Abstract:Recent studies have shown that ice duration in lakes and rivers over the Northern Hemisphere has decreased over the 19th and 20th centuries in response to global warming. However, lake ice trends have not been well documented in Canada. Because of its size, considerable variability may exist in both freeze-up and break-up dates across the country. In this paper, results of the analysis of recent trends in freeze-up and break-up dates across Canada are presented. Trends toward earlier break-up dates are observed for most lakes during the time periods of analysis which encompass the 1990s. Freeze-up dates, on the other hand, show few significant trends and a low degree of temporal coherence when compared with break-up dates. These results are compared with trends in autumn and spring 0°C isotherm dates over the time period 1966-95. Similar spatial and temporal patterns are observed, with generally significant trends toward earlier springs/break-up dates over most of western Canada and little change in isotherm and freeze-up dates over the majority of the country in autumn. Strong correlations (r > 0Ð5) between 0°C isotherm dates and freeze-up/break-up dates at many locations across the country reveal the high synchrony of these variables. These results are also consistent with more recent observations of other cryospheric and atmospheric variables that indicate, in particular, a general trend toward earlier springs in the latter part of the 20th century. The results of this study provide further evidence of the robustness of lake ice as a proxy indicator of climate variability and change.

Abstract:A one-dimensional thermodynamic model for simulating lake-ice phenology is presented and evaluated. The model can be driven with observed daily or hourly atmospheric forcing of air temperature, relative humidity, wind speed, cloud amount and snowfall. In addition to computing the energy balance components, key model output includes the temperature profile at an arbitrary number of levels within the ice/snow (or the water temperature if there is no ice) and ice thickness (clear ice and snow-ice) on a daily basis, as well as freeze-up and break-up dates. The lake-ice model is used to simulate ice-growth processes on shallow lakes in arctic, sub-arctic, and high-boreal forest environments. Model output is compared with field and remote sensing observations gathered over several ice seasons. Simulated ice thickness, including snow-ice formation, compares favourably with field measurements. Ice-on and ice-off dates are also well simulated when compared with field and satellite observations, with a mean absolute difference of 2 days. Model simulations and observations illustrate the key role that snow cover plays on the seasonal evolution of ice thickness and the timing of spring break-up. It is also shown that lake morphometry, depth in particular, is a determinant of ice-off dates for shallow lakes at high latitudes.

Wildlife management is based on various measurements representative of the health of populations and their habitats. Some agencies are focusing on animal surveys to manage species such as white-tailed deer (Odocoileus virginianus). Current survey methods are faced with the challenge of reduced operating costs as well as estimating and correcting detection biases. Our pilot study (data collected on 6 Nov 2012 at Saint-David-de-Falardeau, QC, Canada) assessed the potential of a new approach detect and count deer based on visible and thermal infrared image processing at very-high spatial resolutions using an unmanned aerial system (UAS). Supervised and unsupervised pixel-based image classification approaches as well as object-based image analysis (OBIA) were assessed for different spatial resolutions and with different combinations of spectral bands. None of the pixel-based approaches were effective for detecting deer. The OBIA approach detected deer with a rate of up to 100% under the best conditions by using a combination of visible and thermal infrared imagery at a spatial resolution of 0.8 cm/pixel. Overall, this approach had an average detection rate of 0.5, which is comparable to conventional aerial surveys. Visual obstructions by coniferous canopy and the spectral confusion associated with certain elements (e.g., bare soil, rocks) are problems that remain unresolved. Using UASs with image processing for surveys of deer and other species of large mammals is promising, but currently limited by the flight range of unmanned aerial vehicles and the associated regulations. Ó 2016 The Wildlife Society.KEY WORDS aerial survey, drone, image processing, object-based image analysis, Odocoileus virginianus, remote sensing, thermal infrared and visible imagery, unmanned aerial vehicle (UAV), wildlife survey.

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