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Diameter at Breast Height (DBH) plays an important role in forest management. New technologies such as airborne and terrestrial Light Detection and Ranging (LiDAR) are expensive and time-consuming. We explored the iPad LiDAR sensor to measure DBH considering affordability and availability. The study was carried out at a research forest plantation near Thunder Bay, Ontario, Canada. Five plots were selected for differences in stem spacing, average DBH and species, and made 360° scans separately. A manual circle fitting method was used to produce circular features for the estimation of DBH. The coefficient of determination (R2) between estimated DBH and field measurements was 0.52, indicating a moderate accuracy. The Root Mean Square Error (RMSE) of DBH estimation ranged from 2.82 cm to 8.24 cm. The accuracy of smaller red pine trees was found to be relatively higher than that of larger red pine and white spruce trees. These accuracies did not change with the distance from the scanning location to trees. Therefore, results showed the potential use of an iPad LiDAR Scanner for DBH estimation. We recommend developing an application combining the iPad's location and LiDAR sensors and incorporating a precise positioning method for fieldwork, and further broad field testing. [ABSTRACT FROM AUTHOR]

Loss of forest cover and derived effects on forest ecosystems services has led to the establishment of land management policies and forest monitoring systems, and consequently to the demand for accurate and multitemporal data on forest extent and structure. In recent years, spaceborne Light Detection and Ranging (LiDAR) missions, such as the Global Ecosystem Dynamics Investigation (GEDI) instrument, have facilitated the repeated acquisition of data on the vertical structure of vegetation. In this study, we designed an approach incorporating GEDI and airborne LiDAR data, in addition to detailed forestry inventory data, for estimating tree-growth dynamics for the Laurentides wildlife reserve in Canada. We estimated an average tree-growth rate of 0.32 ± 0.23 (SD) m/year for the study site and evaluated our results against field data and a time series of NDVI from Landsat images. The results are in agreement with expected patterns in tree-growth rates related to tree species and forest stand age, and the produced dataset is able to track disturbance events resulting in the loss of canopy height. Our study demonstrates the benefits of using spaceborne-LiDAR data for extending the temporal coverage of forestry inventories and highlights the ability of GEDI data for detecting changes in forests' vertical structure. [ABSTRACT FROM AUTHOR]

Accurate knowledge of snow depth distributions in forested regions is crucial for applications in hydrology and ecology. In such a context, understanding and assessing the effect of vegetation and topographic conditions on snow depth variability is required. In this study, the spatial distribution of snow depth in two agro-forested sites and one coniferous site in eastern Canada was analyzed for topographic and vegetation effects on snow accumulation. Spatially distributed snow depths were derived by unmanned aerial vehicle light detection and ranging (UAV lidar) surveys conducted in 2019 and 2020. Distinct patterns of snow accumulation and erosion in open areas (fields) versus adjacent forested areas were observed in lidar-derived snow depth maps at all sites. Omnidirectional semi-variogram analysis of snow depths showed the existence of a scale break distance of less than 10 m in the forested area at all three sites, whereas open areas showed comparatively larger scale break distances (i.e., 11–14 m). The effect of vegetation and topographic variables on the spatial variability in snow depths at each site was investigated with random forest models. Results show that the underlying topography and the wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. These results highlight the importance of including and better representing these processes in physically based models for accurate estimates of snowpack dynamics. [ABSTRACT FROM AUTHOR]

The continental divide along the spine of the Canadian Rockies in southwestern Canada is a critical headwater region for hydrological drainages to the Pacific, Arctic, and Atlantic oceans. Major flooding events are typically attributed to heavy precipitation on its eastern side due to upslope (easterly) flows. Precipitation can also occur on the western side of the divide when moisture originating from the Pacific Ocean encounters the west-facing slopes of the Canadian Rockies. In other storms, substantial precipitation can fall on both sides. Meteorological data over this critical region are sparse, with few stations located at high elevations. Given the importance of all these types of events, the Storms and Precipitation Across the continental Divide Experiment (SPADE) was initiated to enhance our knowledge of the atmospheric processes leading to storms and precipitation on either side of the continental divide. This was accomplished by installing specialized meteorological instrumentation on both sides of the continental divide and carrying out manual observations during an intensive field campaign from 24 April–26 June 2019. On the eastern side, there were two field sites: (i) at Fortress Mountain Powerline (2076 m ASL) and (ii) at Fortress Junction Service, located in a high elevation valley (1580 m ASL). On the western side, Nipika Mountain Resort, also located in a valley (1087 m ASL), was chosen as a field site. Various meteorological instruments were deployed including two Doppler LiDARs, three vertically pointing micro rain radars and three optical disdrometers. The three main sites were nearly identically instrumented, and observers were on site at Fortress Mountain Powerline and Nipika Mountain Resort during precipitation events to take manual observations of precipitation type and microphotographs of solid particles. The objective of the field campaign was to gather high temporal frequency meteorological data and to compare the different conditions on either side of the divide to study the precipitation processes that can lead to catastrophic flooding in the region. Details on field sites, instrumentation used, and collection methods are discussed. Data from the study are publicly accessible from the public Federated Research Data Repository at https://dx.doi.org/10.20383/101.0221 (Thériault et al., 2020). This dataset will serve as a baseline for future work on atmospheric conditions over major orographic features by comparing the varying conditions on either side of a large topographic feature. This paper also provides a sample of the data gathered during a precipitation event. [ABSTRACT FROM AUTHOR]

Continuous large-scale mapping of forest canopy height is crucial for estimating and reporting forest carbon content, analyzing forest degradation and restoration, or to model ecosystem variables such as aboveground biomass. Over the last years, the spaceborne Light Detection and Ranging (LiDAR) sensor specifically designed to acquire forest structure information, Global Ecosystem Dynamics Investigation (GEDI), has been used to extract forest canopy height information over large areas. Yet, GEDI has no spatial coverage for most forested areas in Canada and other high latitude regions. On the other hand, the spaceborne LiDAR called Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a global coverage but was not specially developed to study forested ecosystems. Nonetheless, both spaceborne LiDAR sensors obtain point-based information, making spatially continuous forest canopy height estimation very challenging. This study compared the performance of both spaceborne LiDAR, GEDI and ICESat-2, combined with ALOS-2/PALSAR-2 and Sentinel-1 and -2 data to produce continuous canopy height maps in Canada for the year 2020. A set-aside dataset and airborne LiDAR (ALS) from a national LiDAR campaign were used for accuracy assessment. Both maps overestimated canopy height in relation to ALS data, but GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI. PALSAR-2 HV polarization was the most important covariate to predict canopy height, showing the great potential of L-band in comparison to C-band from Sentinel-1 or optical data from Sentinel-2. The approach proposed here can be used operationally to produce annual canopy height maps for areas that lack GEDI and ICESat-2 coverage. [ABSTRACT FROM AUTHOR]

Several species-specific habitat management guidelines have been created over the past decade to help recover declining populations of forest birds in eastern North America. Conserving eastern forest birds through forest management requires understanding the factors driving species occurrence patterns and how the needs of various species complement or contrast with each other. Our study assessed and compared occupancy patterns of two at-risk species associated with riparian forests: the Wood Thrush (Hylocichla mustelina) and the Canada Warbler (Cardellina canadensis). We conducted point-count surveys and vegetation sampling at 304 unique locations along 33 stream sections in central and southwestern Pennsylvania from May – June 2018 and 2019. We used single-season occupancy models to examine the influence of vegetation composition (i.e., microhabitat conditions), forest structure (i.e., from Light Detection and Ranging data, "LiDAR"), and other factors (i.e., stream order and elevation) on focal species occurrence patterns. Wood Thrush occupancy (mean ᴪ ˆ : 0.57) was best explained by the presence of mountain laurel (Kalmia latifolia ; negative effect), elevation (negative effect), canopy height (positive effect), and canopy structural heterogeneity (positive effect). Canada Warbler occupancy (mean ᴪ ˆ : 0.55) was best explained by the presence of mountain laurel (positive effect), stream order (positive effect), elevation (positive effect), density of understory vegetation (positive effect), and canopy structural heterogeneity (negative effect). While both bird species occurred relatively frequently in riparian forests, our results indicate that habitat factors at multiple spatial scales play a role in regulating their occurrence patterns. Moreover, the two species exhibited contrasting habitat associations. Specifically, Canada Warblers preferred sites supporting dense mountain laurel, typically at elevations >600 m, and with a largely intact canopy. These patterns were reversed for Wood Thrushes. These contrasting associations will limit opportunities to manage riparian forests in a manner that conveys co-benefits for our two focal species. As a general rule, forest managers in our study region can target habitat management for Canada Warblers in riparian areas above 600 m and in areas below 600 m for Wood Thrushes. Given our focal species' use of other (non-riparian) forest types as breeding habitat and broad geographic distributions, it is important to recognize that our findings may not apply outside of riparian forests or beyond the Central Appalachian Mountains. • We monitored Wood Thrush and Canada Warbler at 306 locations in riparian forests. • Elements of vegetation structure and composition influenced occupancy for both species. • Vegetation structure and composition associations differed between both species. • The two species were largely separated along an elevational gradient. • Working at respective elevations will reduce concerns about contrasting habitat needs. [ABSTRACT FROM AUTHOR]

Up-to-date information on the distribution and changes associated with wetlands is essential for their effective conservation and management. Satellite remote sensing is arguably the only practical way for mapping and monitoring wetlands in a timely manner over a large area, and therefore has been chosen by the Canadian Wetland Inventory (CWI) initiative to map Canada's wetlands and by the Environment and Sustainable Development Indicators (ESDI) initiative to monitor the extent of wetlands indicators. As a contribution to these initiatives, this paper presents a rule-based wetland mapping method using Landsat-7/ETM+, two-season Radarsat-1/SAR images and DEM data. Three study sites in eastern Canada were selected to test this new method. The test results indicate that the classification accuracy of the new method ranges from 71% to 92%, for open bog, open fen, treed bog, marsh and swamp at the three sites. In comparison, two traditional methods based on Landsat-7/ETM+ alone or both Landsat-7/ETM+ and Radarsat-1/SAR have accuracies of 24-89% and 78-85% respectively. [ABSTRACT FROM AUTHOR]

Wildfire decision support systems combine fuel maps with other fire environment variables to predict fire behaviour and guide management actions. Until recently, financial and technological constraints have limited provincial fuel maps to relatively coarse spatial resolutions. Airborne Laser Scanning (ALS), a remote sensing technology that uses LiDAR (Light Detection and Ranging), is becoming an increasingly affordable and pragmatic tool for mapping fuels across localised and broad areas. Few studies have used ALS in boreal forest regions to describe structural attributes such as fuel load at a fine resolution (i.e.,100m² cell resolution). We usedALS to predict five forest attributes relevant to fire behaviour in black spruce (Picea mariana) stands inAlberta, Canada: canopy bulk density, canopy fuel load, stem density, canopy height and canopy base height. Least absolute shrinkage and selection operator (lasso) regression models indicated statistically significant relationships betweenALS data and the forest metrics of interest (R²≥ 0.81 for all metrics except canopy base height which had a R² value of 0.63). Performance of the regression models was acceptable and consistent with prior studies when applied to test datasets; however, regression models presented in this study mapped stand attributes at a much finer resolution (40 m²). [ABSTRACT FROM AUTHOR]

Northwestern Canada's discontinuous permafrost landscape is transitioning rapidly due to permafrost thaw, with the conversion of elevated, forested peat plateaus to low‐lying, treeless wetlands. Increasing hydrological connectivity leads to partial drainage of previously‐isolated bogs, which have been observed to subsequently develop hummock microtopography. However, the role of microtopographic features in the future trajectory of the transitioning landscape is unclear, including their potential controls on tree re‐establishment. In order to understand the role of hummocks in landscape change, research was conducted at the Scotty Creek Research Station, Northwest Territories, to measure hummock and black spruce tree physical characteristics, and assess tree and hummock spatial coverage in peat plateaus, collapse scar bogs and the advanced transitional feature known as treed bogs. Canopy coverage in all landforms and wetland hummock areal coverage was assessed using a LiDAR (Light Detection and Ranging) canopy gap fraction model and multispectral imagery. Hummocks, which are not underlain by permafrost but contain seasonal ice, support the establishment of black spruce trees due to favourable soil moisture conditions. Hummock flank moisture in treed bogs is intermediate between those of dry peat plateaus and inundated collapse scar bogs. Black spruce trees on peat plateaus and in treed bogs are significantly taller and of greater circumference than those in collapse scar bogs. The spatial distribution of hummocks and canopy coverage of black spruce trees in treed bogs collectively suggest that these features may play an important role in the advanced stages of permafrost thaw‐driven transition of the discontinuous permafrost landscape. [ABSTRACT FROM AUTHOR]

Glaciers are commonly located in mountainous terrain subject to highly variable meteorological conditions. High resolution meteorological (HRM) data simulated by atmospheric models can complement meteorological station observations in order to assess changes in glacier energy fluxes and mass balance. We examine the performance of two snow models, SnowModel and Alpine3D, forced by different meteorological data for winter mass balance simulations at four glaciers in the Canadian portion of the Columbia Basin. The Weather Research and Forecasting model (WRF) with resolution of 1 km and the North American Land Data Assimilation System with ~12 km resolution, provide HRM data for the two snow models. Evaluation is based on the ability of the snow models to simulate snow depth at both point locations (automated snow weather stations) and over the entire glacier surface (airborne LiDAR [Light Detection and Ranging] surveys) during the 2015/2016 winter accumulation. When forced with HRM data, both models can reproduce snow depth to within ±15% of observed values. Both models underestimate winter mass balance when forced by HRM data. When driven with WRF data, SnowModel underestimates winter mass balance integrated over the glacier area by 1 and 10%, whilst Alpine3D underestimates winter mass balance by 12 and 22% compared with LiDAR and stake measurements, respectively. The overall results show that SnowModel forced by WRF simulated winter mass balance the best. [ABSTRACT FROM AUTHOR]

The article focuses on a study which aims at developing a portable, autonomous aerosol lidar system. It discusses the challenges faced by Environment Canada in operating the national lidar network Canadian Operational Research Aerosol Lidar Network (CORALNet) and describes the aerosol lidar technique which is used for measuring backscattered photons from a pulsed laser source.

Forests are an important global resource, playing key roles in both the environment and the economy. The implementation of quality national monitoring programs is required for the generation of robust national statistics, which in turn support global reporting. Conventional monitoring initiatives based on samples of field plots have proven robust but are difficult and costly to implement and maintain, especially for large jurisdictions or where access is difficult. To address this problem, air photo- and satellite-based large area mapping and monitoring programs have been developed; however, these programs also require ground measurements for calibration and validation. To mitigate this need for ground plot data we propose the collection and integration of light detection and ranging (lidar) based plot data. Lidar enables accurate measures of vertical forest structure, including canopy height, volume, and biomass. Rather than acquiring wall-to-wall lidar coverage, we propose the acquisition of a sample of scanned lidar transects to estimate conditions over large areas. Given an appropriate sampling framework, statistics can be generated from the lidar plots extracted from the transects. In other instances, the lidar plots may be treated similar to ground plots, providing locally relevant information that can be used independently or integrated with other data sources, including optical remotely sensed data. In this study we introduce the concept of "lidar plots" to support forest inventory and scientific applications, particularly for large areas. Many elements must be considered when planning a transect-based lidar survey, including survey design, flight and sensor parameters, acquisition considerations, mass data processing, and database development. We present a case study describing the acquisition of over 25 000 km of lidar data in Canada's boreal forests in the summer of 2010. The survey, which included areas of managed and unmanaged forests, resulted in the production of more than 17 million 25 x 25 m lidar plots with first returns greater than 2 m in height. We conclude with insights gained from the case study and recommendations for future surveys. [ABSTRACT FROM AUTHOR]

Abstract: There is a paucity of detailed and timely forest inventory information available for Canada''s large, remote northern boreal forests. The Canadian National Forest Inventory program has derived a limited set of attributes from a Landsat-based land cover product representing circa year 2000 conditions. Of the required inventory attributes, forest vertical structure (e.g., tree height) is critical for terrestrial biomass and carbon modeling and to date, is unavailable for these remote areas. In this study, we develop a large-area, fine-scale (25m) mapping solution to estimate tree height (mean, dominant, and Lorey''s height) across Canada''s northern forests by integrating lidar data (representing 0.27% of the study area), and Landsat imagery (representing 100% of the study area), using a geometric-optical modeling technique. First, spectral mixture analysis (SMA) was used to extract image endmembers and generate fraction images. Second, lidar data were used to calibrate the inverted geometric-optical model by adjusting the model''s three key fractional inputs: sunlit crown, sunlit background, and shade fraction, based upon the SMA derived images. The heterogeneity of the study area, spanning 2.16 million ha, made it challenging to directly and accurately decompose mixed Landsat image pixels into the canopy and background fractions used for the Li–Strahler geometric-optical model inversion. As a result we developed a novel method to use the lidar plot data to facilitate the calculation of these fractions in an accurate and automated manner. The average estimation errors for mean, dominant, and Lorey''s height were 4.9m, 4.1m, and 4.7m, respectively when compared to the lidar data, with the best result achieved using dominant tree height, where the average error was 3.5m for over 80% of the forested area. Using this approach of optical remotely sensed data calibrated and validated with lidar height estimates, we generate and evaluate wall-to-wall estimates of tree height that can subsequently be used as inputs for biomass and carbon modeling. [Copyright &y& Elsevier]

Abstract: The GEOgraphic Object-Based Image Analysis (GEOBIA) paradigm continues to prove its efficacy in remote sensing image analysis by providing tools which emulate human perception and combine analyst''s experience with meaningful image-objects. However, challenges remain in the evolution of this new paradigm as sophisticated methods attempt to deliver on the goal of automated geo-intelligence (i.e., geospatial content within context) from geospatial sources. In order to generate geo-intelligence from a forest scene, this article introduces a GEOBIA framework to estimate canopy height, above-ground biomass (AGB) and volume by combining lidar (light detection and ranging) transects, Quickbird imagery and machine learning algorithms. This framework is comprised three main components: (i) image-object extraction, (ii) lidar transect selection, and (iii) forest parameter generalization. The rational for integrating these methods is to provide a semi-automatic GEOBIA approach from which detailed forest information is obtained at the individual tree crown or small tree cluster level (i.e., mean object size of 0.04ha); while also dramatically reducing airborne lidar data acquisition costs. Analysis is performed over a 16,330ha forested study site in Quebec, Canada. Forest parameter estimation results derived from our GEOBIA framework demonstrate a strong relationship with those using the full lidar cover; where the highest estimates for canopy height (R =0.85; RMSE=3.37m), AGB (R =0.85; RMSE=39.48Mg/ha) and volume (R =0.85; RMSE=52.59m3/ha) were achieved using a lidar transect sample representing only 7.6% of the total study area. [Copyright &y& Elsevier]

Abstract: Height measurements from small-footprint discrete-return LiDAR and backscatter coefficients from C- and L-band radar were used independently and in combination to estimate above-ground component and total biomass for a coniferous temperate forest, located on Vancouver Island, British Columbia, Canada. Reference biomass data were obtained from plot-level data and used for comparison against the LiDAR and radar-based biomass models. For the LiDAR-only model, height metrics such as mean first return height and percentiles (e.g., 10th and 90th) of first returns correlated best to total above-ground and stem biomass. While percent of first returns above 2m and percentiles (75th and 90th) of first returns height metrics correlated best to crown biomass. A comparison between above-ground components and total biomass indicate that stem biomass displayed the highest relationship with the LiDAR measurements while crown biomass showed the lowest relationship with relative root mean squared error ranging from 16% to 22%, respectively. Alternatively, the radar-only models indicated that for C-band radar, a combination of HH and VV backscatter demonstrated the most significant correlation with forest biomass compared to coherence based models with a relative root mean squared error of 53%. For L-band radar, a combination of HH and HV backscatter showed the most significant correlation compared to coherence based models with a relative root mean squared error of 44%. Exploring a mixture of C- and L-band backscatter and coherence based models revealed that a combination of C-HV and L-HV coherence magnitudes provided the best radar relationship with forest biomass with a relative root mean squared error of 35%. Also for all radar-based models, L- and C-band backscatter and coherence magnitudes were poorly correlated with individual biomass components when compared to total above-ground biomass. The addition of C- and L-band backscatter and coherence variables to the LiDAR-only biomass model was also investigated. The results showed that the integration of C-band HH backscatter to the LiDAR-only model significantly improved the relationship with forest biomass by explaining an additional 8.9% and 6.5% of the variability in total aboveground and stem biomass respectively, while C-band polarimetric entropy explained an additional 17.9% of the variability in crown biomass. Improvements in the relative root mean squared errors were also observed ranging from 7.1% to 11.7%. The study suggests that for a temperate forest dominated by coniferous stands, the addition of C-band radar variables to a best LiDAR-only linear model provides improved estimates of above-ground component and total biomass. [Copyright &y& Elsevier]

The FUDOTERAM is a national Canadian light detection and ranging (LIDAR) project founded by the Canadian Network of Excellence GEOmatics for Informed DEcision (GEOIDE) that investigates data fusion from airborne, marine, and terrestrial mapping sensors. In March 2009, the second Fusion des Données TERrestres, Aériennes et Marines (FUDOTERAM) workshop was held in Quebec City, Quebec, Canada. The focus of the workshop was on international collaboration: Workshops can provide an international platform for sharing ideas and study results among academy, industry, mapping and charting organizations, and service providers. LIDAR work and research included data collected from seven different coastal areas in four nations. This special issue contains selected studies from the second FUDOTERAM workshop on LIDAR technology applied in coastal studies and management. Current studies in this special issue explore LIDAR processing in charting and mapping organizations, shoreline mapping, data integration, coastal processes and coastal management, and seafloor characterization. [ABSTRACT FROM AUTHOR]

Abstract: To effectively manage forested ecosystems an accurate characterization of species distribution is required. In this study we assess the utility of hyperspectral Airborne Imaging Spectrometer for Applications (AISA) imagery and small footprint discrete return Light Detection and Ranging (LiDAR) data for mapping 11 tree species in and around the Gulf Islands National Park Reserve, in coastal South-western Canada. Using hyperspectral imagery yielded producer''s and user''s accuracies for most species ranging from >52–95.4 and >63–87.8%, respectively. For species dominated by definable growth stages, pixel-level fusion of hyperspectral imagery with LiDAR-derived height and volumetric canopy profile data increased both producer''s (+5.1–11.6%) and user''s (+8.4–18.8%) accuracies. McNemar''s tests confirmed that improvements in overall accuracies associated with the inclusion of LiDAR-derived structural information were statistically significant (p <0.05). This methodology establishes a specific framework for mapping key species with greater detail and accuracy then is possible using conventional approaches (i.e., aerial photograph interpretation), or either technology on its own. Furthermore, in the study area, acquisition and processing costs were lower than a conventional aerial photograph interpretation campaign, making hyperspectral/LiDAR fusion a viable replacement technology. [Copyright &y& Elsevier]

Abstract: The purpose of this study was to estimate the fraction of photosynthetically active radiation absorbed by the canopy (fPAR) from point measurements to airborne lidar for hierarchical scaling up and assessment of the Moderate Resolution Imaging Spectroradiometer (MODIS) fPAR product within a “medium-sized” (7 km×18 km) watershed. Nine sites across Canada, containing one or more (of 11) distinct species types and age classes at varying stages of regeneration and seasonal phenology were examined using a combination of discrete pulse airborne scanning Light Detection And Ranging (lidar) and coincident analog and digital hemispherical photography (HP). Estimates of fPAR were first compared using three methods: PAR radiation sensors, HP, and airborne lidar. HP provided reasonable estimates of fPAR when compared with radiation sensors. A simplified fractional canopy cover ratio from lidar based on the number of within canopy returns to the total number of returns was then compared with fPAR estimated from HP at 486 geographically registered measurement locations. The return ratio fractional cover method from lidar compared well with HP-derived fPAR (coefficient of determination=0.72, RMSE=0.11), despite varying the lidar survey configurations, canopy structural characteristics, seasonal phenologies, and possible slight inaccuracies in location using handheld GPS at some sites. Lidar-derived fractional cover estimates of fPAR were ∼10% larger than those obtained using HP (after removing wood components), indicating that lidar likely provides a more realistic estimate of fPAR than HP when compared with radiation sensors. Finally, fPAR derived from lidar fractional cover was modelled at 1 m resolution and averaged over 99 1 km areas for comparison with MODIS fPAR. The following study is one of the first to scale between plot measurements and MODIS pixels using airborne lidar. [Copyright &y& Elsevier]

This study compared the suitability of LIDAR (LIght Detection And Ranging) data, three-band multispectral data, and LIDAR data integrated with multispectral information, for classifying spatially complex vegetation in the Aspen Parkland of western Canada. Classifications were performed for both a) general vegetation classes limited to three major formations of deciduous forest, shrubland and grassland, and b) eight detailed vegetation classes including upland mixed prairie and fescue grasslands, closed and semi-open aspen forests, western snowberry and silverberry shrublands, and fresh and saline riparian (lowland) meadows. A Digital Elevation Model (DEM) and Surface Elevation Model (SEM) developed from LIDAR data incorporated both topographic and biological biases in community positioning across the landscape. Using multispectral data, the original digital image mosaic, its hybrid color composite, and an intensity–hue–saturation (IHS) image were each tested. Final vegetation classification was done through integration of information from both digital images and LIDAR data to evaluate the improvement in classification accuracy. Among the land cover schedules with three and eight classes of vegetation, classification from the multispectral imagery, specifically the hybrid color composite image, had the highest accuracy, peaking at 74.6% and 59.4%, respectively. In contrast, the LIDAR classification schedules led to an average classification accuracy of 64.8% and 52.3%, respectively, for the general and detailed vegetation data. Subsequent integration of the LIDAR and digital image classification schedules resulted in accuracy improvements of 16 to 20%, resulting in a superior final accuracy of 91% and 80.3%, respectively, for the three and eight classes of vegetation. A final land cover map including 8 classes of vegetation, fresh and saline water, as well as bare ground, was created for the study area with an overall accuracy of 83.9%, highlighting the benefit of integrating LIDAR and multispectral imagery for enhanced vegetation classification in heterogenous rangeland environments. [Copyright &y& Elsevier]

Forest dynamics are characterized by both continuous (i.e., growth) and discontinuous (i.e., disturbance) changes. Change detection techniques that use optical remotely sensed data to capture disturbance related changes are established and commonly applied; however, approaches for the capture of continuous forest changes are less mature. Optical remotely sensed imagery is well suited for capturing horizontally distributed conditions, structures, and changes, while Light Detection And Ranging (LIDAR) data are more appropriate for capturing vertically distributed elements of forest structure and change. The integration of optical remotely sensed imagery and LIDAR data provides improved opportunities to fully characterize forest canopy attributes and dynamics. The study described in this paper captures forest conditions along a corridor approximately 600 km long through the boreal forest of Canada. Two coincident LIDAR transects, representing 1997 and 2002 forest conditions respectively, are compared using image segments generated from Landsat ETM+ imagery. The image segments are used to provide a spatial framework within which the attributes and temporal dynamics of the forest canopy are estimated and compared. Segmented and classified Landsat imagery provides a context for the comparison of sufficiently spatially related LIDAR profiles and for the provision of categories to aid in the application of empirical models requiring knowledge of land cover. Global and local approaches were employed for characterizing changes in forest attributes over time. The global approach, emphasized the overall trend in forest change along the length of the entire transect, and indicated that key canopy attributes were stable, and transect characteristics, including forest canopy height, did not change significantly over the five-year period of this study (two sample t-test, p =0.08). The local approach analyzed segment-based changes in canopy attributes, providing spatially explicit indications of forest growth and depletion. The local approach identified that 84% of the Landsat segments intercepted by both LIDAR transects either have no change, or have a small average increase in canopy height (0.7 m), while the other 16% of segments have an average decrease in canopy height of 1.6 m. As expected, the difference in the magnitude of the changes was markedly greater for depletions than it was for growth, but was less spatially extensive. Growth tends to occur incrementally over broad areas; whereas, depletions are dramatic and spatially constrained. The approach presented holds potential for investigating the impacts of climate change across a latitudinal gradient of boreal forest. [Copyright &y& Elsevier]

Light detection and ranging (lidar) is becoming an increasingly popular technology among scientists for the development of predictive models of forest biophysical variables. However, before this technology can be adopted with confidence for long-term monitoring applications in Canada, robust models must be developed that can be applied and validated over large and complex forested areas. This will require "scaling-up" from current models developed from high-density lidar data to low-density data collected at higher altitudes. This paper investigates the effect of lowering the average point spacing of discrete lidar returns on models of forest biophysical variables. Validation of results revealed that high-density models are well correlated with mean dominant height, basal area, crown closure, and average aboveground biomass (R2 = 0.84, 0.89, 0.60, and 0.91, respectively). Low-density models could not accurately predict crown closure (R2 = 0.36). However, they did provide slightly improved estimates for mean dominant height, basal area, and average aboveground biomass (R2 = 0.90, 0.91, and 0.92, respectively). Maps were generated and validated for the entire study area from the low-density models. The ability of low-density models to accurately map key biophysical variables is a positive indicator for the utility of lidar data for monitoring large forested areas. [ABSTRACT FROM AUTHOR]

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Satellite forest inventories are the only feasible way to map Canada's vast, remote forest regions, such as those in the Northwest Territories (NWT). A method used to create such inventories is the k-nearest neighbour (k-NN) algorithm, which spatially extends information from forest inventory (FI) plots to the entire forest land base using wall-to-wall features typically derived from Landsat data. However, the benefits of integrating L-band synthetic aperture radar (SAR) data, strongly correlated to forest biomass, have not been assessed for Canadian northern boreal forests. Here we describe an optimized multivariate k-NN implementation of a 151,700 km2 area in southern NWT that included ca. 2007 Landsat and dual-polarized Phased Array type L-band SAR (PALSAR) data on board the Advanced Land Observing Satellite (ALOS). Five forest attributes were mapped at 30 m cells: stand height, crown closure, stand/total volume and aboveground biomass (AGB). We assessed accuracy gains compared to Landsat-based maps. To circumvent the scarcity of FI plots, we used 3600 footprints from the Geoscience Laser Altimeter System (GLAS) as surrogate FI plots, where forest attributes were estimated using Light Detection and Ranging (LiDAR) metrics as predictors. After optimization, k-NN predicted forest attribute values for each pixel as the average of the 4 nearest (k = 4) surrogate FI plots within the Euclidian space of 9 best features (selected among 6 PALSAR, 10 Landsat, and 6 environmental features). Accuracy comparisons were based on 31 National Forest Inventory ground plots and over 1 million airborne LiDAR plots. Maps that included PALSAR HV backscatter resulted in forest attribute predictions with higher goodness of fit (adj. R2), lower percent mean error (ME%), and percent root mean square error (RMSE%), and lower underestimation for larger attribute values. Predictions were most accurate for conifer stand height (RMSE% = 32.1%, adj. R2 = 0.58) and AGB (RMSE% = 47.8%, adj. R2 = 0.74), which is much more abundant in the area than mixedwood or broadleaf. Our study demonstrates that optimizing k-NN parameters and feature space, including PALSAR, Landsat, and environmental variables, is a viable approach for inventory mapping of the northern boreal forest regions of Canada. [ABSTRACT FROM AUTHOR]

As airborne laser scanning (ALS) gains wider adoption to support forest operations in Canada, the consistency and quality of derivative products that support long-term monitoring and planning are becoming a key issues for managers. The Canadian Consortium for Lidar Environmental Applications Research (C-CLEAR) has supported almost 200 projects across Canada since 2000, with forest-related studies being a dominant theme. In 2010 and 2011, field operations were mobilized to support 13 ALS projects spanning almost the full longitudinal gradient of Canada's forests. This paper presents case studies for seven plus an overview of some best practices and data processing workflow tools that have resulted from these consortium activities. Although the projects and research teams are spread across Canada, the coordination and decade of experience provided through C-CLEAR have brought common methodological elements to all. It is clear that operational, analytical and reporting guidelines that adhere to community accepted standards are required if the benefits promised by ALS forestry are to be realized. A national Lidar Institute that builds upon the C-CLEAR model and focuses on developing standards, guidelines, and certified training would address this need. [ABSTRACT FROM AUTHOR]

Copyright of Atmosphere -- Ocean (Taylor & Francis Ltd) is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High-resolution aerial photography was captured covering a geological outcrop at Castle Creek, British Columbia, Canada. Here, for the purposes of hydrocarbon analog modeling, the outcrop was required to be accurately surveyed, so that key stratigraphic surfaces could be mapped in three dimensions. Because the outcrop strata were vertically orientated, these surfaces could be tracked over a wide area; however, to provide a true reconstruction of the geology, it was necessary to also model localized vertical cliffs providing a cross-section through the stratigraphy. Terrestrial lidar was utilized to cover these cliff sections which were poorly represented in the 2.5D aerial data. The integrated outcrop surface was textured with metric aerial and terrestrial imagery providing a photorealistic model that could be used for interpretation by geologists. This formed the basis for building a geocellular model of the geological volume, which was used to assist in the understanding of subsurface reservoirs where data are often limited. [ABSTRACT FROM AUTHOR]

The new full polarimetric modes of Radarsat-2 were evaluated for digital elevation model (DEM) generation using stereo-radargrammetry with Toutin's three-dimensional physical model. The fusion of the four polarimetric bands was thus addressed to improve the resulting DEMs. The stereo-radargrammetric DEMs were evaluated with 20 cm accuracy Lidar elevation data. Results on a test site north of Quebec City, Canada showed a good accuracy: 5 m and 6-7 m (1σ) for the horizontal positioning and the elevation (on bare soils), respectively. However, the best results were obtained when the four polarimetric bands were fused with the restoration method and the second best results (around 5% worse) were obtained with the SPAN method (total power of the four intensity bands). [ABSTRACT FROM AUTHOR]

The accuracy of the Canadian Digital Elevation Data (CDED) produced over the past was based on the accuracy of the sources used for their creation (elevation extracted form contour lines or provincial data exchange). No means to characterize the absolute vertical precision was available, particularly in remote areas. A new production of CDED in the North is currently carried out by the Centre for Topographic Information, Natural Resources Canada with the support of the Canadian Space Agency. Approximately 1,500 new data sets are being produced. Altimetric data is partly acquired with the European Remote Sensing satellite (ERs) by interferometry (70 percent) and partly by stereo-compilation with aerial photography (30 percent). The assessment of the absolute altimetric accuracy of the CDED5 themselves as opposed from the sources is required. ICESAT lidar data gives us such an opportunity. The results obtained on the first CDED data sets produced with ERS interferometry are very promising. Accuracy for a group of 21 CDED is in the order of 0.34 m ± 6.22 m, i.e., 10 m at 90 percent confidence level. Accuracy is recorded in the metadata of each data set and is freely available on the GEOBASE portal (http://www.geobase.co/). [ABSTRACT FROM AUTHOR]

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Threats associated with bioaerosol weapons have been around for several decades. However, with the recent political developments that changed the image and dynamics of the international order and security, the visibility and importance of these bioaerosol threats have considerably increased. Over the last few years, Defence Research and Development Canada has investigated the spectrometric LIDAR-based standoff bioaerosol detection technique to address this menace. This technique has the advantages of rapidly monitoring the atmosphere over wide areas without physical intrusions and reporting an approaching threat before it reaches sensitive sites. However, it has the disadvantages of providing a quality of information that degrades as a function of range and bioaerosol concentration. In order to determine the importance of these disadvantages, Canada initiated in 1999 the SINBAHD (Standoff Integrated Bioaerosol Active Hyperspectral Detection) project investigating the standoff detection and characterization of threatening biological clouds by Laser-Induced Fluorescence (LIF) and intensified range-gated spectrometric detection techniques. This article reports an overview of the different lessons learned with this program. Finally, the BioSense project, a Technology Demonstration Program aiming at the next generation of wide area standoff bioaerosol sensing, mapping, tracking and classifying systems, is introduced. [ABSTRACT FROM AUTHOR]

Detailed GIS studies across spatially complex rangeland landscapes, including the Aspen Parkland of western Canada, require accurate digital elevation models (DEM). Following the interpolation of last return lidar (light detection and ranging) data into a DEM, a series of 256 reference plots, stratified by vegetation type, slope and lidar sensor sampling angle, were surveyed using a total laser station, differential GPS and 27 interconnected benchmarks to assess variation in DEM accuracy. Interpolation using Inverse Distance Weighting IDW resulted in lower mean error than other methods. Across the study area, overall signed error and RMSE were +0.02 m and 0.59 m, respectively. Signed errors indicated elevations were over-estimated in forest but under-estimated within meadow habitats. Increasing slope gradient increased vertical absolute errors and RMSE. In contrast, lidar sampling angle had little impact on measured error. These results have implications for the development and use of high-resolution DEM models derived from lidar data. [ABSTRACT FROM AUTHOR]

Irishmans Road Recreation Site, in Hants County, Nova Scotia, is a publicly accessible park with hundreds of naturally occurring sinkholes. These karstic geomorpho-logical features form as a result of deformation, dissolution, and erosion of Carboniferous evaporite-bearing rocks that exist near the surface. Despite their acknowledged risk factor by the local municipality, no systematic sinkhole mapping has been done in this area. LiDAR (Light Detection and Ranging) data at two different resolutions complement the field-acquired data to map these sinkholes: (1) provincial 1 m resolution collected between 2018 and 2020, and (2) private 25 cm resolution collected in 2022. An in-house automated sinkhole delineation program, COLDS, located over 2000 sinkholes within the Irishmans Road property. However, field data acquired in 2023 suggest inaccuracies in COLDS' results. In areas of lower concentration, COLDS accurately located and defined the shape of large sinkholes. In areas of higher concentration, COLDS struggled to delineate small sinkholes, resulting in sinkholes being combined or missed. For example, in a 1200 m2 area in the northern area, 101 sinkholes were located, measured, and mapped, compared to the 57 sinkholes identified using COLDS. Precise mapping of these sinkholes is important because despite being aware of these sinkholes as a possible threat and health risk within the park and wider area, the county continues to allow unrestricted access to Irishmans Road Recreation Site. The sinkholes can be deep and the lack of clear communication of risks may result in bodily harm to site visitors. [ABSTRACT FROM AUTHOR]

Post-glacial faults (PGFs) have been studied in Scandinavia for over 50 years but no such features been verified in the deglaciated terrane of Canada. We suggest that exploratory analyses of archival aerial photographs and satellite imagery can provide possible evidence of PGFs in Canada. As part of an air photo search more than half a century ago, renowned photogeologist Jack D. Mollard came across a "fault-line scarp" in north-central Manitoba, since dubbed the Holy Grail Fault (HGF), at 55o05'55"N / 99o06'38"W. This N26oE trending, remarkably youthful-looking, linear escarpment of at least 20 km length drew only a specialized engineering audience at the time. Meanwhile, a broad range of satellite imagery available over the area and subsequent analysis may strengthen the interest in validating the HGF as a significant post-glacial fault. Isolation and accessibility of the HGF area of north-central Manitoba pose significant logistical problems for conducting field work. The remote sensing component of our work has several objectives: to re-examine the photo-geological extent and expressions of the suspected PGF; to review seasonal environmental conditions along the fault; and to determine sites for further field work. For this study we review previous work and focus on optical and radar imagery taken at different illumination angles, during various seasonal hydrological conditions and at a range of spatial scales. We also rely on old aerial photography of the area as a source of information. Preliminary image analysis indicates that the HGF feature is unique in its linearity and physiographic characteristics. The fault appears to extend tens of kilometres beyond the initial scarp. Our conclusions to date indicate that remote sensing data collection and analysis can benefit significantly from taking into account varying seasonal environmental conditions to highlight and characterize the Holy Grail Fault as a PGF. [ABSTRACT FROM AUTHOR]

Mariners navigating within Canadian waters rely on Canadian Hydrographic Service (CHS) navigational charts to safely reach their destinations. To fulfil this need, CHS charts must accurately reflect the current state of Canadian coastal regions. While many coastal regions are stable, others are dynamic and require frequent updates. In order to ensure that important and potentially dangerous changes are reflected in CHS products, the organization, in partnership with the Canadian Space Agency, is exploring coastal change detection through satellite remote sensing (SRS). In this work, CHS examined a hybrid shoreline extraction approach which uses both Synthetic Aperture Radar (SAR) and optical data. The approach was applied for a section of the Mackenzie River, one of Canada's most dynamic river systems. The approach used RADARSAT-2 imagery as its primary information source, due to its high positioning accuracy (5 m horizontal accuracy) and ability to allow for low and high water line charting. Landsat represented the primary optical data source due to its long historical record of Earth observation data. Additional sensors, such as Sentinel-2 and WorldView, were also used where a higher resolution was required. The shoreline extraction process is based on an image segmentation approach that uses both the radar and optical data. Critical information was collected using the automated approach to support chart updates, resulting in reductions to the financial, human and time factors present within the ship-based hydrographic survey techniques traditionally used for chart improvements. The results demonstrate the potential benefit of wide area SRS change detection within dynamic waterways for navigational chart improvements. The work also demonstrates that the approach developed for RADARSAT-2 could be implemented with data from the forthcoming RADARSAT Constellation Mission (RCM), which is critical to ensure project continuity. [ABSTRACT FROM AUTHOR]

Abstract: Topographic depressions are abundant in topographically complex landscapes. A common practice with earlier, low resolution Digital Elevation Models (DEMs) was to remove all depressions to ensure that water flowed continuously to the edge of the DEM domain. The assumption was that most depressions were created due to errors in the DEMs. This practice is no longer justified with the increasing availability of high accuracy DEMs. However, very few studies have addressed how DEM processing options such as smoothing and coarsening and setting area and depth thresholds can affect depression identification. In this study, a site located in the Prairie Region of Canada was examined. The site is a hummocky glaciated landscape with many in-field wetlands. Lidar topographic data were collected and were used to generate a 1m by 1m square-grid DEM. Detailed error analyses of the lidar DEM were conducted. A set of DEMs were generated after different degrees of smoothing and coarsening. FlowMapR, an established terrain analysis tool, was used to identify depressions in each DEM with various user-defined area and depth thresholds. The results were validated against a field wetland survey. We determined that the problems associated with depression identification using a lidar DEM are two-fold. On one hand, artefactual depressions created due to DEM errors need to be eliminated, for which the raw lidar DEM need to be smoothed. On the other hand, it is also desirable to remove those topographic depressions that do not function as closed basins at the spatial or temporal scale of the processes of interest. Setting area and depth thresholds appeared to be the preferred choice for this. We suggested using the un-autocorrelated lidar DEM error as the criterion for DEM smoothing and considering depression connections in the selection of area and depth thresholds. Using lidar data on a hummocky landscape with loamy soils in the Prairie Region of Canada, 10 to 20 times smoothing operations with an area threshold of 200m2 and a depth threshold of 0.1m were recommended as guidelines for depression identification. [Copyright &y& Elsevier]

Passive optical remotely sensed images such as those from the Landsat satellites enable the development of spatially comprehensive, well-calibrated reflectance measures that support large-area mapping. In recent years, as an alternative to field plot data, the use of Light Detection and Ranging (lidar) acquisitions for calibration and validation purposes in combination with such satellite reflectance data to model a range of forest structural response variables has become well established. In this research, we use a predictive modeling approach to map forest structural attributes over the ~ 552 million ha boreal forest of Canada. For model calibration and independent validation we utilize airborne lidar-derived measurements of forest vertical structure (known as lidar plots) obtained in 2010 via a > 25,000 km transect-based national survey. Models were developed linking the lidar plot structural variables to wall-to-wall 30-m spatial resolution surface reflectance composites derived from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus imagery. Spectral indices extracted from the composites, disturbance information (years since disturbance and type), as well as geographic position and topographic variables (i.e., elevation, slope, radiation, etc.) were considered as predictor variables. A nearest neighbor imputation approach based on the Random Forest framework was used to predict a total of 10 forest structural attributes. The model was developed and validated on > 80,000 lidar plots, with R 2 values ranging from 0.49 to 0.61 for key response variables such as canopy cover, stand height, basal area, stem volume, and aboveground biomass. Additionally, a predictor variable importance analysis confirmed that spectral indices, elevation, and geographic coordinates were key sources of information, ultimately offering an improved understanding of the driving variables for large-area forest structure modeling. This study demonstrates the integration of airborne lidar and Landsat-derived reflectance products to generate detailed and spatially extensive maps of forest structure. The methods are portable to map other attributes of interest (based upon calibration data) through access to Landsat or other appropriate optical remotely-sensed data sources, thereby offering unique opportunities for science, monitoring, and reporting programs. [ABSTRACT FROM AUTHOR]

A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research. [ABSTRACT FROM AUTHOR]

Abstract: The provincial wetland (“status quo”) maps of the Prairie Pothole Region, Central Canada, do not adequately depict wetland resources and properties. Using satellite remote sensing data from both LANDSAT Enhanced Thematic Mapper Plus (ETM+) and RADARSAT-1 Synthetic Aperture Radar (SAR) results in a more complete picture, although using both sources together is better than when either source is used alone. This study integrates LANDSAT ETM+, RADARSAT-1 SAR, and LIght Detection And Ranging (LIDAR) data, taking advantage of the synergy in their integration. A simple density slicing of the ETM-5 band was used to map inundated areas from LANDSAT ETM+. A fuzzy thresholding technique was used to map wet areas using RADARSAT-1 SAR data after information from LIDAR-DEMs had been used to correct confusing radar backscatter overlaps between open water and dry, flat, smooth surfaces. Compared to the “status quo”, the integrated approach mapped 113% to 600% and 217% to 467% increases in the size of wet areas and pond densities, respectively. Maps based on the ETM-5 band alone detected 133% to 333% and 50% to 350% increases in the size of wet areas and pond densities, respectively over the “status quo” map, while maps based on the RADARSAT-1 SAR data detected 63% to 450% and 100% to 333% increases. The improved mapping capability is attributed to a combinatory power of the integrated approach in detecting small, transient and saturated wet areas. [Copyright &y& Elsevier]

How aggregators reconcile repositories' user-supplied subject keywords is a growing challenge in the metadata profession. While aggregators allow users to search across multiple databases to find information, the search capability is only as good as the supplied metadata. This paper is a case study of a project to reconcile harvested metadata keywords within a research data discovery service. The Federated Research Data Repository (FRDR) Discovery Service is a national, bilingual platform for discovering Canadian research data that harvests metadata from over 90 repositories. This paper outlines the work of a cross-Canada, volunteer group of experts who attempted to develop a semi-automated workflow to map the FRDR subject keywords to Faceted Application of Subject Terminology (FAST) to improve discoverability. The authors, who were members of the working group, discuss why the project failed, the problems encountered, and their thoughts on the future of automated metadata reconciliation. [ABSTRACT FROM AUTHOR]

Lakes are important sources of freshwater and provide essential ecosystem services. Monitoring their spatial and temporal variability, and their functions, is an important task within the development of sustainable water management strategies. The Surface Water and Ocean Topography (SWOT) mission will provide continuous information on the dynamics of continental (rivers, lakes, wetlands and reservoirs) and ocean water bodies. This work aims to contribute to the international effort evaluating the SWOT satellite (2022 launch) performance for water balance assessment over large lakes (e.g., >100 km 2). For this purpose, a hydrodynamic model was set up over Mamawi Lake, Canada, and different wind scenarios on lake hydrodynamics were simulated. The derived water surface elevations (WSEs) were compared to synthetic elevations produced by the Jet Propulsion Laboratory (JPL) SWOT high resolution (SWOT-HR) simulator. Moreover, water storages and net flows were retrieved from different possible SWOT orbital configurations and synthetic gauge measurements. In general, a good agreement was found between the WSE simulated from the model and those mimicked by the SWOT-HR simulator. Depending on the wind scenario, errors ranged between approximately -2 and 5 cm for mean error and from 30 to 70 cm root mean square error. Low spatial coverage of the lake was found to generate important biases in the retrievals of water volume or net flow between two satellite passes in the presence of local heterogeneities in WSE. However, the precision of retrievals was found to increase as spatial coverage increases, becoming more reliable than the retrievals from three synthetic gauges when spatial coverage approaches 100 %, demonstrating the capabilities of the future SWOT mission in monitoring dynamic WSE for large lakes across Canada. [ABSTRACT FROM AUTHOR]

The Balloon Lidar Experiment (BOLIDE) was the first high-power lidar flown and operated successfully on board a balloon platform. As part of the PMC Turbo payload, the instrument acquired high-resolution backscatter profiles of polar mesospheric clouds (PMCs) from an altitude of ∼ 38 km during its maiden ∼ 6 d flight from Esrange, Sweden, to northern Canada in July 2018. We describe the BOLIDE instrument and its development and report on the predicted and actual in-flight performance. Although the instrument suffered from excessively high background noise, we were able to detect PMCs with a volume backscatter coefficient as low as 0.6×10-10 m -1 sr -1 at a vertical resolution of 100 m and a time resolution of 30 s. [ABSTRACT FROM AUTHOR]

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

Despite wide applications of remote-sensing data with high temporal resolution for monitoring phenology, two persistent problems have prevented the realization of their full potential. The first is the subjectivity in defining thresholds for a phenological event (e.g. the start or end of growing season − SOS or EOS). The second is the use of various arbitrarily selected filtering and smoothing algorithms for constructing vegetation index seasonal profiles in order to reduce the noise caused by residue cloud contamination and aerosol variations. In this study, we addressed both problems by developing a biophysically based and objective satellite seasonality observation method (BLOSSOM) for application over Canada’s Arctic. Application of the BLOSSOM method to three northern Canadian national parks (Ivvavik, Wapusk, and Sirmilik) proved that the method is operational. Using the uncertainties in the vegetation index and its threshold, we estimated the overall mean uncertainties as being −5.3 to 3.4 days, −4.2 to 5.2 days, and −6.2 to 8.4 days, respectively, for SOS, EOS, and growing season length (GSL). Further independent tests against SOS, determined using records of snow cover at nearby climate stations (as ‘truth’), indicate that the mean absolute error is less than 3.6 ± 0.2 days. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Successful conservation planning for the Canadian boreal forest requires biodiversity data that are both accessible and reliable. Spatially exhaustive data is required to inform on conditions, trends and context, with context enabling consideration of conservation opportunities and related trade-offs. However, conventional methods for measuring biodiversity, while useful, are spatially constrained, making it difficult to apply over wide geographic regions. Increasingly, remotely sensed imagery and methods are seen as a viable approach for acquiring explicit, repeatable and multi-scale biodiversity data over large areas. To identify relevant remotely derived environmental indicators specific to biodiversity within the Canadian boreal forest, we assessed indicators of the physical environment such as seasonal snow cover, topography and vegetation production. Specifically, we determined if the indicators provided distinct information and whether they were useful predictors of species richness (tree, mammal, bird and butterfly species). Using cluster analysis, we also assessed the applicability of these indicators for broad ecosystem classification of the Canadian boreal forest and the subsequent attribution of these stratified regions (i.e. clusters). Our results reveal that the indicators used in the cluster creation provided unique information and explained much of the variance in tree (92.6%), bird (84.07%), butterfly (61.4%) and mammal (22.6%) species richness. Spring snow cover explained the most variance in species richness. Results further show that the 15 clusters produced using cluster analysis were principally stratified along a latitudinal gradient and, while varied in size, captured a range of different environmental conditions across the Canadian boreal forest. The most important indicators for discriminating between the different cluster groups were seasonal greenness, a multipart measure of climate, topography and land use, and wetland cover, a measure of the percentage of wetland within a 1 km2 cell. [ABSTRACT FROM AUTHOR]

Joint observations of the all-sky camera at Resolute Bay (Nunavut, Canada) and the Polar Dual Auroral Radar Network (PolarDARN) HF radars at Rankin Inlet and Inuvik (Canada) are considered to establish radar signatures of poleward moving polar cap arcs "detaching" from the auroral oval. Common features of the events considered are enhanced power or echo occurrence in the wake of the arcs and enhanced spectral width of these echoes. When the arcs were oriented along some of the radar beams, velocity reversals at the arc location were observed with the directions of the arc-associated flows corresponding to a converging electric field. For the event of 9 December 2007, two arcs were poleward progressing almost along the central beams of the Inuvik radar at the speed close to the E×B drift of the bulk of the F-region plasma as inferred from HF Doppler velocities and from independent measurements by the Resolute Bay ionosonde. In global-scale convection maps inferred from all Super Dual Auroral Radar Network (SuperDARN) radar measurements, the polar cap arcs were often seen close to the reversal line of additional mesoscale convection cells located poleward of the normal cells related to the auroral oval. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Geographer is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)