Building a Rockfall Database Using Remote Sensing: Techniques for Hazard Management in Canadian Rail Corridors

The section of CN railway between Vancouver and Kamloops runs along the base of many hazardous slopes, including the White Canyon, which is located just outside the town of Lytton, BC. The slope has a history of frequent rockfall activity, which presents a hazard to the railway below. Rockfall inventories can be used to understand the frequency-magnitude relationship of events on hazardous slopes, however it can be difficult to consistently and accurately identify rockfall source zones and volumes on large slopes with frequent activity, leaving many inventories incomplete. We have studied this slope as a part of the Canadian Railway Ground Hazard Research Program and have collected remote sensing data, including terrestrial laser scanning (TLS), photographs, and photogrammetry data since 2012, and used change detection to identify rockfalls on the slope. The objective of this thesis is to use a subset of this data to understand how rockfalls identified from TLS data could be used to understand the frequency-magnitude relationship of rockfalls on the slope. This includes incorporating both new and existing methods to develop a semi-automated workflow to extract rockfall events from the TLS data. We show that these methods can be used to identify events as small as 0.01 m3 and that the duration between scans can have an effect on the frequency-magnitude relationship of the rockfalls. We also show that by incorporating photogrammetry data into our analysis, we can create a 3D geological model of the slope and use this to classify rockfalls by lithology, to further understand the rockfall failure patterns. When relating the rockfall activity to triggering factors, we found that the amount of precipitation occurring over the winter has an effect on the overall rockfall frequency for the remainder of the year. These results can provide the railways with a more complete inventory of events compared to records created through track inspection, or rockfall monitoring systems that are installed on the slope. In addition, we can use the database to understand the spatial and temporal distribution of events. The results can also be used as an input to rockfall modelling programs.

Remote Sensing of Vegetation Change Across a Latitudinal Gradient in the Canadian Arctic

Global air surface temperatures and precipitation have increased over the last several decades resulting in a trend of greening across the Circumpolar Arctic. The spatial variability of warming and the inherent effects on plant communities has not proven to be uniform or homogeneous on global or local scales. We can apply remote sensing vegetation indices such as the Normalized Difference Vegetation Index (NDVI) to map and monitor vegetation change (e.g., phenology, greening, percent cover, and biomass) over time. It is important to document how Arctic vegetation is changing, as it will have large implications related to global carbon and surface energy budgets. The research reported here examined vegetation greening across different spatial and temporal scales at two disparate Arctic sites: Apex River Watershed (ARW), Baffin Island, and Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU. To characterize the vegetation in the ARW, high spatial resolution WorldView-2 data were processed to create a supervised land-cover classification and model percent vegetation cover (PVC) (a similar process had been completed in a previous study for the CBAWO). Meanwhile, NDVI data spanning the past 30 years were derived from intermediate resolution Landsat data at the two Arctic sites. The land-cover classifications at both sites were used to examine the Landsat NDVI time series by vegetation class. Climate variables (i.e., temperature, precipitation and growing season length (GSL) were examined to explore the potential relationships of NDVI to climate warming. PVC was successfully modeled using high resolution data in the ARW. PVC and plant communities appear to reside along a moisture and altitudinal gradient. The NDVI time series demonstrated an overall significant increase in greening at the CBAWO (High Arctic site), specifically in the dry and mesic vegetation type. However, similar overall greening was not observed for the ARW (Low Arctic site). The overall increase in NDVI at the CBAWO was attributed to a significant increase in July temperatures, precipitation and GSL.