Forward and Inverse Modeling of GPS Multipath for Snow Monitoring

Snowpacks provide reservoirs of freshwater, storing solid precipitation and delaying runoff to be released later in the spring and summer when it is most needed. The goal of this dissertation is to develop the technique of GPS multipath reflectometry (GPS-MR) for ground-based measurement of snow depth. The phenomenon of multipath in GPS constitutes the reception of reflected signals in conjunction with the direct signal from a satellite. As these coherent direct and reflected signals go in and out of phase, signal-to-noise ratio (SNR) exhibits peaks and troughs that can be related to land surface characteristics. In contrast to other GPS reflectometry modes, in GPS-MR the poorly separated composite signal is collected utilizing a single antenna and correlated against a single replica. SNR observations derived from the newer L2-frequency civilian GPS signal (L2C) are used, as recorded by commercial off-the-shelf receivers and geodetic-quality antennas in existing GPS sites. I developed a forward/inverse approach for modeling GPS multipath present in SNR observations. The model here is unique in that it capitalizes on known information about the antenna response and the physics of surface scattering to aid in retrieving the unknown snow conditions in the antenna surroundings. This physically-based forward model is utilized to simulate the surface and antenna coupling. The statistically-rigorous inverse model is considered in two parts. Part I (theory) explains how the snow characteristics are parameterized; the observation/parameter sensitivity; inversion errors; and parameter uncertainty, which serves to indicate the sensing footprint where the reflection originates. Part II (practice) applies the multipath model to SNR observations and validates the resulting GPS retrievals against independent in situ measurements during a 1-3 year period in three different environments - grasslands, alpine, and forested. The assessment yields a correlation of 0.98 and an RMS error of 6-8 cm, with the GPS under-estimating in situ snow depth by approximately 15%. GPS daily site averages were found effective in mitigating random noise without unduly smoothing the sharp transitions as captured in new snow events. This work corroborates the readiness of quality-controlled GPS-MR for snow depth monitoring, reinforcing its maturity for operational usage

Glucocorticoid Metabolites and GPS Radio Collar Telemetry in Wildlife Conservation: The Jane Goodall Institute Mandrill Release Project in the Republic of Congo

Wildlife populations are being depleted globally by pressures associated with the growing human population and non-human primate populations are in sharp decline. The Tchimpounga Chimpanzee Rehabilitation Sanctuary in the Republic of Congo cares for orphaned primates with the goal of reintroducing them to the wild when appropriate. The primary aim of this study was to reintroduce the mandrills held at Tchimpounga into Conkouati-Douli National Park following the International Union for Conservation of Nature (IUCN) Guidelines as closely as possible. In preparation for the release we built an enclosure at the selected release site and tested the global positioning system (GPS) collars the animals would be wearing. At the end of the study we retrieved the GPS collars and found fewer successful fixes than expected and analysed the collar fix success rates in relationship to each individual’s use of three-dimensional space and mass in an effort to understand the lack of successful fixes. We found that vegetation density and collar height within the vegetation significantly affected fix success rates. Our post-release data indicated larger animals spent more time on the ground than smaller animals, and that smaller animals had more successful fixes. We found variation in GPS collar function and that how the animals interact with their three-dimensional (3D) environment affects collar function. If animals in a study group spend different amounts of time at different heights in the forest it could bias the data. Researchers should thus test the collars they will be using for height bias in circumstances where the release subjects have a 3D relationship with the environment around them. We recommend accounting for an animals’ use of three-dimensional space in GPS collar studies where the species is not fully terrestrial and vegetation, topography, or human-made structures are likely to interfere with their collars’ access to satellites. We also used non-invasive faecal sampling to measure the mandrills’ glucocorticoid metabolite levels as a biological proxy for their stress response to each stage of the release. The findings suggest that faecal glucocorticoid metabolites can be used to capture the biological response to the stages of reintroduction. All mandrills had an increase in glucocorticoid metabolite values post transfer. It took 4 weeks for the glucocorticoid metabolite values to decrease although there was variation amongst individuals. We recommend using faecal glucocorticoid metabolite analysis in release projects to inform decisions about how long the study species should be held in a pre-release enclosure to overcome the stress of transfer and habituate to their surroundings prior to being released. The findings of this study also highlighted that different animals reacted differently to the stages of the release process, thus researchers should assess animals as individuals rather than a group to assure maximum animal welfare through the release process. Ultimately, through scientifically testing aspects of this release project we gained insight to inform future mandrill releases as well as wildlife release projects generally. We recommend GPS collars are tested in the release area and the results are reviewed prior to fitting the collars to the animals. GPS collar studies should account for an animal’s 3D relationship with topographical obstruction and vegetation within their environment because systematic differences in forest usage can bias collar data. Finally, we recommend sanctuary release projects use soft release methods unless hard release had been thoroughly validated for the species under representative circumstances

Habitat modeling using path analysis: delineating mountain goat habitat in the Washington Cascades

A 70-90% decline in mountain goat (Oreamnos americanus) populations in Washington State over the past few decades has spurred the need for an improved understanding of seasonal goat-habitat relationships. Habitat use data have been collected from 46 radio-collared mountain goats across their native range in Washington State. Using Geographical Information Systems (GIS), I explored relationships between use and availability of habitat. To overcome issues of autocorrelation, I compared actual mountain goat paths with available paths of matched identical spatial topology and used multi-scale path analysis to explore various ecologically informed relationships between landscape structure and the movements of mountain goats at the home range scale. I extracted used and available (randomized) paths at 4 scales of analysis using square extraction windows of 0.06, 4.4, 15.2, and 56.2 ha that were centered on each point along the path. Matched case logistic regression allowed me to determine the spatially and temporally explicit scales that were the strongest predictors of seasonal and year-round mountain goat habitat from a suite of predictor variables. I found that for year-round habitat, mountain goats chose both abiotic and biotic components of their landscape including; parkland, areas of high solar loading, terrain that is rugged, and terrain that allows escape from predators. This analysis represents one of the most extensive landscape-level habitat relationship studies conducted on mountain goats. Additionally, my methodological approach is applicable to other species-habitat association analyses

A research for formulation of localization method for outdoor moving vehicles

課題番号：1836011