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Spring-breakup ice-jam floods are a major hazard for many rivers in cold regions. They can cause severe damage to infrastructure, property, and ecosystems along riverbanks. To reduce the risk and impact of these events, it is essential to develop reliable and timely forecasting systems that can provide early warning and guidance for mitigation actions. In this paper, we highlight the elements and processes required for the successful development of a spring-breakup ice-jam flood forecasting system, using the lower Churchill River in Labrador, Canada as a case study. We review the existing forecasting methodologies and systems for spring-breakup ice-jam floods and discuss their strengths and limitations. We then describe the case study of the lower Churchill River, where a large ice-jam flood occurred in May 2017, triggering an independent review and a series of recommendations for improving the flood preparedness and response. We present the main components and features of the forecasting system that was developed for the lower Churchill River, based on the recommendations from the independent review. We also discuss the improvements that were made to the forecasting system, such as parallelization, adaptation, and determination of ice-jam prone areas. Finally, we provide some conclusions and recommendations for future research and development of spring-breakup ice-jam flood forecasting systems, focusing on the requirements for a technical framework that incorporates community engagement and special considerations for regulated rivers. [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]

The Thompson River valley is one of the most important transportation corridors in western Canada as it hosts two important railways. This valley has experienced several historical landslide events, many of them along a 10 km section south of the town of Ashcroft. Six of these landslides, showing varying states of activity, were selected for analysis in this paper, as these have the potential for the biggest impact on the railways. The subsurface interpretation of these landslides is combined with satellite InSAR data from May 2015 to May 2017 to enhance the current understanding of the landslide kinematics. Two InSAR orientations are combined geometrically with the assumption that the horizontal component of landslide movement is parallel to the slope azimuth, which provides a practicable approach to approximate landslide displacement vectors. The results classify these landslides as very slow-moving. The maximum velocities recorded are 29, 35, 26, 64, 18, and 52 mm/year for the Goddard, North, South, South extension, Barnard, and Redhill landslides, respectively. All landslides except the Redhill landslide show near-horizontal movements near the toe, with increasing vertical components as measurements approach the back scarp. This confirms that kinematics include rotational and compound mechanisms. [ABSTRACT FROM AUTHOR]

Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. In Canada, a 2008 user survey indicated that the most practical land cover data is provided in a nationwide 30 m spatial resolution format, with an update frequency of five years. In response to this need, the Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada for the base year 2010, as the first of a planned series of maps to be updated every five years, or more frequently. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2010 Land Cover Map of North America, which is produced by Mexican, American and Canadian government institutions under a collaboration called the North American Land Change Monitoring System (NALCMS). This paper describes the mapping approach used for generating this land cover dataset for Canada from Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) Landsat sensor observations. The innovative part of the mapping approach is the local optimization of the land cover classifier, which has resulted in increased spatial consistency and accuracy. Training and classifying with locally confined reference samples over a large number of partially overlapping areas (i.e., moving windows) ensures the optimization of the classifier to a local land cover distribution, and decreases the negative effect of signature extension. A weighted combination of labels, which is determined by the classifier in overlapping windows, defines the final label for each pixel. Since the approach requires extensive computation, it has been developed and deployed using the Government of Canada's High-Performance Computing Center (HPC). An accuracy assessment based on 2811 randomly distributed samples shows that land cover data produced with this new approach has achieved 76.60% accuracy with no marked spatial disparities. [ABSTRACT FROM AUTHOR]

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant influence on the global climate over geological timescales. Recent shifts in climate patterns and intensified human–forest interactions have led to an increase in the incidence of wildfires. These fires are characterized by their extensive coverage, higher frequency, and prolonged duration, rendering them increasingly destructive. To mitigate the impact of wildfires on climate change, ecosystems, and biodiversity, it is imperative to conduct systematic monitoring of wildfire progression and evaluate their environmental repercussions on a global scale. Satellite remote sensing is a powerful tool, offering precise and timely data on terrestrial changes, and has been extensively utilized for wildfire identification, tracking, and impact assessment at both local and regional levels. The Canada Centre for Mapping and Earth Observation, in collaboration with the Canadian Forest Service, has developed a comprehensive National Burned Area Composite (NBAC). This composite serves as a benchmark for curating a bi-temporal multi-source satellite image dataset for change detection, compiled from the archives of Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2. To our knowledge, this dataset is the inaugural large-scale, multi-source, and multi-frequency satellite image dataset with 20 m spatial resolution for wildfire mapping, monitoring, and evaluation. It harbors significant potential for enhancing wildfire management strategies, building upon the profound advancements in deep learning that have contributed to the field of remote sensing. Based on our curated dataset, which encompasses major wildfire events in Canada, we conducted a systematic evaluation of the capability of multi-source satellite earth observation data in identifying wildfire-burned areas using statistical analysis and deep learning. Our analysis compares the difference between burned and unburned areas using post-event observation solely or bi-temporal (pre- and post-event) observations across diverse land cover types. We demonstrate that optical satellite data yield higher separability than C-Band and L-Band Synthetic Aperture Radar (SAR), which exhibit considerable overlap in burned and unburned sample distribution, as evidenced by SAR-based boxplots. With U-Net, we further explore how different input channels influence the detection accuracy. Our findings reveal that deep neural networks enhance SAR's performance in mapping burned areas. Notably, C-Band SAR shows a higher dependency on pre-event data than L-Band SAR for effective detection. A comparative analysis of U-Net and its variants indicates that U-Net works best with single-sensor data, while the late fusion architecture marginally surpasses others in the fusion of optical and SAR data. Accuracy across sensors is highest in closed forests, with sequentially lower performance in open forests, shrubs, and grasslands. Future work will extend the data from both spatial and temporal dimensions to encompass varied vegetation types and climate zones, furthering our understanding of multi-source and multi-frequency satellite remote sensing capabilities in wildfire detection and monitoring. [ABSTRACT FROM AUTHOR]

Spatial and temporal estimates of burned areas are often used to model greenhouse gas and air pollutant emissions from fire events that occur in a region of interest and over specified time frames. However, fire behaviour, fuel consumption, fire severity, and ecological effects vary over both time and space when a fire grows across varying fuels and topography under different environmental conditions. We developed a method for estimating the progression of individual wildfires (i.e., day-of-burn) employing ordinary kriging of a combination of different satellite-based active fire detection data sources. We compared kriging results obtained using active fire detection products from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and combined MODIS and VIIRS data to study how inferences about a wildfire's evolution vary among data sources. A quasi-validation procedure using combined MODIS and VIIRS active fire detection products that we applied to an independent data set of 37 wildfires that occurred in the boreal forest region of the province of Ontario, Canada, resulted in nearly half of each fire's burned area being accurately estimated to within one day of when it actually burned. Our results demonstrate the strengths and limitations of this geospatial interpolation approach to mapping the progression of individual wildfires in the boreal forest region of Canada. Our study findings highlight the need for future validations to account for the presence of spatial autocorrelation, a pervasive issue in ecology that is often neglected in day-of-burn analyses. [ABSTRACT FROM AUTHOR]

Organic material, including total and dissolved organic carbon (DOC), is ubiquitous within aquatic ecosystems, playing a variety of important and diverse biogeochemical and ecological roles. Determining how land-use changes affect DOC concentrations and bioavailability within aquatic ecosystems is an important means of evaluating the effects on ecological productivity and biogeochemical cycling. This paper presents a methodology case study looking at the deployment of a submersible UV-Vis absorbance spectrophotometer (UV-Vis spectro::lyzer model, s::can, Vienna, Austria) to determine stream organic carbon dynamics within a headwater catchment located near Campbell River (British Columbia, Canada). Field-based absorbance measurements of DOC were made before and after forest harvest, highlighting the advantages of high temporal resolution compared to traditional grab sampling and laboratory measurements. Details of remote deployment are described. High-frequency DOC data is explored by resampling the 30 min time series with a range of resampling time intervals (from daily to weekly time steps). DOC export was calculated for three months from the post-harvest data and resampled time series, showing that sampling frequency has a profound effect on total DOC export. DOC exports derived from weekly measurements were found to underestimate export by as much as 30% compared to DOC export calculated from high-frequency data. Additionally, the importance of the ability to remotely monitor the system through a recently deployed wireless connection is emphasized by examining causes of prior data losses, and how such losses may be prevented through the ability to react when environmental or power disturbances cause system interruption and data loss. [ABSTRACT FROM AUTHOR]

The Saskatchewan River Basin (SRB) of central Canada plays a crucial role in the Canadian Prairies. Yet, climate change and human action constitute a real threat to its hydrological processes. This study aims to evaluate and analyze groundwater spatial and temporal dynamics in the SRB. Groundwater information was derived and compared using two different approaches: (1) a mathematical modeling framework coupling the Soil and Water Assessment Tool (SWAT) and the Modular hydrologic model (MODFLOW) and (2) gravimetric satellite observations from the Gravity Recovery and Climate Experiment (GRACE) mission and its follow-on (GRACE-FO). Both methods show generalized groundwater depletion in the SRB that can reach −1 m during the study period (2002–2019). Maximum depletion appeared especially after 2011. The water balance simulated by SWAT-MODFLOW showed that SRB could be compartmented roughly into three main zones. The mountainous area in the extreme west of the basin is the first zone, which is the most dynamic zone in terms of recharge, reaching +0.5 m. The second zone is the central area, where agricultural and industrial activities predominate, as well as potable water supplies. This zone is the least rechargeable and most intensively exploited area, with depletion ranging from +0.2 to −0.4 m during the 2002 to 2011 period and up to −1 m from 2011 to 2019. Finally, the third zone is the northern area that is dominated by boreal forest. Here, exploitation is average, but the soil does not demonstrate significant storage power. Briefly, the main contribution of this research is the quantification of groundwater depletion in the large basin of the SRB using two different methods: process-oriented and satellite-oriented methods. The next step of this research work will focus on the development of artificial intelligence approaches to estimate groundwater depletion from a combination of GRACE/GRACE-FO and a set of multisource remote sensing data. [ABSTRACT FROM AUTHOR]

Thermal refuges are becoming increasingly influential for dictating the population status and spatial distribution of cold-water stenotherm salmonids in the mid- to southern extent of their range. The global climate is predicted to continue to warm, and therefore, the overall thermal suitability of freshwater habitats for stream salmonids is predicted to decline in concert. However, stream and river thermal heterogeneity will offer considerable resiliency for these populations. Thermal refuges are formed by many physical processes; common natural refuges include cold tributary plumes, groundwater springs, alcoves, and hyporheic upwellings. However, many anthropogenically formed refuges (such as stratified reservoirs or cold-water tailrace outflows) also exist in hydropower-regulated rivers. The significance of these refuges to stream salmonids depends on their size and temperature differential, but also other habitat characteristics such as their depth, flow velocity, Froude number, and many biotic factors within the refuges. Modern technologies such as drone-mounted thermal infrared cameras and other remote sensing techniques allow for the efficient identification of such refuges, and inexpensive options include the identification of refuges during ice cover using orthophotographs. Behavioural thermoregulation, i.e., salmonids aggregating in cold-water refuges, can be either facultative or obligate and the timing of these events is governed by life stage, species, and population-specific physiologically regulated cumulative thresholds that are inherently related to the recent thermal history, or hysteresis, of each individual. Salmonids appear to have an excellent spatial cognition for locating and relocating cold-water refuges, and their spatial distribution is largely affected by the availability of the cold-water refuges during the warm-water period in many thermally stressed rivers. Gregarious behaviour is the norm for salmonid fishes within the thermal refuges; however, the size/microhabitat hierarchy appears to dictate the within-refuge distribution at the micro-scale. There continues to be a great impetus for protecting—and in carefully determined cases creating—cold-water refuges in the future. A thorough understanding of what a "goldilocks" refuge is for various salmonids and their different life stages will be imperative as cold-water restoration is gaining popularity. Finally, disentangling the roles of the climate-induced and landscape activity-induced warming potential of fluvial freshwater will be important to ensure continued environmentally responsible landscape activities in future waterscapes. [ABSTRACT FROM AUTHOR]

This study examines the response of a cold-regions deltaic wetland ecosystem in northwestern Canada to two separate and differing seasonal wetting cycles. The goal of this paper was to examine the nature of reflected electromagnetic energy measured by earth observation (EO) satellites, and to assess whether seasonal wetland hydroperiod and episodic flooding events impact the information retrieved by the Sentinel-2 sensors. The year 2018 represents a year characterized by a large spring freshet and ice-jam flooding, while 2019 represents a year characterized more by summer open-water flooding. We applied the Modified Normalized Difference Wetness Index (MNDWI) to address the effects of the wetting cycles. The response of the vegetative cover was tracked using the fraction of the absorbed photosynthetically active radiation (fAPAR) and the Leaf Area Index (LAI). All three indices were viewed through the lens of cover classes as derived through a previously published study by the authors. The study provides a framework for designing longer-term studies where multiple intra- and inter-annual hydrological cycles can be accessed via EO data. Future studies will enable the examination of lag times inherent in the response to the various water sources applied to spectral response and incorporate this EO approach into a monitoring framework. [ABSTRACT FROM AUTHOR]

The Thompson River valley hosts 14 landslides along a 10 km section, which threaten the two major railroads connecting the Port of Vancouver and the interior provinces in Canada. The Ripley landslide is one of the active landslides in this section of the valley. Previous research at this site included an analysis of landslide deformations using satellite radar interferometry focusing on deformations measured in the line of sight between the satellite and the slopes, and average downslope displacement (deformations projected in the average downslope direction). Since then, further stratigraphic interpretation has provided an enhanced understanding of the Ripley landslide. In this update, the new stratigraphic interpretation is supplemented with satellite InSAR data from May 2015 to May 2017 to enhance the current understanding of the landslide kinematics. The results indicate that the Ripley landslide has been moving at a rate between 2 and 82 mm per year, corresponding to a very slow to slow landslide. It is also observed that the movements tend to be near-horizontal on areas closer to the toe of the landslide, while the vertical component of deformation increases near the scarp of the landslide. This, together with the interpreted stratigraphy, indicates the kinematics corresponds to a compound landslide. This is consistent with interpreted landslide kinematics of older, more mature landslides in the area that have shown episodes of retrogression and suggests the possibility of a similar future behaviour of the Ripley landslide. [ABSTRACT FROM AUTHOR]

The lowest elevation of spring snow ("snowline") is an important factor influencing recruitment and survival of wildlife in alpine areas. In this study, we assessed the spatial and temporal variability of alpine spring snowline across major Dall sheep mountain areas in Alaska and northwestern Canada. We used a daily MODIS snow fraction product to estimate the last day of 2000-2016 spring snow for each 500-m pixel within 28 mountain areas. We then developed annual (2000-2016) regression models predicting the elevation of alpine snowline during mid-May for each mountain area. MODIS-based regression estimates were compared with estimates derived using a Normalized Difference Snow Index from Landsat-8 Operational Land Imager (OLI) surface reflectance data. We also used 2000-2009 decadal climate grids to estimate total winter precipitation and mean May temperature for each of the 28 mountain areas. Based on our MODIS regression models, the 2000-2016 mean 15 May snowline elevation ranged from 339 m in the cold arctic class to 1145 m in the interior mountain class. Spring snowline estimates from MODIS and Landsat OLI were similar, with a mean absolute error of 106 m. Spring snowline elevation was significantly related to mean May temperature and total winter precipitation. The late spring of 2013 may have impacted some sheep populations, especially in the cold arctic mountain areas which were snow-covered in mid-May, while some interior mountain areas had mid-May snowlines exceeding 1000 m elevation. We found this regional (>500,000 km²) remote sensing application useful for determining the inter-annual and regional variability of spring alpine snowline among 28 mountain areas. [ABSTRACT FROM AUTHOR]

The winter regime of river-ice covers in high northern latitude regions is often a determining factor in the management of water resources, conservation of aquatic ecosystems and preservation of traditional and cultural lifestyles of local peoples. As ground-based monitoring of river-ice regimes in high northern latitudes is expensive and restricted to a few locations due to limited accessibility to most places along rivers from shorelines, remote sensing techniques are a suitable approach for monitoring. This study developed a RADARSAT-2 based method to monitor the spatio-temporal variation of ice covers, as well as ice types during the freeze-up period, along the main channel of the Slave River Delta in the Northwest Territories of Canada. The spatio-temporal variation of ice covers along the river was analyzed using the backscatter-based coefficient of variation (CV) in the 2013-2014 and 2014-2015 winters. As a consequence of weather and flow conditions, the ice cover in the 2013-2014 winter had the higher variation than the 2014-2015 winter, particularly in the potential areas of flooded/cracked ice covers. The river sections near active channels (e.g., Middle Channel and Nagle Channel), Big Eddy, and Great Slave Lake also yielded higher intra-annual variation of ice cover characteristics during the winters. With the inclusion of backscatter and texture analysis from RADARSAT-2 data, four water and ice cover classes consisting of open water, thermal ice, juxtaposed ice, and consolidated ice, were discriminated in the images acquired between November and March in both the studied winters. In addition to river geomorphology and climatic conditions such as river width, sinuosity or air temperature, the fluctuation of water flows during the winter has a significant impact on the variation of ice cover as well as the formation of different ice types in the Slave River. The RADARSAT-2 based monitoring algorithm can also be applied to other river systems in high latitude ecosystems to annually monitor their river-ice variation and formation during the freeze-up and ice cover progression period. [ABSTRACT FROM AUTHOR]

Spectral vegetation indices have been the primary resources for characterizing grassland vegetation based on remotely sensed data. However, the use of spectral indices for vegetation characterization in grasslands has been challenged by the confounding effects from external factors, such as soil properties, dead materials, and shadowing of vegetation canopies. Dead materials refer to the dead component of vegetation, including fallen litter and standing dead grasses accumulated from previous years. The abundant dead materials have been presenting challenges to accurately estimate green vegetation using spectral vegetation indices (VIs) derived from remote sensing data in mixed grasslands. Therefore, a close investigation of the relationship between VIs and dead materials is needed. The identified relationships could provide better insight into not only using remote sensing data for quantitative estimation of dead materials, but also the improvement of green vegetation estimation in the mixed grassland that has a high proportion of dead materials. In this article, the spectral reflectance of dead materials and green vegetation mixtures and dead material cover were measured in mixed grasslands located in Grassland National Park (GNP), Saskatchewan, Canada. Nine VIs were derived from the measured spectral reflectance. The relationship between dead material cover and VIs was quantified using the regression model and sensitivity analysis. Results indicated that the relationship between dead material cover and VIs is a function of the amount of dead material cover. Weak positive relationship was found between VIs and dead materials where the cover was less than 50%, and a significant high negative relationship was evident when cover was greater than 50%. When the combined exponential and linear model was applied to fit the negative relationships, more than 90% variation in dead material cover could be explained by VIs. Sensitivity analysis was further applied to the developed models, indicating that sensitivities of all VIs were significant over the entire range of dead material cover except for the triangular vegetation index (TVI), which has insignificant sensitivity when dead material cover was greater than 94%. Among all VIs, the weighted difference vegetation index (WDVI) had the highest sensitivity to changes in dead material cover higher than 50%. The results from this study indicated that vegetation indices based on combination of reflectance in red and NIR bands can be used to estimate dead material cover that is greater than 50%. [ABSTRACT FROM AUTHOR]

Since 2002, global snow water equivalent (SWE) estimates have been generated using Advanced Microwave Scanning Radiometer (AMSR-E)/Aqua data. Accurate estimates of SWE are important to improve monitoring and managing of water resources in specific regions. SWE and snow map product accuracy are functions of topography and of land cover type because landscape characteristics have a strong influence on redistribution and physical properties of snow cover, and influence the microwave properties of the surface. Here we evaluate the AMSR-E SWE and derived snow map products in the Mackenzie River Basin (MRB), Canada, which is characterized by complex topography and varying land cover types from tundra to boreal forest. We compare in situ snow depth observations and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover maps from January 2003 to December 2007 with passive microwave remotely sensed SWE from AMSR-E and derived snow cover maps. In the MRB the mean absolute error ranges from 12 mm in the early winter season to 50 mm in the late winter season and overestimations of snow cover maps based on a 1 mm threshold of AMSR-E SWE varies from 4% to 8%. The optimal threshold for AMSR-E SWE to classify the pixels as snow ranges from 6 mm to 9 mm. The overall accuracy of new snow cover maps from AMSR-E varies from 91% to 94% in different sub-basins in the MRB. [ABSTRACT FROM AUTHOR]

Wildland fires and anthropogenic disturbances can cause changes in vegetation species composition and structure in boreal peatlands. These could potentially alter regeneration trajectories following severe fire or through cumulative impacts of climate-mediated drying, fire, and/or anthropogenic disturbance. We used lidar-derived point cloud metrics, and site-specific locational attributes to assess trajectories of post-disturbance vegetation regeneration in boreal peatlands south of Fort McMurray, Alberta, Canada using a space-for-time-chronosequence. The objectives were to (a) develop methods to identify conifer trees vs. deciduous shrubs and trees using multi-spectral lidar data, (b) quantify the proportional coverage of shrubs and trees to determine environmental conditions driving shrub regeneration, and (c) determine the spatial variations in shrub and tree heights as an indicator of cumulative growth since the fire. The results show that the use of lidar-derived structural metrics predicted areas of deciduous shrub establishment (92% accuracy) and classification of deciduous and conifer trees (71% accuracy). Burned bogs and fens were more prone to shrub regeneration up to and including 38 years after the fire. The transition from deciduous to conifer trees occurred approximately 30 years post-fire. These results improve the understanding of environmental conditions that are sensitive to disturbance and impacts of disturbance on northern peatlands within a changing climate. [ABSTRACT FROM AUTHOR]

Snowfall affects the terrestrial climate system at high latitudes through its impacts on local meteorology, freshwater resources and energy balance. Precise snowfall monitoring is essential for cold countries such as Canada, and particularly in temperature-sensitive regions such as the Arctic; however, its size and remote location means the precipitation gauge network there is sparse. While satellite remote sensing of snowfall from instruments such as CloudSat-CPR offers a potential solution, satellite detection of precipitation phase has not been systematically evaluated across Canada. In this study, CloudSat-based precipitation occurrence and phase retrievals were validated at 26 stations across Canada maintained by Environment and Climate Change Canada (ECCC). Probability of Detection (POD), defined as the percentage agreement between coincident CloudSat and human-observed present weather information for precipitation (solid, liquid or no precipitation), and False Alarm Ratio (FAR) were used as the primary metrics for validation. The mean POD (FAR) for precipitation occurrence across Canada is 65.5% ± 4.3 (31.4% ± 5.1) and for no precipitation is 90.6% ± 1.4 (11% ± 2.5). The results show lower rates of detection under cloudier skies, in the presence of (freezing) drizzle and for lighter snowfall, which may be explained by a large number of false-positives due to CloudSat-CPR's high instrumental sensitivity. When CloudSat correctly detects the occurrence of precipitation, it shows uniformly high POD (>80%) and low FAR (<10%) for classifying the phase of precipitation. Large databases of coincident ground and satellite measurements allow us to provide a new estimate of around 9% for the frequency of virga events, a factor of two smaller than a previous estimate for the Arctic. The results from this study show that CloudSat has useful accuracy in detecting precipitation occurrence and very high accuracy at classifying precipitation phase, over diverse climate zones across Canada. As such, there is significant potential for satellite monitoring of snowfall in remote, cold regions. [ABSTRACT FROM AUTHOR]

In this follow-on study on aboveground biomass of shrubs and short-stature trees, we provide plant component aboveground biomass (herein 'AGB') as well as plant component AGB allometric models for five common boreal shrub and four common boreal short-stature tree genera/species. The analyzed plant components consist of stem, branch, and leaf organs. We found similar ratios of component biomass to total AGB for stems, branches, and leaves amongst shrubs and deciduous tree genera/species across the southern Northwest Territories, while the evergreen Picea genus differed in the biomass allocation to aboveground plant organs compared to the deciduous genera/species. Shrub component AGB allometric models were derived using the three-dimensional variable volume as predictor, determined as the sum of line-intercept cover, upper foliage width, and maximum height above ground. Tree component AGB was modeled using the cross-sectional area of the stem diameter as predictor variable, measured at 0.30 m along the stem length. For shrub component AGB, we achieved better model fits for stem biomass (60.33 g ≤ RMSE ≤ 163.59 g; 0.651 ≤ R2 ≤ 0.885) compared to leaf biomass (12.62 g ≤ RMSE ≤ 35.04 g; 0.380 ≤ R2 ≤ 0.735), as has been reported by others. For short-stature trees, leaf biomass predictions resulted in similar model fits (18.21 g ≤ RMSE ≤ 70.0 g; 0.702 ≤ R2 ≤ 0.882) compared to branch biomass (6.88 g ≤ RMSE ≤ 45.08 g; 0.736 ≤ R2 ≤ 0.923) and only slightly better model fits for stem biomass (30.87 g ≤ RMSE ≤ 11.72 g; 0.887 ≤ R2 ≤ 0.960), which suggests that leaf AGB of short-stature trees (<4.5 m) can be more accurately predicted using cross-sectional area as opposed to diameter at breast height for tall-stature trees. Our multi-species shrub and short-stature tree allometric models showed promising results for predicting plant component AGB, which can be utilized for remote sensing applications where plant functional types cannot always be distinguished. This study provides critical information on plant AGB allocation as well as component AGB modeling, required for understanding boreal AGB and aboveground carbon pools within the dynamic and rapidly changing Taiga Plains and Taiga Shield ecozones. In addition, the structural information and component AGB equations are important for integrating shrubs and short-stature tree AGB into carbon accounting strategies in order to improve our understanding of the rapidly changing boreal ecosystem function. [ABSTRACT FROM AUTHOR]

Since 1989, the COMET® Program's staff of instructional designers, scientists, graphic artists, and web developers has been creating targeted, effective, and scientifically sound instructional materials for the geosciences in multiple languages and formats. The majority of COMET training materials and services are available via COMET's online training portal, MetEd. MetEd hosts over 500 self-paced English-language lessons, which are freely available to registered users. The lessons cover a broad array of topics, including satellite meteorology, numerical weather prediction, hydrometeorology, oceanography, aviation weather, climate science, and decision support. Nearly 300 lessons have been translated to other languages. NOAA NESDIS, EUMETSAT, the Meteorological Service of Canada, and the US National Weather Service all provide funding and subject matter expertise for satellite training efforts at COMET. The COMET team is focused on helping our sponsors refine their learning objectives and produce instructional material that is focused on learner engagement, knowledge retention, and measurable performance improvement. The COMET Program has continually transformed its instructional approach to better meet the shifting needs of learners. Our satellite remote sensing educational and training materials provide sound foundational knowledge for existing and new satellite products paired with increasing opportunities to apply that knowledge. [ABSTRACT FROM AUTHOR]

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral RapidEye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in Canada. In addition, the classification results obtained from deep CNNs are compared with those based on conventional machine learning tools, including Random Forest and Support Vector Machine, to further evaluate the efficiency of the former to classify wetlands. The results illustrate that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, ResNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies of 96.17%, 94.81%, and 93.57%, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are 74.89% and 76.08%, respectively, considerably inferior relative to CNNs. Importantly, InceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands. [ABSTRACT FROM AUTHOR]