Environmental Mapping through Novel Spatial Data Integration
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Spatial data are becoming ubiquitous in modern society. Improvements in geographic information systems (GIS) and the development of global positioning systems (GPS) have resulted in wide-spread public interest in geographical location. However at present, vast amounts of environmental spatial data are currently discarded. As an example, sea floor elevation data generated by fish or depth finders are collected but not stored. Spatial data collected ubiquitously through modern living are an untapped and highly valuable source of data. Read more
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This research emphasizes the development of techniques to integrate, refine, and analyze underutilized novel spatial data sources. For instance, a method for combining spatially detailed depth sounding data on sea floor elevation with currently available digital bathymetry maps will improve the spatial resolution of bathymetry mapping in some regions.
Spatial Surveillance of Zoonotic Disease in Sri Lanka
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SPAR lab is working with a team of researchers, including veterinarians from the Centre for Coastal Health, the University of Calgary, and the Sri Lankan Ministry of Estate Infrastructure and Livestock Development to design and implement a spatial surveillance system for the early detection of emerging diseases in farm animals in Sri Lanka. Using space-time cluster detection techniques, we are working to monitor zoonotic diseases, detect outbreaks and identify areas of emerging risk. To determine the efficacy of the spatial surveillance system, we are comparing our results and costs to standard detection approaches, and assessing sensitivity, specificity and timeliness by veterinary confirmation of reported cases.
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Linking Space-Time Trends in Alberta Grizzly Bear Habitat Use to Landscape Change
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SPAR lab research focuses on quantifying the temporal trends in the spatial distribution of grizzly bears in the foothills of the Rocky Mountains of Alberta, Canada. Grizzly bears and other wildlife utilizing habitat in this area are subject to the influences of many human activities, such as mining, oil and gas exploration, logging and recreation. Read more
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The Foothills Model Forest Grizzly Bear Research Program (FMFGBRP) has collected extensive data to examine these populations and to evaluate the response of grizzly bears to human activities and habitat conditions with the use of remotely sensed habitat maps and radio-telemetry data. Previous research has identified the home ranges of animals and used this information to model the habitat preferences of a species. Our work will examine the persistence or variability in the distribution of grizzly bear home ranges through time, in relation to landscape changes identified by the FMFGBRP.
Spatial-Temporal Analysis of Snow Water Equivalence
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SPAR lab is also examining the spatial-temporal characteristics of snow water equivalence (SWE) in Canada, and comparing these characteristics to known or hypothesized climate and ecological processes. The spatial and temporal distribution of terrestrial snow cover has implications for many ecological processes, such as local snowmelt release, local and global atmospheric circulation, as well as climate, and hydrological cycles. Read more
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The sensitivity of terrestrial snow cover and SWE to atmospheric conditions and overlying air temperatures also makes snow cover a useful indicator of climate change. Thus, the examination of the spatial distribution of terrestrial snow cover and SWE over time aids in understanding current and future trends in changing climate conditions. Our research attempts to link the spatial-temporal interactions of SWE to underlying land-cover characteristics, as well as generate relevant temporal characteristics from SWE time-series’ in order to explain the dominant SWE regimes across Canada. In addition to its broad implications for climate change research in general, this research also has implications for use in a national biodiversity monitory system for Canada, driven by remote sensing of multiple indicators of biodiversity. These indicators include productivity, disturbance, topography, and land-cover, of which terrestrial snow cover and SWE play an integral part.
Evaluating the Spatial Pattern of Forests Impacted by the Mountain Pine Beetle
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The spatial pattern of forested landscapes affects a variety of ecological processes such as hydrologic regimes, the carbon budget, soil processes and wildlife habitat use. The current mountain pine beetle epidemic in British Columbia has caused widespread mortality in the provinces pine forests. Here at SPAR we are evaluating how the spatial pattern in these forested environments is being changed as a result of forest management practices. Read more
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Resulting salvage harvesting activities are causing large openings in forest cover not seen in previous management scenarios. It is important to quantify how the spatial pattern of forests is changing to provide information for wildlife and forestry managers. Currently, we are using landscape pattern indices and local indicators for categorical data (LICD) as well as a novel method for delineating regions of characteristic spatial pattern.
Completed Projects
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Western Canada is experiencing the largest mountain pine beetle epidemic on record. The spatial extent of the epidemic requires management of large areas. However, the majority of mountain pine beetle research is conducted at the stand level. As well, there is a dearth of spatially explicit research on the mountain pine beetle, which limits our ability to manage and predict mountain pine beetle activity. Therefore, we are investigating spatial and spatial-temporal questions relating to large area mountain pine beetle infestations. Several specific topics have or are being investigated.
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- Quantifying the quality of mountain pine beetle monitoring data
- Incorporating data uncertainty in the representation of mountain pine beetle data
- Identifying infestation hot spots
- Characterizing temporal patterns in infestation hot spots
- Linking multi-temporal spatial patterns in infestation to mountain pine beetle dispersal
- Detecting and exploring spatial trends in temporal changes in infestation rates
- Quantifying the efficacy of direction control treatment
- Investigating the scaling up of stand scale models for landscape-level management
Graduate Student Research Projects
For more information on ongoing research, see the following links:
Spatial-temporal analysis of moving polygons - Colin Robertson - home page
The effect of directionality and anisotropy on a local measure
of spatial autocorrelation- the Gi* - Ian Mackenzie -
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The spatial-temporal distribution of gray whales is being studied in conjunction with the Whale Research Lab at the University of Victoria. We have developed a new method, the space-time string (STS), to investigate spatial trends in temporal patterns associated with prey-predator relationships. We are also interesting in spatial and temporal change in the distribution of foraging whales
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The spatially extensive nature of the current mountain pine beetle epidemic requires planning for the post-infestation environment. The initial step to managing the post-beetle landscape is to understand the structure and composition of the remaining forests. The goal of this project is to integrate several existing data sets to characterize and explore the nature and spatial variability in forest composition and structure for a sub-area of the post-beetle landscape. Based on knowledge gained, we will map the post mountain pine beetle forest structure in British Columbia.
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