lidar acquisition technology and data processing over the last decade have led to increased accuracy. However, the lidar-derived DEMs for coastal salt marshes that are densely vegetated are generally unreliable without adjustment based on local ground truth elevations. In this study, Random Forest (RF) DEM adjustment models are trained for two similar Northern Gulf of Mexico salt marshes. The need for local topographic ground truth data to train the models is also investigated. Two Real-Time Kinematic (RTK) GNSS field surveys were conducted by others to acquire ground truth elevations near St. Marks, Florida (n=377) and Pascagoula, Mississippi (n=610). These elevations, along with lidar elevations and Sentinel-2A multispectral satellite imagery (MSI) reflectance values were used to train the RF salt marsh DEM adjustment models and apply them under two scenarios: local and non-local. A local adjustment relies on data collected within the adjustment domain to train the model whereas a non-local adjustment uses data collected outside the adjustment domain. The RF-local models achieved the lowest mean absolute error (MAE) values for St. Marks and Pascagoula. The predictions using non-local RF models were unsatisfactory. The evidence suggests that local ground truth data are necessary for mitigating bias in salt marsh lidar DEMs, although future work should investigate if increasing the data set size could narrow the accuracy gap. This mitigation adjustment technique can be replicated in other coastal regions with similar vegetation profiles. As the world becomes increasingly vulnerable to the effects of climate change and SLR, it is important to accurately characterize the current state of the system to model marsh restoration and migration, natural and nature based protective infrastructure, and land use planning policies, for example

Improving Projections of Tidal Marsh Persistence under Climate Change with Remote Sensing and Site-Specific Data

Tidal marshes are dynamic ecosystems that are threatened by climate change and sea-level rise. To characterize baseline condition and historic climate sensitivities, and improve projections into the future, new methods are required that integrate data from the field and remote sensing platforms. Marsh elevation response models can be calibrated with site-specific data to determine the vulnerability of a marsh to sea-level rise and help guide management decisions. Elevation models are sensitive to initial elevation, the rate of accretion, and aboveground biomass. The overarching goal of this dissertation was to develop techniques to improve these important model inputs and evaluate the range of spatial and temporal variation. Light detection and ranging (lidar) is an invaluable tool for collecting elevation data, however dense vegetation prevents the accurate measurement of the tidal marsh surface. In Chapter 2, I describe the development of a technique to calibrate lidar digital elevation models with survey elevation data using readily available multispectral aerial imagery from the National Agricultural Inventory Program (NAIP). Using survey elevation data across 17 Pacific Coast tidal marshes, I demonstrated the utility of the Lidar Elevation Adjustment with NDVI (LEAN) technique to account for the positive bias in lidar due to vegetation. LEAN reduced error from an average of 23.1 cm to 7.2 cm root mean squared error and removed the positive bias caused by vegetation. This increase in accuracy will facilitate more accurate assessments of current and future vulnerability to sea-level rise. The phenology of aboveground biomass in tidal marsh plants in relation to climate variation has not been explored in the Pacific Northwest (PNW). In Chapter 3 I explain how I leveraged the Landsat archive and cloud computing capabilities to assess how Tasseled Cap Greenness (TCG, a proxy for aboveground biomass) in three PNW tidal marshes has responded to recent variation in climate to characterize sensitivity to climate change. Through analysis of over 3700 Landsat images obtained from 1984-2015, I found increased annual precipitation resulted in a higher peak TCG, while warmer May temperatures resulted in an earlier day of peak TCG. These results also demonstrate how time-series analysis of remote sensing data can be used to examine the sensitivity of tidal marsh plants to climate variability and directional change. The range of variation in tidal marsh accretion rates has not been characterized across the PNW. For Chapter 4, I collected and analyzed twenty-two soil cores from eight estuaries to estimate historic accretion rates with radioisotope dating techniques and evaluated the amount and source of variation across estuaries. I found that tidal marshes across the PNW had accretion rates greater than the current rate of sea-level rise, ranging from 2.3 – 7.3 mm yr⁻¹. Using a watershed-scale analysis, I found that long-term average annual fluvial discharge was the top predictor of tidal marsh accretion rates. Additionally, I found that calibrating the Wetland Accretion Rate Model for Ecosystem Resilience (WARMER) with accretion rate data from nearby estuaries can result in uncertainties of up to 41% (50 cm) after 100 years. Finally, in Chapter 5, I demonstrate that a range of 62 cm of error is possible in WARMER models after a 100 year simulation when both uncorrected lidar and non-local accretion rates are used, fundamentally changing the interpretation of the results. Altogether, this dissertation illustrates the importance of collecting site-specific wetland vegetation and elevation data and demonstrates how lidar and multispectral remote sensing data can be leveraged to improve our understanding of how climate variability and change impacts coastal ecosystems

Detecting the Morphology of Prograding and Retreating Marsh Margins—Example of a Mega-Tidal Bay

Retreat and progradation make the edges of salt marsh platforms their most active features. If we have a single topographic snapshot of a marsh, is it possible to tell if some areas have retreated or prograded recently or if they are likely to do so in the future? We explore these questions by characterising marsh edge topography in mega-tidal Moricambe Bay (UK) in 2009, 2013 and 2017. We first map outlines of marsh platform edges based on lidar data and from these we generate transverse topographic profiles of the marsh edge 10 m long and 20 m apart. By associating profiles with individual retreat or progradation events, we find that they produce distinct profiles when grouped by change event, regardless of event magnitude. Progradation profiles have a shallow scarp and low relief that decreases with event magnitude, facilitating more progradation. Conversely, steep-scarped, high-relief retreat profiles dip landward as retreat reveals older platforms. Furthermore, vertical accretion of the marsh edge is controlled by elevation rather than its lateral motion, suggesting an even distribution of deposition that would allow bay infilling were it not limited by the migration of creeks. While we demonstrate that marsh edges can be quantified with currently available DTMs, oblique observations are crucial to fully describe scarps and better inform their sensitivity to wave and current erosion

Radiometric Calibration of EAARL‐B Bathymetric Lidar Data

The Experimental Advanced Airborne Research Lidar ‐ B (EAARL‐B), which was built and deployed by the U.S. Geological Survey (USGS) in 2014, is a novel topographic‐bathymetric lidar system. While retaining a number of features that were pioneered in the original National Aeronautics and Space Administration (NASA) EAARL system (e.g., low energy, short pulse width, narrow receiver field of view, green‐only laser wavelength), the EAARL‐B added a split‐beam, four‐channel design to improve bathymetric data density and depth measurement range (up to 44 m in clear water). In 2014, the EAARL‐B was used to acquire bathymetric data in the U.S. Virgin Islands (USVI), in support of the National Oceanic and Atmospheric Administration (NOAA) Center for Coastal Monitoring and Assessment (CCMA) Biogeography Branch. The enhanced capabilities of the EAARL‐B system, combined with updated algorithms in the processing software, provided high‐quality data covering over 600 km2 in the USVI, filling critical data gaps. However, the EAARL‐B processing software and workflows were lacking a set of tools and procedures to exploit return waveforms (digitized samples of the backscattered signal) for generating seafloor reflectance mosaics and characterizing seafloor composition. This functionality is of significant interest to CCMA to support benthic habitat mapping and management of coral reef ecosystems. While seafloor reflectance mapping and waveform feature extraction tools do exist, in varying degrees and forms, for other bathymetric lidar systems, the extension of these capabilities to the EAARL‐B is challenging, due to the system’s unique design. The goal of this study was to address this need, through development and testing of a new set of processing procedures and algorithms for generating seafloor relative reflectance mosaics and gridded waveform features from EAARL‐B data. The procedures were developed using data from two test sites: Barnegat Bay, New Jersey, and Buck Island, north of Saint Croix. After testing and refining the methods, a seafloor relative reflectance mosaic was generated for a large site south of Saint Thomas. Additionally, raster grids of waveform shape features were produced for a smaller study site encompassing Flat Cays, south of Charlotte Amalie, Saint Thomas. The procedures have been demonstrated to enable generation of seamless seafloor data products, in which the effects of confounding variables, such as depth, incidence angle, and flight direction, have been virtually eliminated. Current research, led by project partners at NOAA CCMA and the University of New Hampshire, is focusing on using the results of this work to predict species richness, canopy cover, complexity, and coral health (including disease and bleaching) and other parameters for the USVI project sites.Keywords: intensity, Bathymetric lidar, radiometric calibratio

A review of carbon monitoring in wet carbon systems using remote sensing

Carbon monitoring is critical for the reporting and verification of carbon stocks and change. Remote sensing is a tool increasingly used to estimate the spatial heterogeneity, extent and change of carbon stocks within and across various systems. We designate the use of the term wet carbon system to the interconnected wetlands, ocean, river and streams, lakes and ponds, and permafrost, which are carbon-dense and vital conduits for carbon throughout the terrestrial and aquatic sections of the carbon cycle. We reviewed wet carbon monitoring studies that utilize earth observation to improve our knowledge of data gaps, methods, and future research recommendations. To achieve this, we conducted a systematic review collecting 1622 references and screening them with a combination of text matching and a panel of three experts. The search found 496 references, with an additional 78 references added by experts. Our study found considerable variability of the utilization of remote sensing and global wet carbon monitoring progress across the nine systems analyzed. The review highlighted that remote sensing is routinely used to globally map carbon in mangroves and oceans, whereas seagrass, terrestrial wetlands, tidal marshes, rivers, and permafrost would benefit from more accurate and comprehensive global maps of extent. We identified three critical gaps and twelve recommendations to continue progressing wet carbon systems and increase cross system scientific inquiry

High-resolution topography: tools and analysis of the life and death of salt marshes

Salt marshes are grassy platforms that develop on sheltered coasts with high sediment supply. They may be found on sub-tropical shores where they often coexist with mangrove swamps, or in temperate climates where they might front brackish and fresh wetlands. These landscapes filter pollutants, protect coastlines against storm surges, and sequester carbon at high rates, making salt marshes some of the most valuable ecosystems on Earth. However, their survival is jeopardised by imbalance between formative and destructive processes: salt marshes rely heavily on external sources of sediment, and the poor sediment supply may prevent them from recovering from wave-driven erosion or from matching accelerating sea level rise. The sustained existence of a salt marsh ecosystem depends strongly on its topographic evolution. Hence, quantifying marsh platform topography is vital to improve coastal management, and the current development of high-resolution topographic data acquisition techniques presents geomorphologists with important opportunities to achieve this objective. This thesis addresses the need for topographic analysis tools specific to the morphology of salt marshes and explores a selection of potential uses for these tools. First, I propose a novel, unsupervised method to reproducibly isolate salt marsh scarps and platforms from a Digital Terrain Model (DTM). This method takes the form of a multiple routing algorithms grouped under a single programme referred to as the Topographic Identification of Platforms (TIP). Field observations and numerical models show that salt marshes mature into subhorizontal platforms delineated by subvertical scarps. Based on this premise, the programme identifies scarps as lines of local maxima on a slope raster, then fills the DEM from the scarps upward, thus isolating mature marsh platform objects. I then test the TIP method using lidar-derived DTMs from six salt marshes in England with varying tidal ranges and geometries, for which topographic platforms were manually isolated from tidal flats. Agreement between manual and unsupervised classification exceeds 90 $\%$ for resolutions up to 3m. I also find that our method allows for the accurate detection of local block failures as small as 3 times the DTM resolution. Ultimately, I show that unsupervised classification of marsh platforms from high-resolution topography is possible and sufficient to monitor and analyse topographic evolution over time. The relevance of such monitoring is however dependant on the frequency and time-span of data acquisition, a point which I discuss further in the conclusive chapter. Second, I use the TIP method to extract the distribution of elevations of multiple marsh platforms in the United Kingdom and the United States. I compare marsh elevations relative to current sea level and run simple 0-dimensional settling simulations in order to explore constraints on suspended sediment concentration and particle size. These experiments set a basis for comparison with observed accretion rates from field sources, as lidar-derived accretion rates are found to be inaccurate. I find that the marsh platforms examined occupy a narrow range of elevations in the upper tidal frame, situated between Mean High Tide and the Observed Highest High Tide. At these elevations, accretion models using sinusoidal tidal forcing do not allow these platforms to be inundated nor experience deposition. However, when forced with year-long tidal records, I find not inconsiderable deposition rates that follow hyperbolic contour lines when expressed as a function of sediment concentration and median grain size. I find that the deposition of coarse, concentrated sediment is necessary for platforms in the upper tidal frame to immediately match sea level rise, suggesting a strong dependance on infrequent high-deposition events for short-term accretion. This is particularly true for marshes that are very high in the tidal frame, making accretion increasingly storm-driven as marsh platforms gain elevation. Finally, I reflect on the capacity of marshes to regenerate after erosion events within a context of changing sediment supply conditions and how this may affect the long-term, dynamic equilibrium of marsh platforms. Finally, I add a module to the TIP method to determine the topographic signature of retreat and progradation on the edges of salt marsh platforms in mega-tidal Moricambe Bay (UK) in 2009, 2013 and 2017. I first describe the TIP method, and from the outlines it determines I generate transverse topographic profiles of the marsh edge 10m long and 20m apart. Profiles are grouped into categories depending on whether they experienced erosion or accretion in the 2009-2013 or 2013-2017 periods respectively, and I find that profiles belonging to the same retreat or progradation event have distinctly similar morphologies, regardless of the event magnitude. Progradation profiles have a shallow scarp and low relief that decreases with event magnitude, facilitating more progradation. Conversely, steep-scarped, high-relief retreat profiles that dip away from levees as retreat reveals older platforms. Furthermore, vertical accretion of the marsh edge is found to be primarily controlled by elevation in the study site, suggesting an even distribution of deposition that would allow bay infilling were it not limited by the migration of creeks. The scope of this research within future research on marsh margins is further discussed in the conclusive chapter