The recent emergence of Structure-from-Motion Photogrammetry (SfM) has created a cost-effective alternative to conventional laser scanning for the production of high-resolution topographic datasets. There has been an explosion of applications of SfM within the geomorphological community in recent years, however, the focus of these has largely been small-scale (102 - 103 m2), building on innovations in low altitude Unmanned Aircraft Systems (UAS). This thesis examines the potential to extend the scope of SfM photogrammetry in order to quantify of landscape scale processes. This is examined through repeat surveys of a ~35 km2 reach of the Dart River, New Zealand. An initial SfM survey of this reach was conducted in April 2014, following a large landslide at the Slipstream debris fan. Validation of the resulting digital elevation models using Independent Control Point's (ICPs) suggested encouraging results, however benchmarking the survey against a long-range laser scanned surface indicated the presence of significant systematic errors associated with inaccurate estimation of the SfM bundle adjustment. Using a combination of scaled laboratory field experiments, this research aimed to develop and test photogrammetric data collection and modelling strategies to enhance modelling of 3D scene structure using limited constraints. A repeat survey in 2015 provided an opportunity to evaluate a new survey strategy, incorporating a convergent camera network and a priori measurement of camera pose. This resulted in halving of mean checkpoint residuals and a reduction in systematic error. The models produced for both 2014 and 2015 were compared using a DEM differencing (DoD) methodology to assess the applicability of wide-area SfM models for the analysis of geomorphic change detection. The systematic errors within the 2014 model confound reliable change detection, although strategies to correlate the two surveys and measure the residual change show promise. The future use of SfM over broad landscape scales has significant potential, however, this will require robust data collection and modelling strategies and improved error modelling to increase user confidence.

Cultivation of marine ecosystems began in the early Holocene and has contributed vital resources to humans over millennia. Several more recent cultivation practices, however, erode biodiversity. Emerging lines of evidence indicate that certain resource management practices may promote favourable ecological conditions. Here, I use the co-occurrence of 24 First Nations clam gardens, shellfish aquaculture farms, and unmodified clam beaches to test several hypotheses concerning the ecological implications of managing intertidal bivalve populations. To so do, in 2015 and 2016, I surveyed epifaunal (surface) and bivalve communities and quantified each intertidal sites’ abiotic conditions, including sediment characteristics and substrate composition. In 2017, I generated three-dimensional models of each site using structure-from-motion photogrammetry and measured several aspects of habitat complexity. Statistical analyses use a combination of non-parametric multivariate statistics, multivariate regression trees, and random forests to quantify the extent to which the intertidal resource cultivation structures nearshore biodiversity Chapter 1 outlines a brief history of humanity's use of marine resources, the transition from extracting to cultivating aquatic taxa, and the emergences of the northeast Pacific’s most prevalent shellfish cultivation practices: clam gardens and shellfish farms. Chapter 2 evaluates the ability of epifaunal community assessment methods to capture species diversity by conducting a paired field experiment using four assessment methods: photo-quadrat, point-intercept, random subsampling, and full-quadrat assessments. Conducting each method concurrently within multiple intertidal sites allowed me to quantify the implications of varying sampling areas, subsampling, and photo surveys on detecting species diversity, abundance, and sample- and coverage-based biodiversity metrics. Species richness, density, and sample-based rarefaction varied between methods, despite assessments occurring at the same locations, with photo-quadrats detecting the lowest estimates and full-quadrat assessments the highest. Abundance estimates were consistent among methods, supporting the use of extrapolation. Coverage-based rarefaction and extrapolation curves confirmed that these dissimilarities were due to differences between the methods, not the sample completeness. The top-performing method, random subsampling, was used to conduct Chapter 4’s surveys. Chapter 3 examines the connection between shellfish biomass and the ecological conditions clam garden and shellfish farms foster. First, I established the methodological implications of varying sediment volume on the detection of bivalve diversity, abundance, shell length, and sample- and coverage-based biodiversity metrics. Similar to Chapter 2, this examination identified the most suitable method, which I used during the 2015 and 2016 bivalve surveys. The analyses quantified several interactions between each sites’ abiotic conditions and biological communities including, the influence of substrate composition, sediment characteristics, and physical complexity on bivalve communities, and if bivalve richness and habitat complexity facilitates increases in bivalve biomass. Chapter 4 quantifies the extent to which managing intertidal bivalves enhance habitat complexity, fostering increased diversity in the epifaunal communities. This chapter combines 2015, 2016, and 2017 surveys of the sites' epifaunal communities and habitat complexity metrics, including fractal dimension at four-resolutions and linear rugosity. Clam gardens enhance fine- and broad-scale complexity, while shellfish farms primarily increase fine-scale complexity, allowing for insights into parallel and divergent community responses. Chapter 5 presents an overview of shellfish as a marine subsidy to coastal terrestrial ecosystems along the Pacific coast of North America. I identified the vectors that transport shellfish-derived nutrients into coastal terrestrial environments, including birds, mammals, and over 13,000 years of marine resource use by local people. I also examined the abundance of shellfish-derived nutrients transported, the prolonged persistence of shellfish subsidies once deposited within terrestrial ecosystems, and the ecological implications for recipient ecosystems. Chapter 6 contextualizes the preceding chapters relative to the broader literature. The objective is to provide insight into how multiple shellfish cultivation systems influence biological communities, how ecological mechanisms facilitate biotic responses, and summarize the implications for conservation planning, Indigenous resource sovereignty, and biodiversity preservation. It also explores future work, specifically the need to support efforts that pair Indigenous knowledge, and ways of knowing with Western scientific insights to address conservation challenges. / Graduate / 2022-12-13

We compared high-resolution drone laser scanning (DLS) and structure from motion (SfM) photogrammetry-derived vegetation heights at the Virginia Tech StREAM Lab to determine Manning's roughness coefficient. We utilized two calibrated approaches and a calculated approach to estimate roughness from the two data sets (DLS and SfM), then utilized them in a two-dimensional (2D) hydrodynamic model (HEC-RAS). The calculated approach used plant characteristics to determine vegetative roughness, while the calibrated approaches involved adjusting roughness values until model outputs approached values of field data (e.g., velocity probe and visual observations). We compared the model simulations to seven actual high-flow events during the fall of 2018 and 2019 using measured field data (velocity sensors, groundwater well height, marked flood extents). We used a t-test to find that all models were not significantly different to water surface elevations from our 18 wells in the floodplain (p > 0.05). There was a decrease in RMSE (-0.02 m) using the calculated compared to the calibrated models. Another decrease in RMSE was found for DLS compared to SfM (-0.01 m). This increase might not justify the increased cost of a DLS setup over SfM (~$150,000 versus ~$2,000), though future studies are needed. Our results inform hydrodynamic modeling efforts, which are becoming increasingly important for management and planning as we experience increasing high-flow events in the eastern United States due to climate change. / Master of Science / We compared high-resolution drone laser scanning (DLS) and structure from motion (SfM) photogrammetry-derived vegetation heights at the Virginia Tech StREAM Lab to improve flood modeling. DLS uses laser pulses to measure distances to create a three-dimensional (3D) point cloud of the landscape. SfM combines overlapping aerial images to create a 3D point cloud. Each method has limitations, such as cost (DLS) and accuracy (SfM). These remote sensing methods have been increasingly used to provide inputs to flood models, due to lower cost, and increased accuracy compared to airplane or satellite-based surveys. Quantifying roughness or resistance to flow can be extremely difficult and results in flood model accuracy problems. We used two forms of a calibrated approach, and a calculated approach to estimate roughness from the two data sets (DLS and SfM) which were then used in a two-dimensional (2D) flood model. We compared the model results to measured field data from seven actual high-flow events in Fall 2018 and 2019. We used statistics to determine compare the various techniques. We found that model results were not significantly different from measured water-surface elevations measured in the floodplain during floods. We also used root mean square error (RMSE) to measure the differences between modeled and observed data. There was slight decrease (-0.02 m) in error when comparing model results using the calculated and calibrated techniques. The error also decreased (-0.01 m) for simulations using the DLS versus SfM data sets. The improved accuracy due to the use of DLS might not be justified based on the increased cost of a DLS setup to SfM (~$150,000 versus ~$2,000), though future studies are needed. Insights from this analysis will help improve flood modeling, particularly as we plan for increasing high-flow events in the eastern Unites States due to climate change.