The Importance of Landscape Position Information and Elevation Uncertainty for Barrier Island Habitat Mapping and Modeling

Barrier islands provide important ecosystem services, including storm protection and erosion control to the mainland, habitat for fish and wildlife, and tourism. As a result, natural resource managers are concerned with monitoring changes to these islands and modeling future states of these environments. Landscape position, such as elevation and distance from shore, influences habitat coverage on barrier islands by regulating exposure to abiotic factors, including waves, tides, and salt spray. Geographers commonly use aerial topographic lidar data for extracting landscape position information. However, researchers rarely consider lidar elevation uncertainty when using automated processes for extracting elevation-dependent habitats from lidar data. Through three case studies on Dauphin Island, Alabama, I highlighted how landscape position and treatment of lidar elevation uncertainty can enhance habitat mapping and modeling for barrier islands. First, I explored how Monte Carlo simulations increased the accuracy of automated extraction of intertidal areas. I found that the treatment of lidar elevation uncertainty led to an 80% increase in the areal coverage of intertidal wetlands when extracted from automated processes. Next, I extended this approach into a habitat mapping framework that integrates several barrier island mapping methods. These included the use of landscape position information for automated dune extraction and the use of Monte Carlo simulations for the treatment of elevation uncertainty for elevation-dependent habitats. I found that the accuracy of dune extraction results was enhanced when Monte Carlo simulations and visual interpretation were applied. Lastly, I applied machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, to predict habitats using landscape position information extracted from topobathymetric data. I used the habitat map to assess the accuracy of the prediction model and I assessed the ability of the model to generalize by hindcasting habitats using historical data. The habitat model had a deterministic overall accuracy of nearly 70% and a fuzzy overall accuracy of over 80%. The hindcast model had a deterministic overall accuracy of nearly 80% and the fuzzy overall accuracy was over 90%. Collectively, these approaches should allow geographers to better use geospatial data for providing critical information to natural resource managers for barrier islands

The use of small-Unmanned Aerial Systems for high resolution analysis for intertidal wetland restoration schemes

Coastal and estuarine wetlands provide a range of important ecosystem services, but are currently being damaged and degraded due to human activities, reduced sediment supply and sea level rise. Managed realignment (MR) is one approach used to compensate for the loss of intertidal habitat, however saltmarshes in MR sites have been recognised to have lower biodiversity than natural environments. This has been associated with differences in the physical functioning including the sediment structure, reduced hydraulic connectivity, and lower topographic variability such as the abundance of intertidal creek networks. Intertidal morphology, including creek networks, play an important role in supporting and regulating saltmarsh environments through the supply of sediment, nutrients and water, and in draining intertidal marshes. However, there is a lack of empirical data on the formation and evolution of topographic features and variability in saltmarsh environments. This is likely to be due to creek networks in natural marshes already being in a state of quasi-equilibrium, making MR sites an ideal environment to investigate creek development. However, traditional remote sensing techniques (such as LiDAR) tend to be relatively expensive, infrequent and at a coarse resolution meaning small, but important (cm-scale), changes are often missed. This study advances the ability to detect these small scale changes by demonstrating the suitability and potential applications of using the emerging photogrammetric method Structure-from-Motion (SfM) on images taken using a small-Unmanned Aerial System (sUAS). Three surveys from a rapidly changing, near-breach site were taken at the Medmerry Managed Realignment Site in July 2016, September 2017 and July 2018. A suitable degree of confidence was found between the modelled surface and independent check points (vertical root-mean-square-errors of 0.0245, 0.0704 and 0.1571 for 2016, 2017 and 2018 respectively). DSMs of Difference (DoD) analysis was performed to evaluate elevation change, with areas experiencing up to 85 cm of accretion between 2016 and 2018. However, when considering the error associated with both surveys, between 2016 and 2017, only 34.39% of the survey area experienced change above the level of detection (LoD). In contrast, 76.97% experienced change greater than the LoD between 2017 and 2018. Stream order analysis classified the creek networks into five orders in 2016 and four orders in 2017 and 2018, with 2016 having a higher abundance (291 in 2016 compared to 117 (2017) and 112 (2018)) and density (0.44 m/m2 in 2016 compared to 0.27 m/m2 in both 2017 and 2018) of creek networks. These results provide an innovative high resolution insight into the evolution of restored intertidal wetlands, and suggest that SfM analysis of images taken using a sUAS can be a useful tool with the potential to be incorporated into studies of MR and natural saltmarsh sites. sUAS analysis can, therefore, advance the management of these environments to ensure the provision of ecosystem services and to protect against future anthropogenic activity, sea level rise and climate change

Best Practices for Elevation-Based Assessments of Sea-Level Rise and Coastal Flooding Exposure

Elevation data are critical for assessments of sea-level rise (SLR) and coastal flooding exposure. Previous research has demonstrated that the quality of data used in elevation-based assessments must be well understood and applied to properly model potential impacts. The cumulative vertical uncertainty of the input elevation data substantially controls the minimum increments of SLR and the minimum planning horizons that can be effectively used in assessments. For regional, continental, or global assessments, several digital elevation models (DEMs) are available for the required topographic information to project potential impacts of increased coastal water levels, whether a simple inundation model is used or a more complex process-based or probabilistic model is employed. When properly characterized, the vertical accuracy of the DEM can be used to report assessment results with the uncertainty stated in terms of a specific confidence level or likelihood category. An accuracy evaluation has been conducted of global DEMs to quantify their inherent vertical uncertainty to demonstrate how accuracy information should be considered when planning and implementing a SLR or coastal flooding assessment. The evaluation approach includes comparison of the DEMs with high-accuracy geodetic control points as the independent reference data over a variety of coastal relief settings. The global DEMs evaluated include SRTM, ASTER GDEM, ALOS World 3D, TanDEM-X, NASADEM, and MERIT. High-resolution, high-accuracy DEM sources, such as airborne lidar and stereo imagery, are also included to give context to the results from the global DEMs. The accuracy characterization results show that current global DEMs are not adequate for high confidence mapping of exposure to fine increments (<1 m) of SLR or with shorter planning horizons (<100 years) and thus they should not be used for such mapping, but they are suitable for general delineation of low elevation coastal zones. In addition to the best practice of rigorous accounting for vertical uncertainty, other recommended procedures are presented for delineation of different types of impact areas (marine and groundwater inundation) and use of regional relative SLR scenarios. The requirement remains for a freely available, high-accuracy, high-resolution global elevation model that supports quantitative SLR and coastal inundation assessments at high confidence levels

The Impact of Lidar Elevation Uncertainty on Mapping Intertidal Habitats on Barrier Islands

While airborne lidar data have revolutionized the spatial resolution that elevations can be realized, data limitations are often magnified in coastal settings. Researchers have found that airborne lidar can have a vertical error as high as 60 cm in densely vegetated intertidal areas. The uncertainty of digital elevation models is often left unaddressed; however, in low-relief environments, such as barrier islands, centimeter differences in elevation can affect exposure to physically demanding abiotic conditions, which greatly influence ecosystem structure and function. In this study, we used airborne lidar elevation data, in situ elevation observations, lidar metadata, and tide gauge information to delineate low-lying lands and the intertidal wetlands on Dauphin Island, a barrier island along the coast of Alabama, USA. We compared three different elevation error treatments, which included leaving error untreated and treatments that used Monte Carlo simulations to incorporate elevation vertical uncertainty using general information from lidar metadata and site-specific Real-Time Kinematic Global Position System data, respectively. To aid researchers in instances where limited information is available for error propagation, we conducted a sensitivity test to assess the effect of minor changes to error and bias. Treatment of error with site-specific observations produced the fewest omission errors, although the treatment using the lidar metadata had the most well-balanced results. The percent coverage of intertidal wetlands was increased by up to 80% when treating the vertical error of the digital elevation models. Based on the results from the sensitivity analysis, it could be reasonable to use error and positive bias values from literature for similar environments, conditions, and lidar acquisition characteristics in the event that collection of site-specific data is not feasible and information in the lidar metadata is insufficient. The methodology presented in this study should increase efficiency and enhance results for habitat mapping and analyses in dynamic, low-relief coastal environments

sUAS and Deep Learning for High-Resolution Monitoring of Tidal Marshes in Coastal South Carolina

Tidal marshes are dynamic environments, now more than ever threatened by both natural and anthropogenic forces. Best practices for monitoring tidal marshes, as well as the environmental factors that affect them, have been studied for more than 40 years. With recent technological advances in remote sensing, new capabilities for monitoring tidal marshes have emerged. One of these new opportunities and challenges is hyper-spatial resolution imagery (\u3c10 \u3ecm) that can be captured by small unmanned aerial systems (sUAS). Aside from enhanced visualization, structure-from-motion (SfM) technology can derive dense point clouds from overlapped sUAS images for high resolution digital elevation models (DEMs). Furthermore, Deep Learning (DL) algorithms, patterned after the brain’s neural networks, provide effective and efficient analysis of mass amounts of pixels in high-resolution images. In this dissertation, I seek to apply these developing geospatial technologies—sUAS and DL—to map, monitor, and model marsh vegetation. First, sUAS and coastal vegetation related literature was extensively reviewed to provide a secure foundation to build upon. Second, an above ground biomass (AGB) model of the tidal marsh vegetation Spartina Alterniflora was developed using high resolution sUAS imagery to assess marsh distribution and healthiness in the estuary. We determined that the best RGB-based index for mapping S. Alterniflora biomass was the Excess Green Index (ExG), and using a quadratic relationship we achieved an R2 of 0.376. Third, with a time series of sUAS missions, tidal marsh wrack was monitored before and after a hurricane event to map and monitor its short- and long-term effects of tidal wrack deposition on vegetation. sUAS proved to be an exceptionally capable tool for this study, revealing that 55% of wrack stayed within 10 m of a water body and wrack may persist for only 3-4 months over the same location after a hurricane event. Finally, deep learning remote sensing techniques were applied to county-wide NAIP aerial imagery to map Land Use/ Land Cover (LULC) changes of Beaufort County, South Carolina from 2009 to 2019, and to assess if and why marsh losses or gains may have occurred around the county from coastal development. We discovered that the DL U-net classifier performed the best (92.4% overall accuracy) and the largest changes in the county have come by way of forest loss for urban growth, which will impact the marshes over time. This dissertation advances the theoretical and application-based use of sUAS and DL to benefit application driven GIScientists and coastal managers in the coastal marsh realm to mitigate future negative impacts and expand our understanding of how we can protect such majestic environments