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AbstractWe investigated the potential of using Synthetic Aperture Radar (SAR) imagery from three different frequencies: X-, C-, and L-band, to characterize coastal wetlands in the Great Lakes. Three sets of SAR data acquired over the Bay of Quinte, Ontario, Canada between 2016 and 2018 from Radarsat-2, 2016 from TerraSAR-X, and 2018 from ALOS-2 satellites were processed using small baseline subset (SBAS) Interferometric SAR (InSAR) techniques to provide maps of surface changes in marshes and swamps. Results showed that SAR backscatter and coherence were sensitive to sensor characteristics (frequency, polarization, incidence angle, acquisition interval), changes in water level, and phenology. InSAR time series observations were evaluated using measurements from water level loggers based on correlation and root mean square error (RMSE) from a linear regression model. Correlation between InSAR measurements and water level changes in the field varied from −1 to 1 depending on the site, type of wetland vegetation, and incidence angle. Although results from some sensor modes provided good correlation (0.77–1) at a few locations, the low fringe rate and large RMSE between 4 and 64 cm indicated that InSAR observations of water level changes in the dynamic wetland environment were generally underestimated.

AbstractWe compared traditional spatial filters and multi-temporal filters to remove speckle from synthetic aperture radar (SAR) data for mapping Phragmites australis. SAR constellations, with more rapid revisit capability, allow one to generate stacks of SAR data and to use multi-temporal filters for speckle reduction. GAMMA software offers multi-temporal filters for SAR processing, two of which we compared to the traditional Enhanced Lee and the Lee filters. We evaluated the filters using three criteria: (1) visual inspection, (2) signal level ratio, and (3) the equivalent number of looks (ENL). The results of this study show that multi-temporal filters were able to reduce speckle from areas of surface water and land, as well as to improve the detection of Phragmites patches due to preserving the resolution and texture which helped in the detection of the patch boundaries. The signal level ratio was approximately 1.0 with the GAMMA Multi-temporal filter and approximately 0.9 with the other filters. The enhanced Lee and the two multi-temporal filters produced an equal ENL of about 6. However, due to small patch sizes and backscatter similarity with cattails and other cover types Phragmites patches were difficult to separate from other types of flooded vegetation with C-HH intensity only.

ABSTRACTMost mapping methods for Arctic land cover are pixel-based techniques for low resolution data, and have limitations in mapping land cover heterogeneity over complex Arctic polygonal tundra terrain. In this study, we developed a hybrid object-based approach for Arctic coastal tundra mapping using very high resolution optical satellite imagery by combining results from semi-automatic water/land separation, texture analysis based on local binary pattern (LBP), and image classification via Random Forests (RF). The method was applied for coastal land cover mapping in a study site in Tuktoyaktuk, Northwest Territories, Canada using Pleiades satellite data. Results from pixel-based Maximum Likelihood Classifier (MLC), segment-based MLC, pixel-based RF, and segment-based RF were compared with the proposed method. The hybrid method outperformed other approaches and achieved an overall accuracy of 88% for 9 classes. In particular, it has successfully identified unique land cover types of Ice-Wedge Polygons, Wetland (inundated low-lying tundra and marsh with water ponds), with both producer's and user's accuracy over 91%. Results from this study indicate that the developed hybrid method is suitable to be applied for mapping Arctic coastal ecosystems, and confirms the feasibility of proper use of LBP at segment level for mapping complex environment.

AbstractArctic amplification has many impacts on sea-ice extent, thickness, and flux. It becomes critical to monitor sea-ice conditions at finer spatio-temporal resolution. We used a simple convolutional neural network (CNN) on the RADARSAT-2 dual-polarized ScanSAR wide archive available over Coronation Gulf, Canada, to assess which SAR parameter improves model performances to classify sea ice from water on a large volume of data covering 11 years of ice and surface water conditions. An overall accuracy of 90.1% was achieved on 989 scenes of 100% ice cover or ice-free conditions. An accuracy of 86.3% was achieved on the last year of data (134 scenes) which was kept out of the training process to test the model on an independent dataset. A better accuracy is obtained at lower incidence angles and the HH polarization provides the most information to classify ice from water. To achieve the best accuracy, the incidence angle and the noise equivalent sigma-nought had to be included as input to the model. A comparison done with the ASI passive microwave product shows similar errors in total sea ice concentration when using the Canadian Ice Service regional charts as reference. Nonetheless, errors from both datasets differ and the CNN outputs show greater potential to reduce masked areas, given the better spatial resolution, enabling data classification closer to land and identify features not captured by the ASI dataset.

Abstract. With increasing ship traffic and natural resource extraction in the Canadian Arctic, the chance of an oil spill occurring is steadily increasing. Oil deposited in the marine environment will impact the ocean, and as it is washed onto shore many species and their habitats. In 2009, Environment Canada initiated a project to produce baseline coastal information required for operational prioritization in the event of a marine oil spill in the Canadian Arctic. Earth observation data can potentially be used to improve on traditional mapping methods. RADARSAT-2 C-band data and geotagged videography were collected for selected sites in the Canadian Arctic. Object-based classification was compared to pixel-based classification using two of these pilot sites, Richards Island (Northwest Territories) and Ivvavik (Yukon Territory). The object-based classification achieved an overall accuracy of 74% at Richards Island and 63% at Ivvavik, whereas the pixel-based reached 73% at both sites. To test transferability, the classifiers developed at Richards Island were applied to Ivvavik. In this case, the object-based classification achieved an overall accuracy of 78% and the pixel-based reached 71%. Results showed that characterizing shorelines in the Arctic using Earth observation data for oil spill cleanup response activities is possible.Résumé. Avec l’accroissement du trafic maritime et de l’exploitation des ressources naturelles dans l’Arctique canadien les risques d’un déversement d’hydrocarbure sont en augmentation. Les dépôts d’hydrocarbures auront un impact sur l’environnement marin. En 2009, Environnement Canada a initié un projet qui visait à améliorer la connaissance des types de rivage afin de prioriser les opérations de nettoyage dans l’éventualité d’un déversement d’hydrocarbure. Des vidéos géo-référencés et des images RADARSAT-2 ont été acquis pour plusieurs sites de l’Arctique canadien. Une classification orientée-objet a été comparée à une classification pixel-à-pixel pour deux de ces sites, soit l’île Richards (Territoires du Nord-Ouest) et Ivvavik (Yukon). La classification orientée-objet a atteint une précision globale de 74% à l’île Richards et de 63% à Ivvavik. La classification pixel-à-pixel a atteint une précision globale de 73% à chacun des sites. Les méthodes de classification développées à l’île Richards ont été appliquées à Ivvavik afin d’évaluer la transférabilité. La classification orientée-objet a atteint une précision globale de 78% et la classification pixel-à-pixel de 71%. Dans l’ensemble, les résultats ont démontrés que les types de rivage de l’Arctique canadien peuvent être caractérisés à partir de données d’observation de la terre afin de supporter les opérations de nettoyage.

AbstractFreeman–Durden and Cloude–Pottier decompositions were applied to polarimetric RADARSAT-2 data to characterize the scattering behaviour of shore and near-shore land cover types for two study areas in the Beaufort Sea, Canada: Richards Island and Tuktoyaktuk Harbour. The impact of incidence angle was evaluated through comparison of Single Look Complex Fine Quad-Pol images acquired at three angles: shallow (45.3°–49.5°), medium (39.3°–41.6°), and steep (20.9°–24.2°), and the potential for accurate classification was assessed using three unsupervised polarimetric SAR classifiers: Wishart-entropy/alpha, Wishart-entropy/anisotropy/alpha, and Freeman-Wishart. Results indicate that overall, medium, and shallow incidence angle images provide more useful information than steep angle images and that these classifiers are capable of distinguishing some general land cover types, including substrates, wetlands, vegetated tundra, and water. Results from this analysis will inform future research related to classification and interpretation of RADARSAT-2 data for shoreline mapping.Résumé. Des décompositions Freeman–Durden et Cloude–Pottier ont été appliquées à des données polarimétriques RADARSAT-2 pour caractériser le comportement de diffusion des différents types de couverture terrestre des milieux littoraux et côtiers pour deux zones d'étude dans la mer de Beaufort, au Canada: l'île Richards et le port de Tuktoyaktuk. L'impact de l'angle d'incidence a été évalué par comparaison des images singulières complexes <> quad-pol fines acquises à trois angles; plus rasants (45.3°–49.5°), moyens (39.3°–41.6°) et abrupts (20.9°–24.2°), tandis que le potentiel pour une classification précise a été évalué à l'aide de trois classificateurs RSO polarimétriques non dirigés: Wishart-entropie/alpha, Wishart-entropie/anisotropie/alpha et Freeman–Wishart. Les résultats indiquent que, dans l'ensemble, les images avec des angles d'incidence moyens à rasants fournissent des informations plus utiles que les images aux angles abrupts, et que ces classificateurs sont capables de distinguer certains types généraux de couverture terrestre, y compris: les substrats, les zones humides, la toundra végétalisée et l'eau. Les résultats de cette analyse seront utiles dans les recherches futures liées à la classification et à l'interprétation des données RADARSAT-2 pour la cartographie des rivages.

AbstractContinued development of natural resources and a greater human presence in the Arctic places biologically and culturally sensitive shorelines at greater risk of environmental emergencies. Maps of vegetation, substrate, and water classes are needed in order to develop emergency response contingency plans. This study was completed as part of Environment Canada's Emergency Spatial Pre-SCAT for Arctic Coastal Ecosystems (e-SPACE) project, and focused on assessing the potential for improved mapping efficiency through semiautomated classification of the thematic classes defined for these purposes by Environment Canada–Environmental Emergencies Branch, using RADARSAT-2 and SPOT-4 alone and in combination. Repeatability and optimal RADARSAT-2 acquisition parameters were evaluated through comparison of multiple incidence angle images acquired over Tuktoyaktuk Harbour and West Point on Richards Island, Northwest Territories, Canada. Shallow incidence angles (45.3°–49.5°) were preferred over medium (39.3°–41.6°) and steep incidence angles (20.9°–24.2°), and when used alone as inputs, a number of land cover types could be discriminated, including sand, mixed sediment, herbs, shrubs, and wetlands. When used in combination with SPOT-4 data, an overall accuracy of 86.1% and 76.2% was achieved for Tuktoyaktuk Harbour and West Point, respectively, on Richards Island.

Double-crested cormorant (Phalacrocorax auritus) populations have increased greatly across North America. The interior North America subpopulation is the largest with many birds nesting on the Laurentian Great Lakes. Lake Erie supports a large number of breeding pairs that nests primarily on islands in the western basin of the lake. These islands also harbor many rare plant species constituting some of the last vestiges of Carolinian plant communities in Canada. Nesting cormorants can adversely affect the plant communities on the islands on which they nest. Annual ground censuses were conducted from 1979 to 2011 to assess temporal changes in the density of nesting cormorants on 3 islands in western Lake Erie. We used aerial photographs taken over a maximum 16-year span to assess changes in forest cover on these island ecosystems. We observed declines in forest cover on all 3 islands ranging from 47% to 85%. Trends among islands differed reflecting differences in cormorant colonization histories and the degree to which cormorants were managed, thereby influencing nest densities. Islands without cormorant management had cormorant nest densities ranging from approximately 300–500?nests/ha and forests declined continuously through time. On the island where cormorants were culled, nest densities were lower (approx. 200?nests/ha) and forest decline stabilized. © 2014 The Wildlife Society.

The generation of geospatial thematic information for managing and monitoring Canada's boreal ecosystem is essential for researchers, land managers, and policy makers. Canada's boreal region is a vast mosaic of forests, wetlands, rivers, and lakes, but anthropogenic disturbances have impacted these ecosystems resulting in habitat loss, fragmentation, and threats to biodiversity. Across Canada various geospatial datasets representing anthropogenic disturbance exist for timber harvesting, hydro-electric activity, settlement, and oil and gas activities; however, these products often vary in scale, attributes, time period, and mapping technique. Driven by the need for national data as part of the 2011 boreal caribou science assessment, a standardized methodology was developed and implemented to create a single geospatial dataset representing anthropogenic disturbances across a significant portion of Canada's boreal ecosystem. The boreal ecosystem anthropogenic disturbances data are a vector disturbance dataset of individual linear and polygonal disturbance types that were manually collected through the interpretation of 2008–2010 Landsat imagery at a 1:50000 viewing scale. Summary results identified a total polygonal anthropogenic disturbance footprint of approximately 24 million ha with forest cutblocks accounting for more than 60% of mapped polygonal disturbance. Linear disturbance features across the boreal total approximately 600000 km with roads and seismic exploration lines contributing to more than 80% of the mapped linear disturbances.

Winter habitat for mule deer (Odocoileus hemionus) is a critical concern throughout interior British Columbia, Canada. In winter, mule deer require a food source of twigs and woody browse and face significant winter snow cover. A range of studies have established that good winter range for mule deer reduces the impact of a negative energy balance by providing adequate food, good vegetative cover, and shallow snow. Generally, sites with old Douglas-fir and moderate to high canopy cover on warmer aspects and moderately steep slopes are preferred, resulting in a suite of structural stand conditions, which can be used to map mule deer winter range habitat within the interior Douglas-fir range. The increased availability of light detection and ranging (lidar) data to management agencies and the recent adoption of lidar technology by forestry agencies allow us to assess the capacity of this technology to map some variables important to winter mule deer habitat suitability, using criteria similar to those defined using conventional aerial photography. Results indicate that lidar-derived solar radiation regime, elevation, and overstorey cover are all useful attributes in decision-tree models relating lidar to conventionally derived descriptors of mule deer winter habitat. These lidar-derived models describe up to 75% of the variance in overall stand structure and confirm that this technology is a viable tool which can be used to assess habitat throughout this region.

Abstract: Double-crested cormorant (Phalacrocorax auritus) populations on the Great Lakes expanded greatly during the past 2 decades. On Lake Erie, the number of breeding cormorants increased from 174 birds (87 nests) in 1979 to 26,542 (13,271 nests) in 2000. In 2000, 81% of the breeding population was on 2 western-basin islands (East Sister and Middle Islands). The plant communities on these islands represent some of the last remnants of Carolinian vegetation in Canada. Our study is the first to quantitatively assess the relationship between the distribution of nesting cormorants and forest health. On East Sister Island, 2 measures of forest cover were obtained using infrared aerial photographs and ground-based measurements of leaf area index. These measures of forest cover were correlated (rs= 0.70, P< 0.001), which validated the use of remotely sensed data to assess forest cover. Cormorant nest density was negatively correlated with tree cover on both East Sister and Middle Islands. Temporal comparisons of Middle Island data indicated a reduction in tree cover from 1995 to 2001, and these reductions coincided with a large increase in the island's cormorant population. Although correlational in nature, our results suggest that cormorants may be detrimentally affecting island forests.

Since the mid-1970s, the city of St. Louis has encountered frequent litigation and court involvement surrounding the testing procedures used to evaluate candidates for promotion in the fire service. During this period, the city has expended a considerable amount of time, effort, and money to develop valid and defensible methods of testing. In light of this experience, we have decided to review the past history to see if any conclusions can be drawn that would be helpful to other organizations. This article contains a brief history of the city's fire service testing and litigation, and a description of the current testing model used by the city. It also addresses the use of various testing components and their impact on all candidates. Finally, some practical considerations are discussed.

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The article evaluates the Instant Radio created by Shahid Malik.