Search

We observed Bald Eagles (Haliaeetus leucocephalus) foraging at the landfill in Vancouver, British Columbia, Canada, 1994-1996 and 2001-2002, to determine (1) diet and time budgets of eagles visiting the landfill; {2) whether food taken from the landfill provided a significant energy source for local eagle populations; and (3) the effects of eagle density and weather on eagle behavior. Eagles fed primarily on human refuse (95%, n = 628), but food items taken from the landfill accounted for only 10 ± 3% of their daily energy needs. Subadults foraged at the landfill more often than adults, and most "refuse specialists" appeared to be subadults. Eagle time budgets consisted of mostly resting (91%), the remainder largely spent drinking (2.6%), scavenging (2.3%), and pirating (1.8%). Resting increased with wind speed, and foraging efficiency declined with precipitation, consistent with the hypothesis that the landfill is primarily a location for resting during inclement weather. Foraging efficiency decreased when number of eagles and piracies increased, and percent of eagles foraging decreased with increased numbers of eagles. The home ranges of only 2 of 11 radio-tagged eagles, both subadults, consisted largely (>20%) of the landfill; home-hinge size and percent of the home range that included the landfill were negatively correlated, suggesting that most eagles visited the landfill occasionally while a few spent most of their time there. We concluded that (1) the Vancouver landfill was not a major energy source for eagles, in part because their foraging is inefficient due to the large number of potential pirates; (2) most eagles apparently used the landfill primarily as a site for resting during inclement weather (the landfill is protected from the wind, is slightly warmer than surrounding areas due to decomposing refuse and the surrounding conifer trees, and is relatively fee of human activity); and (3) a small population of largely subadult refuse specialists appeared to gain much or all of their energy from the landfill. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sixty-four adult captive American kestrels (Falco sparverius) were exposed to a dietary source of halogenated dimethyl bipyrroles (HDBPs), a group of organohalogens proposed to have a natural origin. Adult kestrels were assigned to four treatment groups that were administered a different dose of HDBPs [high, 140 µg/(bird ⋅ d); medium, 4.3 µg/(bird ⋅ d); low, 0.13 µg/ (bird ⋅ d); control, 0 µg/(bird ⋅ d)]. Exposure began prior to pairing and continued during breeding until approximately 28 d after hatching of the final eggs. Juvenile and adult kestrels accumulated HDBPs in carcass tissue, plasma, and liver. In addition, HDBPs were transferred to yolks during egg development. Low dietary absorption efficiencies (0.011-0.029) indicated that HDBPs were effectively cleared from kestrels either from increased elimination, metabolism, or a combination of the two processes. Reproductive and morphological endpoints examined for HDBP-induced changes included egg volume, egg mass, eggshell thickness, egg fertility, clutch size, liver and whole body mass, antebrachium, central retrice, tarsus, ninth primary, and wing cord lengths. Few statistically significant dose-dependent effects were observed. Hepatic tissue samples from selected individuals underwent histomorphological evaluation. Although the hepatic function was not evaluated specifically in these birds, no clinical signs suggestive of liver disease or gross hepatic anomalies were observed. Only minor histomorphological changes were detected in the hepatic tissue. The lack of serious effects suggests that HDBPs are not an acute reproductive threat to avian populations. [ABSTRACT FROM AUTHOR]

Most 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. [ABSTRACT FROM AUTHOR]

Arctic 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 FROM AUTHOR]

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 1:50 000 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 600 000 km with roads and seismic exploration lines contributing to more than 80% of the mapped linear disturbances. [ABSTRACT FROM AUTHOR]

Mapping and monitoring of Arctic vegetation biochemical and biophysical properties is gaining importance as global climate change is disproportionately affecting this region. Previous studies using remote sensing to model Arctic vegetation biochemical and biophysical properties have generally involved empirical modelling with nadir looking broadband sensors and have typically been conducted at the field scale in one study area. Satellite hyperspectral remote sensing has not been previously investigated for retrieving leaf and canopy biochemical and biophysical properties of Arctic vegetation across multiple sites using either empirical or physically-based modelling approaches. Furthermore, multi-angle hyperspectral sensors (CHRIS/PROBA), which can provide insight into vegetation reflectance anisotropy and potentially improve vegetation parameter estimation, have also not been investigated for this purpose. In this study, three modelling approaches previously investigated with field spectroscopy data (Kennedy et al., 2020) were used with CHRIS Mode-1 imagery to predict leaf chlorophyll content, plant area index and canopy chlorophyll content across a bioclimatic gradient in the Western Canadian Arctic. Modelling approaches included: parametric linear regression based on vegetation indices (VI), non-parametric machine learning Gaussian processes regression (GPR) and inversion of the PROSAIL radiative transfer model using a look-up table approach (LUT). CHRIS imagery was acquired with −55°, −36°, 0°, +36°, +55° view zenith angles (VZA) between 2011 and 2014 over three field sites extending from the Richardson Mountains in central Yukon, Canada to the north end of Banks Island, Northwest Territories, Canada. Field measurements were acquired within several weeks of satellite acquisitions. GPR had the best model fit (mean cross-validated (cv) coefficient of determination, r2cv = 0.61 across all vegetation variables, sites and VZAs vs. 0.59 for the simple ratio, SR) and predictive performance (normalized root mean square error, NRMSEcv = 0.13 vs. 0.14 for SR). The revised optimized soil adjusted VI (ROSAVI) performance was slightly poorer (r2cv = 0.51; NRMSEcv = 0.15). The physically-based PROSAIL model performed poorer than all empirical models (r2 = 0.50; NRMSE = 0.18). This ranking of model performance is similar to that found in the previous field spectroscopy study, where empirical model fits and predictive performance were only slightly worse. With respect to view angle performance, NRMSE varied only slightly, indicating no distinct advantage for any one VZA. Overall, strong potential has been demonstrated for empirical modelling of Arctic vegetation chlorophyll and plant area index using hyperspectral data combined with band selection/optimization procedures in the Arctic. Recently launched and future hyperspectral satellites, including next generation airborne sensors, will likely provide improvements to the model performance reported here. [ABSTRACT FROM AUTHOR]

We 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. [ABSTRACT FROM AUTHOR]

To evaluate the potential of multi-angle hyperspectral sensors for monitoring vegetation variables in Arctic environments, empirical and physical modelling using field data was implemented for the retrieval of leaf and canopy chlorophyll content (LCC, CCC) and plant area index (PAI) measured at four sites situated across a bioclimatic gradient in the Western Canadian Arctic. Field reflectance data were acquired with an ASD FieldSpec (305–1075 nm) and used to simulate CHRIS Mode1 spectra (411–997 nm). Multi-angle measurements were taken corresponding to CHRIS view zenith angles (VZA) (−55°, −36°, 0°, +36°, +55°). Empirical modelling compared parametric regression based on vegetation indices (VIs) to non-parametric Gaussian Processes Regression (GPR). In physical modelling, PROSAIL was inverted using numerical optimization and look-up table (LUT) approaches. Cross-validation of the empirical models ranked GPR as best, followed by simple ratio (SR) with optimally selected NIR and red wavelengths, and then ROSAVI using its published wavelengths (mean r2cv = 0.62, 0.58, and 0.54, respectively across all sites, variables, and VZAs). However, the best predictive performance was achieved by SR followed by GPR and ROSAVI (NRMSEcv = 0.12, 0.16, 0.16, respectively). PROSAIL simulated the multi-angle top-of-canopy reflectance well with numerical optimization (r2 = ~0.99, RMSE = 0.004 ± 0.002), but best performing LUT models of LCC, CCC and PAI were poorer than the empirical approaches (mean r2 = 0.48, mean NRMSE = 0.22). PROSAIL performed best at the high Arctic sparsely vegetated site (r2 = 0.57–0.86 for all parameters). Overall, the best performing VZA was −55° for empirical modelling and 0° and ±55° for physical modelling; however, these were not significantly better than the other VZAs. Overall, this study demonstrates that, for Arctic vegetation, nadir narrowband reflectance data used to derive simple empirical VIs with optimally selected bands is a more efficient approach for modelling chlorophyll and PAI than more complex empirical and physical approaches. [ABSTRACT FROM AUTHOR]

We 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. [ABSTRACT FROM AUTHOR]

Landsat is a fundamental data source for understanding historical change and its effect on environmental processes. In this research we test shallow and deep convolution neural networks (CNNs) for Landsat image super-resolution enhancement, trained using Sentinel-2, in three study sites representing boreal forest, tundra, and cropland/woodland environments. The analysis sought to assess baseline performance and determine the capacity for spatial and temporal extension of the trained CNNs. This is not a data fusion approach and a high-resolution image is only needed to train the CNN. Results show improvement with the deeper network generally achieving better results. For spatial and temporal extension, the deep CNN performed the same or better than the shallow CNN, but at greater computational cost. Results for temporal extension were influenced by change potentiality reducing the performance difference between the shallow and deep CNN. Visual examination revealed sharper images regarding land cover boundaries, linear features, and within-cover textures. The results suggest that spatial enhancement of the Landsat archive is feasible, with optimal performance where CNNs can be trained and applied within the same spatial domain. Future research will assess the enhancement on time series and associated land cover applications. [ABSTRACT FROM AUTHOR]

In this study, a new method is proposed for semi-automated surface water detection using synthetic aperture radar data via a combination of radiometric thresholding and image segmentation based on the simple linear iterative clustering superpixel algorithm. Consistent intensity thresholds are selected by assessing the statistical distribution of backscatter values applied to the mean of each superpixel. Higher-order texture measures, such as variance, are used to improve accuracy by removing false positives via an additional thresholding process used to identify the boundaries of water bodies. Results applied to quad-polarized RADARSAT-2 data show that the threshold value for the variance texture measure can be approximated using a constant value for different scenes, and thus it can be used in a fully automated cleanup procedure. Compared to similar approaches, errors of omission and commission are improved with the proposed method. For example, we observed that a threshold-only approach consistently tends to underestimate the extent of water bodies compared to combined thresholding and segmentation, mainly due to the poor performance of the former at the edges of water bodies. The proposed method can be used for monitoring changes in surface water extent within wetlands or other areas, and while presented for use with radar data, it can also be used to detect surface water in optical images. [ABSTRACT FROM AUTHOR]

Detailed information on the land cover types present and the horizontal position of the land-water interface is needed for sensitive coastal ecosystems throughout the Arctic, both to establish baselines against which the impacts of climate change can be assessed and to inform response operations in the event of environmental emergencies such as oil spills. Previous work has demonstrated potential for accurate classification via fusion of optical and SAR data, though what contribution either makes to model accuracy is not well established, nor is it clear what shorelines can be classified using optical or SAR data alone. In this research, we evaluate the relative value of quad pol RADARSAT-2 and Landsat 5 data for shoreline mapping by individually excluding both datasets from Random Forest models used to classify images acquired over Nunavut, Canada. In anticipation of the RADARSAT Constellation Mission (RCM), we also simulate and evaluate dual and compact polarimetric imagery for shoreline mapping. Results show that SAR data is needed for accurate discrimination of substrates as user's and producer's accuracies were 5-24% higher for models constructed with quad pol RADARSAT-2 and DEM data than models constructed with Landsat 5 and DEM data. Models based on simulated RCM and DEM data achieved significantly lower overall accuracies (71-77%) than models based on quad pol RADARSAT-2 and DEM data (80%), with Wetland and Tundra being most adversely affected. When classified together with Landsat 5 and DEM data, however, model accuracy was less affected by the SAR data type, with multiple polarizations and modes achieving independent overall accuracies within a range acceptable for operational mapping, at 89-91%. RCM is expected to contribute positively to ongoing efforts to monitor change and improve emergency preparedness throughout the Arctic. [ABSTRACT FROM AUTHOR]

For this research, the Random Forest (RF) classifier was used to evaluate the potential of simulated RADARSAT Constellation Mission (RCM) data for mapping landcover within peatlands. Alfred Bog, a large peatland complex in Southern Ontario, was used as a test case. The goal of this research was to prepare for the launch of the upcoming RCM by evaluating three simulated RCM polarizations for mapping landcover within peatlands. We examined (1) if a lower RCM noise equivalent sigma zero (NESZ) affects classification accuracy, (2) which variables are most important for classification, and (3) whether classification accuracy is affected by the use of simulated RCM data in place of the fully polarimetric RADARSAT-2. Results showed that the two RCM NESZs (-25 dB and -19 dB) and three polarizations (compact polarimetry, HH+HV, and VV+VH) that were evaluated were all able to achieve acceptable classification accuracies when combined with optical data and a digital elevation model (DEM). Optical variables were consistently ranked to be the most important for mapping landcover within peatlands, but the inclusion of SAR variables did increase overall accuracy, indicating that a multi-sensor approach is preferred. There was no significant difference between the RF classifications which included RADARSAT-2 and simulated RCM data. Both medium- and high-resolution compact polarimetry and dual polarimetric RCM data appear to be suitable for mapping landcover within peatlands when combined with optical data and a DEM. [ABSTRACT FROM AUTHOR]

The Random Forest algorithm was used to classify 86 Wide Fine Quadrature Polarized RADARSAT-2 scenes, five Landsat 5 scenes, and a Digital Elevation Model covering an area approximately 81,000 km² in size, and representing the entirety of Dease Strait, Coronation Gulf and Bathurst Inlet, Nunavut. The focus of this research was to assess the potential to operationalize shoreline sensitivity mapping to inform oil spill response and contingency planning. The impact of varying the training sample size and reducing model data load were evaluated. Results showed that acceptable accuracies could be achieved with relatively few training samples, but that higher accuracies and greater probabilities of correct class assignment were observed with larger sample sizes. Additionally, the number of inputs to the model could be greatly reduced without impacting overall performance. Optimized models reached independent accuracies of 91% for seven land cover types, and classification probabilities between 0.77 and 0.98 (values for latter represent per-class averages generated from independent validation sites). Mixed results were observed when assessing the potential for remote predictive mapping by simulating transferability of the model to scenes without training data. [ABSTRACT FROM AUTHOR]

Wynja, V.; Demers, A.-M.; Laforest, S.; Lacelle, M.; Pasher, J.; Duffe, J.; Chaudhary, B.; Wang, H., and Giles, T., 2015. Mapping coastal information across Canada's northern regions based on low-altitude helicopter videography in support of environmental emergency preparedness efforts. In the face of increasing economic opportunities in Canada's northern regions, the need to improve our state of preparedness for oil spill-related emergencies is critical. While significant efforts have been put toward documenting baseline coastal information across Canada's southern regions, there is a large information gap regarding Arctic shorelines. Baseline coastal information, such as shoreline form, substrate, and vegetation type, is required for prioritizing operations, coordinating onsite spill response activities ( i.e. Shoreline Cleanup Assessment Technique [SCAT]), and providing information for wildlife and ecosystem management. Georeferenced high-definition videography was collected during the summers of 2010 to 2012 along coastlines within six study sites across the Canadian Arctic. Detailed information describing the upper intertidal, supratidal, and backshore zones was extracted from the video and entered into a geospatial database using a data collection form. This information was used to delimit and map alongshore segments in the upper intertidal zone. Almost 15,000 km of northern shorelines were mapped, including 25 shoreline types based on the upper intertidal zone. This information will feed into a larger ongoing project focused on Arctic coastal ecosystems and oil spill response planning should the need arise. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques. [ABSTRACT FROM AUTHOR]

Freeman-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. [ABSTRACT FROM AUTHOR]

Effective conservation planning often requires difficult decisions when at-risk species inhabit economically valuable landscapes or if the needs of multiple threatened species do not align. In the agriculture-dominated landscape of eastern Ontario and southwestern Quebec, Canada, conflicting habitat requirements exist between threatened grassland birds benefiting from certain agriculture practices, and those of a diverse woodland bird community dependent on forest recovery. Using multi-scale species distribution models with data from the North American Breeding Bird Survey (BBS), we assessed habitat suitability for 8 threatened grassland and forest specialists within this region. We also identified landscapes that jointly maximize occurrence of the 8 focal species and diversity of the full avian community. Influential habitat associations differed among species at the territory (200 m radius) and landscape level (1 km), highlighting the importance of considering multiple spatial scales. Species diversity was maximized when forest or grassland/pasture cover approached 40–50%, indicating a positive response to land cover heterogeneity. We identified species diversity hotspots near Lake Huron, as well as along the shore and southeast of the St. Lawrence River. These areas represent mosaic landscapes, balancing forest patches, wetland, grassland/pasture, and row crops such as corn, soybean, and cereals. Despite drastic landscape changes associated with agroecosystems, we demonstrate that targeted habitat protection and enhancement that prioritizes land cover diversity can maximize protection of bird communities with directly contrasting needs. We highlight multiple pathways to achieve this balance, including forest retention or separating row crops with hedgerows and wooded fence-lines, improving flexibility in conservation approaches. • Heterogeneous agroecosystems support both grassland and forest bird communities. • At-risk species and high avian diversity co-occur in forest, pasture, & crop mosaics. • ~48% of suitable habitat for 8 focal declining species is unprotected. • Conserving priority habitat requires considering multiple spatial scales. • Multiple management strategies foster similarly diverse habitats (e.g., hedgerows). [ABSTRACT FROM AUTHOR]

Ecosystems are valuable as well as aesthetic. The natural functions of ecosystems can have profound effects on the economy, and human and wildlife health. The aggregate value of these 'ecosystem services' may far exceed the economic value derived from resource extraction or industrial development, especially when considering the costs of restoring ecosystems. There is increasing interest, therefore, in monitoring and protecting ecosystems, and accounting for the biodiversity and services they provide. In 2010, Canada undertook a review of ecosystem status and trends that identified the regions and ecosystems where management is most urgently needed. The authors concluded that more large-scale, long-term, standardized, and spatially complete information is needed for effective monitoring and management. Satellite-based earth observation (EO) tools were seen as a means of addressing this information need. In a separate exercise, a list of priority questions for conservation policy and management at a national level was produced: the resolution of three-quarters of those questions appears to depend on EO tools to a significant or critical extent. Canada has a long and successful history in all aspects of earth observation, placing it amongst the leaders in the international remote sensing community. Whereas the need for measuring ecosystem services to humans and wildlife is increasingly important, the challenges for doing so are increasingly significant and the technology required is increasingly complex. Overcoming these challenges is necessary to address emerging conservation priorities including measurement of ecosystem attributes to support habitat conservation for Species at Risk, measuring functional capacity of ecosystems to mitigate effects of climate change, monitoring and mitigating effects of resource extraction, and supporting industrial development in Canada's north. Addressing emerging priorities requires dialogue among ecologists and decision makers, coordinated at regional and national scales, and requires drawing on the best EO technologies and infrastructure available. This review highlights the urgency of a coordinated approach for innovative applications of EO tools toward conservation and discusses some of the key elements that might be included and opportunities and challenges that might be encountered, by such an approach. [ABSTRACT FROM AUTHOR]

Continued 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. [ABSTRACT FROM AUTHOR]

ABSTRACT 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. [ABSTRACT FROM AUTHOR]

Arctic amplification is accelerating changes in sea ice regimes in the Canadian Arctic with later freeze-up and earlier melt events, adversely affecting Arctic wildlife and communities that depend on the stability of sea ice conditions. To monitor both the rate and impact of such change, there is a need to accurately measure sea ice deformation, an important component for understanding ice motion and polar climate. The objective of this study is to determine the spatial-temporal pattern of deformation over landfast ice in the Arctic using time series SAR imagery. We present Interferometric Synthetic Aperture Radar (InSAR) monitoring of Arctic landfast sea ice deformation using C-band Radarsat-2, Sentinel-1 and L-band ALOS-2 in this paper. The small baseline subset (SBAS) approach was explored to process time series observations for retrieval of temporal deformation changes along a line-of-sight direction (LOS) over the winter. It was found that temporal and spatial patterns of deformation observed from different sensors were generally consistent. Horizontal and vertical deformations were also retrieved by a multi-dimensional SBAS technique using both ascending and descending Sentinel-1 observations. Results showed a horizontal deformation in the range of −95–85 cm, and vertical deformation in the range of −41–63 cm in Cambridge Bay, Nunavut, Canada during February-April 2019. High coherence over ice from C-band was maintained over a shorter time interval of acquisitions than L-band due to temporal decorrelation. [ABSTRACT FROM AUTHOR]

Concentrations of polychlorinated biphenyls (PCBs), hydroxylated metabolites of PCBs (HO-PCBs) and octachlorostyrene (4-HO-HpCS), and pentachlorophenol (PCP) were determined in umbilical cord plasma samples from three different regions of Québec. The regions studied included two coastal areas where exposure to PCBs is high because of marine-food—based diets—Nunavik (Inuit people) and the Lower North Shore of the Gulf of St. Lawrence (subsistence fishermen)—and a southern Québec urban center where PCB exposure is at background levels (Québec City). The main chlorinated phenolic compound in all regions was PCP. Concentrations of PCP were not significantly different among regions (geometric mean concentration 1,670 pg/g, range 628–7,680 pg/g wet weight in plasma). The ratio of PCP to polychlorinated biphenyl congener number 153 (CB153) concentration ranged from 0.72 to 42.3. Sum HO-PCB (∑HO-PCBs) concentrations were different among regions, with geometric mean concentrations of 553 (range 238–1,750), 286 (103–788), and 234 (147–464) pg/g wet weight plasma for the Lower North Shore, Nunavik, and the southern Québec groups, respectively. Lower North Shore samples also had the highest geometric mean concentration of sum PCBs (sum of 49 congeners; ∑PCBs), 2,710 (525–7,720) pg/g wet weight plasma. ∑PCB concentrations for Nunavik samples and southern samples were 1,510 (309–6,230) and 843 (290–1,650) pg/g wet weight plasma. Concentrations (log transformed) of ∑HO-PCBs and ∑PCBs were significantly correlated (r = 0.62, p < 0.001), as were concentrations of all major individual HO-PCB congeners and individual PCB congeners. In Nunavik and Lower North Shore samples, free thyroxine (T[sub 4]) concentrations (log transformed) were negatively correlated with the sum of quantitated chlorinated phenolic compounds (sum PCP and ∑HO-PCBs; r = −0.47, p = 0.01, n... [ABSTRACT FROM AUTHOR]

The prairie region of Canada is a dynamically changing landscape in relation to past and present anthropogenic activities and recent climate change. Improving our understanding of the rate, timing, and distribution of landscape change is needed to determine the impact on wildlife populations and biodiversity, ultimately leading to better-informed management regarding requirements for habitat amount and its connectedness. In this research, we assessed the viability of an approach to detect from–to class changes designed to be scalable to the prairie region with the capacity for local refinement. It employed a deep-learning convolutional neural network to model general land covers and examined class memberships to identify land-cover conversions. For this implementation, eight land-cover categories were derived from the Agriculture and Agri-Food Canada Annual Space-Based Crop Inventory. Change was assessed in three study areas that contained different mixes of grassland, pasture, and forest cover. Results showed that the deep-learning method produced the highest accuracy across all classes relative to an implementation of random forest that included some first-order texture measures. Overall accuracy was 4% greater with the deep-learning classifier and class accuracies were more balanced. Evaluation of change accuracy suggested good performance for many conversions such as grassland to crop, forest to crop, water to dryland covers, and most bare/developed-related changes. Changes involving pasture with grassland or cropland were more difficult to detect due to spectral confusion among classes. Similarly, conversion to forests in some cases was poorly detected due to gradual and subtle change characteristics combined with confusion between forest, shrub, and croplands. The proposed framework involved several processing steps that can be explored to enhance the thematic content and accuracy for large regional implementation. Evaluation for understanding connectivity in natural land covers and related declines in species at risk is planned for future research. [ABSTRACT FROM AUTHOR]

The purpose of this research was to develop a state of science synthesis of remote sensing technologies that could be used to track changes in Great Lakes coastal vegetation for the Great Lakes-St. Lawrence River Adaptive Management (GLAM) Committee. The mapping requirements included a minimum mapping unit (MMU) of either 2 × 2 m or 4 × 4 m, a digital elevation model (DEM) accuracy in x and y of 2 m, a "z" value or vertical accuracy of 1–5 cm, and an accuracy of 90% for the classes of interest. To determine the appropriate remote sensing sensors, we conducted an extensive literature review. The required high degree of accuracy resulted in the elimination of many of the remote sensing sensors used in other wetland mapping applications including synthetic aperture radar (SAR) and optical imagery with a resolution >1 m. Our research showed that remote sensing sensors that could at least partially detect the different types of wetland vegetation in this study were the following types: (1) advanced airborne "coastal" Airborne Light Detection and Ranging (LiDAR) with either a multispectral or a hyperspectral sensor, (2) colour-infrared aerial photography (airplane) with (optimum) 8 cm resolution, (3) colour-infrared unmanned aerial vehicle (UAV) photography with vertical accuracy determination rated at 10 cm, (4) colour-infrared UAV photography with high vertical accuracy determination rated at 3–5 cm, (5) airborne hyperspectral imagery, and (6) very high-resolution optical satellite data with better than 1 m resolution. [ABSTRACT FROM AUTHOR]

The restoration of peatlands is critical to help reduce the effects of climate change and further prevent the loss of habitat for many species of flora and fauna. The objective of this research was to evaluate RADARSAT-2 satellite imagery and high-resolution Unmanned Aerial Systems (UASs) to determine if they could be used as surrogates for monitoring the success of peatland restoration. Areas of peatland that were being actively harvested, had been restored from past years (1994–2003), and natural shrub bog in Lac St. Jean, Quebec were used as a test case. We compared the Freeman–Durden and Touzi decompositions by applying the Bhattacharyya Distance (BD) statistic to see if the spectral signatures of restored peatland could be separated from harvested peat and natural shrub bog. We flew Unmanned Aerial Surveys (UASs) over the study site to identify Sphagnum and Polytrichum strictum, two indicator species of early peatland restoration success. Results showed that the Touzi decomposition was better able to separate the spectral signatures of harvested, restored, and natural shrub bog (BD values closer to 9). Symmetric scattering type αs1, Helicity |τ1,2,3|, a steep incidence angle, and peak growing season appear to be important for separating the spectral signatures. We had moderate success in detecting Sphagnum and Polytrichum strictum visually by using texture and pattern but were unable to use colour due to differences in sun angle and clouds during the UAS flights. Results suggest that RADARSAT-2 data using the Touzi decomposition and UAS imagery show potential for monitoring peatland restoration success over time. [ABSTRACT FROM AUTHOR]

There is limited research focusing on Interferometric Synthetic Aperture Radar (InSAR) applications in the Great Lakes coastal wetlands with large water level fluctuations. In this study, we investigated the potential of using C-band SAR data to characterize marsh wetland and monitor water level changes along the coast of the Great Lakes. InSAR analysis was conducted using Radarsat-2 and Sentinel-1 data collected at Long Point, Ontario, Canada over the period of 2016–2018. Observations indicated that both backscattering coefficients and coherence from tall plants (e.g. cattail/Phragmites), short plants (e.g. grass), and water varied with different sensor modes (incidence angles and polarizations) in response to changes in phenology, disturbance, and water level. InSAR phase changes were closely related to fluctuations in water level and flow direction. We evaluated InSAR time series observations using measurements from water level loggers based on correlation and root mean square error (RMSE). It was found that correlation between InSAR measurements and water level changes in the field varied depending on the site, type of wetland vegetation, incidence angle and polarization. Although results from some sensor modes provided good correlation at a few locations, the low fringe rate and RMSE between 9 and 28 cm indicated that InSAR observations of water level changes were generally underestimated. • Utility of C-band InSAR is confirmed for monitoring water level in the Great Lakes. • SAR images are sensitive to marsh vegetation phenology and manmade changes. • Correlation between InSAR and field water level measurements varies in marsh. • High coherence is consistent in cattail and Phragmites dominated marsh areas. • C-band InSAR interferogram is saturated in high water level changes. [ABSTRACT FROM AUTHOR]

Terricolous macrolichens are an important food source for caribou (Rangifer tarandus) and can greatly influence their movement, distribution and demography over time. Mapping the spatial distribution and cover of macrolichens with remote sensing can serve as an important approach for assessing the impact of disturbances (e.g. fire, grazing, trampling) on lichen cover at the landscape scale and for monitoring post-disturbance rates of recovery. Previous remote sensing-based efforts to retrieve the distribution and abundance of lichen have been restricted to particular regions and thus are not indicative of the potential for large extent mapping and monitoring. In this study, we assessed the effectiveness of machine learning methods for retrieving lichen cover and change across different regions in northern Canada and Alaska using Landsat-5 images, topographic and climate data. Global and regional-scale models were evaluated to assess whether regionally specific analyses would improve performance. Of the models tested, the deep neural network was the most accurate for predicting lichen cover (model efficiency (ME) = 0.58, mean absolute error (MAE) < 7%). For the regional analysis, the performance was the best in north-central Canada (ME = 0.56, MAE = 8%) and the worst in north-eastern Canada (ME = 0.22, MAE < 4%) due to lower lichen cover, more exposed ground, and reduced sample quality and distribution. Analysis of trend-based change detection from 1984 to 2011 in the three regional test areas showed the expected directional response with declining lichen cover in north-western Canada in response to climate-induced shrub expansion, slow recovery to wildfire in north-central Canada, and declining lichen cover in north-eastern Canada related to caribou foraging/trampling and shrub expansion. • Lichen cover retrieval assessed for northern Canada and Alaska using Landsat • Globally trained neural network preformed as well as regional models. • Lichen time series was consistent with known regional drivers of change. [ABSTRACT FROM AUTHOR]

Methods for effective wetland monitoring are needed to understand how ecosystem services may be altered from past and present anthropogenic activities and recent climate change. The large extent of wetlands in many regions suggests remote sensing as an effective means for monitoring. Remote sensing approaches have shown good performance in local extent studies, but larger regional efforts have generally produced low accuracies for detailed classes. In this research we evaluate the potential of deep-learning Convolution Neural Networks (CNNs) for wetland classification using Landsat data to bog, fen, marsh, swamp, and water classes defined by the Canada Wetland Classification System (CWCS). The study area is the northern part of the forested region of Alberta where we had access to two reference data sources. We evaluated ResNet CNNs and developed a Multi-Size/Scale ResNet Ensemble (MSRE) approach that exhibited the best performance. For assessment, a spatial extension strategy was employed that separated regions for training and testing. Results were consistent between the two reference sources. The best overall accuracy for the CWCS classes was 62–68%. Compared to a pixel-based random forest implementation this was 5–7% higher depending on the accuracy measure considered. For a parameter-optimized spatial-based implementation this was 2–4% higher. For a reduced set of classes to water, wetland, and upland, overall accuracy was in the range of 86–87%. Assessment for sampling over the entire region instead of spatial extension improved the mean class accuracies (F1-score) by 9% for the CWCS classes and for the reduced three-class level by 6%. The overall accuracies were 69% and 90% for the CWCS and reduced classes respectively with region sampling. Results in this study show that detailed classification of wetland types with Landsat remains challenging, particularly for small wetlands. In addition, further investigation of deep-learning methods are needed to identify CNN configurations and sampling methods better suited to moderate spatial resolution imagery across a range of environments. [ABSTRACT FROM AUTHOR]

To better understand and mitigate threats to the long-term health and functioning of wetlands, there is need to establish comprehensive inventorying and monitoring programs. Here, remote sensing data and machine learning techniques that could support or substitute traditional field-based data collection are evaluated. For the Bay of Quinte on Lake Ontario, Canada, different combinations of multi-angle/temporal quad pol RADARSAT-2, simulated compact pol RADARSAT Constellation Mission (RCM), and high and low spatial resolution Digital Elevation and Surface Models (DEM and DSM, respectively) were used to classify six land cover classes with Random Forests: shallow water, marsh, swamp, water, forest, and agriculture/non-forested. Results demonstrate that high accuracies can be achieved with multi-temporal SAR data alone (e.g., user's and producer's accuracies ≥90% for a model based on a spring image and a summer image), or via fusion of SAR and DEM and DSM data for single dates/incidence angles (e.g., user's and producer's accuracies ≥90% for a model based on a spring image, DEM, and DSM data). For all models based on single SAR images, simulated compact pol data generally achieved lower accuracies than quad pol RADARSAT-2 data. However, it was possible to compensate for observed differences through either multi-temporal/angle data fusion or the inclusion of DEM and DSM data (i.e., as a result, there was not a statistically significant difference between multiple models). With a higher repeat-pass cycle than RADARSAT-2, RCM is expected to be a reliable source of C-band SAR data that will contribute positively to ongoing efforts to inventory wetlands and monitor change in areas containing the same land cover classes evaluated here. [ABSTRACT FROM AUTHOR]