High-elevation environments above 2500 metres above sea level (m.a.s.l.) were among the planet's last frontiers of human colonization. Research on the speed and tempo of this colonization process is active and holds implications for understanding rates of genetic, physiological and cultural adaptation in our species. Permanent occupation of high-elevation environments in the Andes Mountains of South America tentatively began with hunter-gatherers around 9 ka according to current archaeological estimates, though the timing is currently debated. Recent observations on the archaeological site of Soro Mik'aya Patjxa (8.0-6.5 ka), located at 3800 m.a.s.l. in the Andean Altiplano, offer an opportunity to independently test hypotheses for early permanent use of the region. This study observes low oxygen (delta O-18) and high carbon (delta C-13) isotope values in human bone, long travel distances to low-elevation zones, variable age and sex structure in the human population and an absence of non-local lithic materials. These independent lines of evidence converge to support a model of permanent occupation of high elevations and refute logistical and seasonal use models. The results constitute the strongest empirical support to date for permanent human occupation of the Andean highlands by hunter-gatherers before 7 ka.

The Province of British Columbia is developing a suite of attributes to assess and monitor forest sustainability. Each attribute is in turn evaluated using a variety of indicators. Recently, digital remote sensing technologies have emerged as both alternative and supplement to traditional monitoring techniques, with light detection and ranging (lidar) in particular showing great promise for estimating a variety of indicators. The goal of this thesis was to review and assess the ability of lidar to estimate selected indicators of forest sustainability. Specifically, digital elevation model (DEM) interpolation (from which indicators are extracted both directly and indirectly) and wildlife tree class distributions were examined. Digital elevation models are a key derivative of lidar data, and their generation is a critical step in the data processing stream. A validation exercise was undertaken to determine which combination of interpolation routine and spatial resolution was the most accurate. Ground returns were randomly subsetted into prediction and validation datasets. Linear, quintic, natural neighbour, spline with tension, regularized spline, inverse distance weighting, and ANUDEM interpolation routines were used to generate surfaces at spatial resolutions of 0.5, 1.0, and 1.5 m. The 0.5 m natural neighbour surface was found to be the most accurate (RMSE=0.17 m). Classification and regression tree analysis indicated that slope and ground return density were the best predictors of interpolation error. The amount and variability of living and dead wood in a forest stand is an important indicator of forest biodiversity. In the second study, the capacity of lidar to estimate the distribution of living and dead trees within forests is investigated. Twenty-two field plots were established in which each stem (DBH>10cm) was assigned to a wildlife tree (WT) class. For each plot, a suite of lidar-derived predictor variables were extracted. Ordinal logistic regression was then employed to predict the cumulative proportions of stems within the WT classes. Results indicated that the coefficient of variation of the lidar height data was the best predictor variable (r = 0.85, p <0.000, RMSE = 4.9%). The derived relationships allowed for the prediction of the proportion of stems within WT classes across the landscape. / Forestry, Faculty of / Graduate

There has been a marked increase in melt season length over the past two decades on glaciers and ice caps within Canada’s Queen Elizabeth Islands (QEI). Prior to the year ~2000 land ice was in a state of slightly negative mass balance (-11 +/- 11.5 Gt yr⁻¹ over 1958-1995), but recent GRACE measurements suggest that mass losses averaged -33 +/- 5 Gt yr⁻¹ between 2003-2015. These losses have primarily been attributed to meltwater runoff, making the QEI one of the largest recent contributors to sea level rise outside of the ice sheets. Despite these losses, there is a lack of information concerning how a warming climate is affecting small (<1 km²) ice bodies, which are considered sensitive indicators of change due to their short response time. In this study, historical and contemporary aerial photographs, high resolution optical satellite imagery, and ground penetrating radar (GPR) surveys are used to determine area, thickness, mass and volume changes of Adams Icefield within Expedition Fiord, Axel Heiberg Island, Nunavut, over the past seven decades (1948/59-2019). Area changes are determined from a comparison of air photos acquired in 1948/59 with satellite images acquired since 1979. Contemporary (2001, 2012, 2019) digital elevation models (DEMs) were either collected or created from stereo satellite images, and via aerial photo surveys using Structure from Motion photogrammetry. DEM of Difference maps calculated from these DEMs provide volume and mass changes. Results illustrate a steady reduction in glacier area, thickness, and volume prior to the year ~2000, followed by a rapid increase in losses over the past two decades. As a result, Adams Icefield is now rapidly declining and is likely to completely disappear early in the twenty-second century.

Studying past landscapes existing before the modern era (pre-1945) carries great difficulty. Historical maps can offer insight to researchers, but the two-dimensional cartographic features on these maps remain largely inaccessible for geospatial analyses. This study investigates the idea of unlocking the data within historical maps to be utilized by Geographic Information Systems (GIS). To realize this goal, the cartographic features must be extracted and converted into digital vector (line) and raster (grid) data. For the purposes of this study, we focus on the extraction of elevation contour lines in United States Geological Survey (USGS) historical topographic maps. These lines are converted into Digital Elevation Models (DEMs), thus creating historically accurate digital landscapes. To ensure a high-quality result, the topographically derived DEMs (TOPO-DEMs) are compared to modern satellite-derived DEMs. The implications of this study can be directly applied to historical, archeological, and environmental research.

Maximum sustained and peak gust winds from eighteen European windstorms over the last 25 years were analyzed previously to develop surface-level wind predictions across a large and topographically varied landscape based on an anisotropic kriging interpolation methodology for meteorological station data. Results suggested that coastal and mountainous areas experience the highest wind speeds and highest variability over short distances, resulting in the highest errors across concurrent interpolated surfaces. This study utilizes covariates in conjunction with cokriging to investigate the use of cokriging as a method of improvement through the interpolation of five windstorms that impacted both the Alps region and the topographically-varied coastal regions of Western Europe. Results show that cokriging improves isotach interpolation for windstorms in 8 out of 10 models by reducing root mean square error and the total number of high-error stations, primarily in coastal and mountainous areas. Land cover alone contributed to the greatest model improvement in a majority of the models, while aspect and elevation (singularly and collectively) also improved models when compared to original kriging models. Improved surface interpolation is critical for improved understanding of macro-scale windstorm patterns and resulting damage, thus improving risk and vulnerability estimates.

Copepods in high elevation lakes and ponds in Colorado are exposed to significant levels of ultraviolet radiation (UV), necessitating development of UV avoidance behavior and photoprotective physiological adaptations. The copepods are brightly pigmented due to accumulation of astaxanthin, a carotenoid which has photoprotective and antioxidant properties. Astaxanthin interacts with a crustacyanin-like protein, shifting its absorbance from 473 nm (hydrophobic free form, appears red) to 632 nm (protein-bound complex, appears blue). In six sites in Colorado, habitat-specific coloration patterns related to carotenoprotein complex have been observed. The objective of this study was to determine whether pigment accumulation or carotenoprotein expression has a greater effect on resistance to UV exposure. For each site, copepod tolerance to UV was assessed by survivorship during UV exposure trials. Average UV exposure was determined for each habitat. Astaxanthin profiles were generated for copepods in each site. Ability to withstand UV exposure during exposure trials was significantly different between color morphs (p < 0.0001). Red copepods were found to tolerate 2-fold greater levels of UVB than blue or mixed copepods. Additionally, red copepods have much higher levels of total astaxanthin than blue or mixed copepods (p < 0.0001) and receive a higher daily UV dose (p < 0.0003). Diaptomid carotenoprotein sequence is not homologous with that of other crustaceans in which crustacyanin has been characterized which prevented quantification of carotenoprotein transcript expression. Overall, diaptomid color morph may be an important indicator of UV conditions in high elevation lentic ecosystems.

To meet the surging water demand due to rapid population growth and changing climatic conditions around the world, and to reduce the impact of floods and droughts, comprehensive water management and planning is necessary. Climatic variability, hydrologic uncertainty and variability of hydrologic quantities in time and space are inherent to hydrological modeling. Hydrologic modeling using a physically-based model can be very complex and typically requires detailed knowledge of physical processes. The availability of data is an important issue to justify the use of these models. Data-driven models are an alternative choice. This is a relatively new and efficient approach to modeling. Data-drive models bridge the gap between the classical regression and physically-based models. By using a data-driven model that relies on the machine learning approach, it is possible to produce reasonable predictions from a limited data set and limited knowledge of underlying physical processes of the system by just relating input and output. This dissertation uses the Multivariate Relevance Vector Machine (MVRVM) and Support Vector Machine (SVM) for predicting a variety of hydrological quantities. These models are used in this dissertation for identifying influential climate indicators, and are used for long-term streamflow prediction for multiple lead times at different locations in Utah. They are also used for prediction of Great Salt Lake (GSL) elevation series. They provide reasonable predictions of hydrological quantities from the available data. The predictions from these models are robust and parsimonious. This research presents the first attempt to identify influential climate indicators and predict long lead-time streamflow in Utah, and to predict lake elevation using machine learning models. The approach presented herein has potential value for water resources planning and management especially for irrigation and flood management.

No description available.

Multiple SAR interferograms with judiciously selected height sensitivities can be iteratively combined to create a high accuracy digital elevation map. An initial height estimate is refined by iteratively using larger baselines to obtain a height estimation accuracy limited by the spatial decorrelation of the antenna baseline. Spatial filtering is used to reduce the propagation of errors for accurate height estimation. Images containing regions isolated by phase discontinuities, as often found in urban environments, can be resolved by this iterative multi-baseline technique. Computationally demanding and potentially unreliable phase unwrapping is not required to determine scene elevation using SAR inMultiple SAR interferograms with judiciously selected height sensitivities can be iteratively combined to create a high accuracy digital elevation map. An initial height estimate is refined by iteratively using larger baselines to obtain a height estimation accuracy limited by the spatial decorrelation of the antenna baseline. Spatial filtering is used to reduce the propagation of errors for accurate height estimation. Images containing regions isolated by phase discontinuities, as often found in urban environments, can be resolved by this iterative multi-baseline technique. Computationally demanding and potentially unreliable phase unwrapping is not required to determine scene elevation using SAR interferometry.

Archaeological data has increased significantly with Cultural Resource Management agencies finding and recording archaeological sites all across Utah. With the site data from the Utah State Historical Preservation Office, I examine the expansion of Archaic sites in the Eastern Great Basin from the Early Archaic through the Late Archaic, through the lens of elevation and ecological zones and proximity to wetland resources. I argue that the aridness of the Middle Holocene caused the people to expand into the mountains of Utah, and that the expansion continued into the Late Archaic period, even though the environment became more moist again. I also argue that the people of the Archaic stayed near to wetlands and wetland resources throughout the Archaic.