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Airborne sensor image texture derived following a geostatistical analysis can increase the accuracy of forest classification because the resulting texture is insensitive to random variations in spectral response but related to the structural features of interest at the scale of a forest inventory (e.g. tree species). The combination of spectral and textural data derived from a kriging surface provided 86% classification accuracy in 36 pure and mixed-wood stands in seven forest classes in Alberta. This is an increase over the classification accuracy obtained when texture was derived from the original image data, and when the spectral response patterns were used alone. [ABSTRACT FROM AUTHOR]

Lichen mapping is vital for caribou management plans and sustainable land conservation. Previous studies have used random forest, dense neural network, and convolutional neural network models for mapping lichen coverage. However, to date, it is not clear how these models rank in this task. In this study, these machine learning models were evaluated on their ability to predict lichen percent coverage in Sentinel-2 imagery in Québec and Labrador, Canada. The models were trained on 10-m resolution lichen coverage (%) maps created from 20 drone surveys collected in July 2019 and 2022. The dense neural network achieved a higher accuracy than the other two, with a reported mean absolute error of 5.2% and an R2 of 0.76. By comparison, the random forest model returned a mean absolute error of 5.5% (R2: 0.74) and the convolutional neural network had a mean absolute error of 5.3% (R2: 0.74). A regional lichen map was created using the trained dense neural network and a Sentinel-2 imagery mosaic. There was greater uncertainty on land covers that the model was not exposed to in training, such as mines and deep lakes. While the dense neural network requires more computational effort to train than a random forest model, the 5.9% performance gain in the test pixel comparison renders it the most suitable for lichen mapping. This study represents progress toward determining the appropriate methodology for generating accurate lichen maps from satellite imagery for caribou conservation and sustainable land management. [ABSTRACT FROM AUTHOR]

Supraglacial drainage networks play an integral role in both glacier dynamics and run-off timing, and mapping them provides insight into their role in glacial systems. Here we present a reproducible approach for semi-automated mapping of supraglacial hydrologic features, which complements existing work in automated and manual mapping by providing clear definitions for identification of features. This framework uses a digital terrain model (DTM) to identify potential flow routes on the glacier surface, which are then classified using a set of standardized rules based on the DTM and an orthomosaic. We found that the normalized difference water index calculated from digital imagery was influenced by image brightness and introduce a new approach using average RGB values to correct for this. Using this framework we mapped supraglacial drainage networks at Nàłùdäy and Thores Glacier, Canada. The framework was easier to implement with high-resolution (0.5 m) imagery and DTMs, compared to data with lower resolution (10 m), due to the increased detail in topography and feature boundaries at high-resolution. Lower-resolution data captured larger streams (>2 pixels wide), however, indicating that the framework can still be used at this resolution. Mapping supraglacial hydrology using standardized methods opens possibilities for investigating many questions relating to changes in supraglacial hydrology over time. [ABSTRACT FROM AUTHOR]

Background: Texture analysis (TA) is an image-analysis technique that detects complex intervoxel statistical patterns. 3D TA has shown potential in detecting cerebral degeneration not perceptible to the human eye in many neurological disorders. The reliability of this method's application in a multicenter study is unknown., Purpose: To assess the intrasite and intersite reliability of a novel 3D TA method from data acquired systematically from the Canadian ALS Neuroimaging Consortium (CALSNIC)., Study Type: Prospective multicenter data with harmonized MR sequence parameters acquired from five sites., Population: Six healthy subjects., Field Strength: 3T 3D-MPRAGE and 3D-SPGR T 1 -weighted MRI of the brain., Assessment: Voxel-based 3D TA was performed on the whole brain to produce texture maps., Statistical Tests: Intra- and intersite reliability of texture features was assessed using a two-way mixed-effects model for intraclass correlation coefficients (ICC). ICCs were calculated in a region-of-interest (ROI) analysis of predetermined anatomically relevant areas. A voxelwise approach was used to assess the whole brain., Results: In the ROI analyses, intrasite reliability was excellent (ICC > 0.75) across most regions and texture features (autocorrelation [autoc], contrast [contr], energy [energ]). Intersite reliability was excellent for most regions with autoc, ranging from fair to excellent for contr, and ICCs ranging from poor to good (<0.40-0.75) for energ. Voxelwise analyses revealed a large range in ICC across the brain for both intrasite and intersite ICCs (0.0-0.90), with higher reliability in the cortical gray matter compared with deeper subcortical structures., Data Conclusion: Overall, the reliability of 3D TA was highly dependent on texture feature, region studied, and method of analysis (ROI or voxelwise). Intrasite reproducibility was good to excellent, and better than intersite. ROI-based analyses present higher reliability in comparison with voxelwise analyses. Autoc has overall excellent reliability. These factors might be considered when designing future 3D TA studies., Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1200-1209., (© 2019 International Society for Magnetic Resonance in Medicine.)

Although research with digital airborne remote sensing data has been undertaken in different ecoregions to classify forested areas, the potential role of such imagery in deriving information to assist forest management has not yet been fully defined. The objective of this study was to determine the extent that the addition of texture could improve spectral classification of high spatial resolution images (pixel size 1m). These images represented pure and mixed wood forest stands from ecoregions in Alberta and New Brunswick, Canada. This study employed a judicious, selective application of texture to stands within a hierarchical classification framework. In Alberta, the addition of texture made a modest improvement in classification accuracy from 60% to 65%. In New Brunswick, the application of texture to selected land cover types resulted in an overall 12% improvement in classification accuracy. The addition of image texture increased classification accuracy for high spatial detail imagery relative to low spatial detail imagery. Incorporating texture into classification also improved classification accuracies for hardwood stands more so than for softwood stands, but greater attention to stand structure and composition will be needed in future work. Classification accuracies on the order of 60-65% were achieved with simple texture derivatives, maximum likelihood decision rules and conventional classification methods. [ABSTRACT FROM AUTHOR]

A new technique for automated retrieval of ice concentration from RADARSAT-2 dual-polarization HH-HV ScanSAR Wide images for subsequent assimilation in ice numerical models is presented. First, we extended our previously introduced ice and water detection approach operating at a 2.05 km $\times2.05$ km spatial scale to a set of 19 different spatial scales ranging from 2.05 km (41 pixels) down to 0.25 km (5 pixels). As the spatial resolution was increased, the overall accuracy of ice and water detection stayed at a very high level across all scales (between 99.5% and 99.8%), but the number of water retrievals substantially dropped. Second, we designed an approach for estimating ice concentration in a 2 km $\times 2$ km ($40\times40$ pixels) area consisting of $64\,\,5 \times 5$ pixel blocks. The $5\times 5$ pixel blocks which are initially classified as unknowns are iteratively combined in clusters with effective spatial scales larger than 5 pixels. The clusters are further classified as ice or water using the ice probability model corresponding to the effective spatial scale. The $40\times40$ pixel area becomes populated with high-resolution ($5\times 5$ pixels) ice and water retrievals, and the ice concentration is estimated based on the number of ice and water retrievals. The proposed approach produces a much better agreement with the Canadian Ice Service Image Analysis ice concentrations (root-mean-square error (RMSE) = 2.2%) compared to the original 2-km ice/water detection approach (RMSE = 19.9%). The developed technique will be adapted to the RADARSAT Constellation Mission data for data assimilation in Environment and Climate Change Canada Regional Ice-Ocean Prediction System. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature 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.)

Environmental factors controlling the distribution and abundance of boreal avifauna are not fully understood, limiting our ability to predict the consequences of a changing climate and industrial development activities underway. We used a compilation of avian point-count data, collected over 1990-2008 from nearly 36 000 locations, to model the abundance of individual forest songbird species within the Canadian boreal forest. We evaluated 30 vegetation and 101 climatic variables, representing most of the widely-used dimensions of climate space, along with less usual measures of inter-annual variability. Regression tree models allowed us to calculate the relative importance of climate and vegetation variable classes according to avian migration strategy without the need for a priori variable selection or dimension reduction. We tested for hierarchical habitat selection by formulating hypotheses on the locations of variables within the model tree structures. Climate variables explained the majority (77%) of deviance explained over 98 species modelled. As may be expected at high latitudes, we found energy availability (temperature, 65%) to be more important than moisture availability (precipitation, 12%). The contributions of inter- and intra-annual climate variability (28%) were about half that of mean conditions. The relatively large contribution of remotely-sensed vegetation metrics (23%) highlighted the importance of local vegetation heterogeneity controlled by non-climatic factors. The two most important vegetation variables were landcover type and April leaf area index. When selected, these generally occurred in a model's right subtree, consistent with predictions from hierarchical habitat selection theory. When occupying the root node, landcover effectively delineated the historical forest-prairie ecotone, reflecting the current disequilibrium between climate and vegetation due to human land use. Our findings suggest a large potential for avian distributional shifts in response to climate change, but also demonstrate the importance of finer scale vegetation heterogeneity in the spatial distribution of boreal birds. [ABSTRACT FROM AUTHOR]

British Columbia (BC), Canada, has a diverse landscape that provides breeding habitat for >300 avian species, and the recent development of the BC Breeding Bird Atlas data set presents key information for exploring the landscape conditions which lead to biological richness. We used the volunteer-collected raw breeding bird evidence data set to analyze the effects of sampling biases on spatial distribution of observed breeding bird species and implemented regression tree analysis (Random Forests) to examine the influence of productivity, ambient energy, and habitat heterogeneity on independently measured breeding bird richness. Results indicated that total breeding species richness is correlated with total survey effort (a < 0.001). By stratifying species richness by survey effort, we observed that ambient energy is the top-ranked environmental predictor of breeding bird richness across BC, which, when used in combination with a number of other environmental variables, explains ~40% of the variation in richness. Using our modeled relationships, we predicted breeding bird species richness in the areas of BC not presently surveyed between three and six hours. The majority of the productive Boreal Plains, the southern portion of the Taiga Plains region, the lowlands of the Southern and Central Interior, along the Rocky Mountain Trench, and the coastal areas of the Georgia Depression are predicted to have the highest categories of breeding richness (35-57 unique species). Our results support ongoing species diversity gradient research, which identifies ambient energy as an important factor influencing species diversity distributions in the Northern Hemisphere. By linking breeding bird richness to environmental data derived from remotely sensed data and systematically collected climate data, we demonstrate the potential to monitor shifts in ambient energy as a surrogate for vertebrate habitat condition affecting population levels. By analyzing the influence of survey effort on species richness metrics, we also highlight the need to consider adding attributes to the raw breeding bird data set to describe observer experience, such as hours or seasons spent surveying, and provide survey dates to allow greater flexibility for removing survey bias. These additions can increase the utility of atlas data for species richness studies useful for conservation planning. [ABSTRACT FROM AUTHOR]

The population of loggerhead shrike (Lanius ludovicianus excubutirudes) in Grassland National Park of Canada (GNPC) has undergone a severe decline due to habitat loss and limitation. Shrike habitat availability is highly impacted by the biophysical characteristics of grassland landscapes. This study was conducted in the west block of GNPC. The overall purpose was to extract important biophysical and topographical variables from both SPOT satellite imagery and in situ measurements. Statistical analysis including Analysis of Variance (ANOVA), measuring Coefficient Variation (CV), and regression analysis were applied to these variables obtained from both imagery and in situ measurement. Vegetation spatial variation and heterogeneity among active, inactive and control nesting sites at 20 m × 20 m, 60 m × 60 m and 100 m × 100 m scales were investigated. Results indicated that shrikes prefer to nest in open areas with scattered shrubs, particularly thick or thorny species of smaller size, to discourage mammalian predators. The most important topographical characteristic is that active sites are located far away from roads at higher elevation. Vegetation index was identified as a good indicator of vegetation characteristics for shrike habitats due to its significant relation to most relevant biophysical factors. Spatial variation analysis showed that at all spatial scales, active sites have the lowest vegetation abundance and the highest heterogeneity among the three types of nesting sites. For all shrike habitat types, vegetation abundance decreases with increasing spatial scales while habitat heterogeneity increases with increasing spatial scales. This research also indicated that suitable shrike habitat for GNPC can be mapped using a logistical model with ATSAVI and dead material in shrub canopy as the independent variables. [ABSTRACT FROM AUTHOR]

Large-scale leaf area index (LAI) inversion algorithms were developed to determine the LAI of a forest located in Gatineau Park, Canada, using high-resolution colour and colour infrared (CIR) digital airborne imagery. The algorithms are parameter-independent and developed based on the principles of optical field instruments for gap fraction measurements. Cloud-free colour and CIR images were acquired on 21 August 2007 with 35 and 60 cm nominal ground pixel size, respectively. Normalized Difference Vegetation Index (NDVI), maximum likelihood and object-oriented classifications, and principal component analysis (PCA) methods were applied to calculate the mono-directional gap fraction. Subsequently, LAI was derived from inversion and compared with ground measurements made in 54 plots of 20 by 20 m using hemispherical photography between 10 and 20 August 2007. There was high inter-correlation (the Pearson correlation coefficient, R > 0.5, p < 0.01) among LAI values inverted using the classifications and PCA methods, but neither were highly correlated with LAI inverted from the NDVI method. LAI inverted from the NDVI-based gap fraction significantly correlated with ground-measured LAI (R = 0.63, root mean square error (RMSE) = 0.52), while LAI inverted from the classification and PCA-derived gap fraction showed poor correlation with ground-measured LAI. Consequently, the NDVI method was used to invert LAI for the whole study area and produce a 20-m resolution LAI map. [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.)

This paper synthesizes 10-years' worth of interannual time-series space-borne ERS-1 and RADARSAT-1 synthetic aperture radar (SAR) data collected coincident with daily measurement of snow-covered, land-fast first-year sea ice (FYI) geophysical and surface radiation data collected from the Seasonal Sea Ice Monitoring and Modeling Site, Collaborative-Interdisciplinary Cryospheric Experiment and 1998 North Water Polynya study over the period 1992 to 2002. The objectives are to investigate the seasonal co-relationship of the SAR time-series dataset with selected surface mass (bulk snow thickness) and climate state variables (surface temperature and albedo) measured in situ for the purpose of measuring the interannual variability of sea ice spring melt transitions and validating a time-series SAR methodology for sea ice surface mass and climate state parameter estimation. We begin with a review of the salient processes required for our interpretation of time-series microwave backscatter from land-fast FYI. Our results suggest that time-series SAR data can reliably measure the timing and duration of surface albedo transitions at daily to weekly time-scales and at a spatial scales that are on the order of hundreds of metres. Snow thickness on FYI immediately prior to melt onset explains a statistically significant portion of the variability in timing of SAR-detected melt onset to pond onset for SAR time-series that are made up of more than 25 images. Our results also show that the funicular regime of snowmelt, resolved in time-series SAR data at a temporal resolution of approximately 2.5 images per week, is not detectable for snow covers less than 25 cm in thickness. [ABSTRACT FROM AUTHOR]

Topographic correction based on sun-canopy-sensor (SCS) geometry is more appropriate than terrain-based corrections in forested areas since SCS preserves the geotropic nature of trees (vertical growth) regardless of terrain, view, and illumination angles. However, in some terrain orientations, SCS experiences an overcorrection problem similar to other simple photometric functions. To address this problem, we propose a new SCS+C correction that accounts for diffuse atmospheric irradiance based on the C-correction. A rigorous, comprehensive, and flexible method for independent validation based on canopy geometric optical reflectance models is also introduced as an improvement over previous validation approaches, and forms a secondary contribution of this paper. Results for a full range of slopes, aspects, and crown closures showed SCS+C provided improved corrections compared to the SCS and four other photometric approaches (cosine, C, Minnaert, statistical-empirical) for a Rocky Mountain forest setting in western Canada. It was concluded that SCS+C should be considered for topographic correction of remote sensing imagery in forested terrain. [ABSTRACT FROM AUTHOR]

A large abandoned tailings deposit at a mine site near Timmins, Ontario, Canada has produced significant damage in an adjacent forest due to contamination and wind stress. Significant forest structure changes were measured between 1997 and 1999. A multivariate image-based forest structure index (FSI) was developed using canonical correlation analysis of 1997 field and airborne digital camera data. FSI included decreasing canopy closure and leaf area index, and increasing blown down and standing dead structure measures associated with image spectral, textural and radiometric fraction variables. An image model predicting FSI achieved an R 2 =0.66. The model equation was then applied to 1999 airborne imagery to predict FSI for each plot. Comparing the 1999 image predicted FSI to that calculated from field data showed that the model was strong in predicting positive or no forest structure changes, but not increased structure degradation. The latter was due to the presence of herbaceous and shrub vegetation that had developed during the two-year period in open plots near the tailings where blow down was significant. The next research phase will derive means to separate these two signals in forests of open overstory. [ABSTRACT FROM AUTHOR]

Empirical relationships between forest stand attributes and Landsat-5 Thematic Mapper spectral response were developed in order to assess its informational value in support of forest inventory operations in the Northwest Territories. An existing large-area classification procedure, based on a supervised methodology, has been able to generate classes of white spruce and jack pine. Within individual forest species groups, spectral variability related to differences in crown closure, height and age is of interest to forest managers in the region. The objective of this study was to determine the accuracy with which stands within the white spruce and jack pine classes could be further separated into two stand height classes (<15 m and &ges;15 m), two age classes (<100 years and &ges;100 years) and two crown closure classes (<30% and &ges;30%) with a single (summer) Landsat-5 Thematic Mapper (TM) image. Discrimination generally improved with the addition of spectral texture measures where independently assessed accuracies ranged from 60 to 90%. A look-up table was devised for conifer-dominated areas (>80% dominant species) which could subsequently be assigned for height, age and crown closure class values based on Landsat TM spectral response patterns. [ABSTRACT FROM AUTHOR]

Multisource data consisting of satellite imagery, topographic descriptors derived from DEMs, and GIS inventory information have been used with a detailed, field-based landcover classification scheme to support a quantitative analysis of the spatial distribution and configuration of grizzly bear (Ursus arctos horribilis) habitat within the Alberta Yellowhead Ecosystem study area. The map is needed to determine if bear movement and habitat use patterns are affected by changing landscape conditions and human activities. We compared a multisource Evidential Reasoning (ER) classification algorithm, capable of handling this large and diverse data set, to a more conventional maximum likelihood decision rule which could only use a subset of the available data. The ER classifier provided an acceptable level of accuracy (ranging to 85% over 21 habitat classes) for a level 3 product, compared to 71% using a maximum likelihood classifier. [ABSTRACT FROM AUTHOR]

This Letter presents the results of textural separability tests obtained by first- and second-order texture methods on different aged Douglas-fir stands in IKONOS panchromatic imagery acquired 3 June 2000 over the Sooke River watershed in British Columbia. The effects of different measures and window sizes on the textural separability are discussed. Small windows sizes were not as effective in separating stands as larger windows sizes. Second-order (spatial co-occurrence homogeneity) texture values were the most effective in distinguishing between age classes. A first-order (variance) texture measure, though still useful, provided less separability. [ABSTRACT FROM AUTHOR]

The timing of snowmelt is an important turning point in the seasonal cycle of small Arctic catchments. The TerraSAR-X (TSX) satellite mission is a synthetic aperture radar system (SAR) with high potential to measure the high spatiotemporal variability of snow cover extent (SCE) and fractional snow cover (FSC) on the small catchment scale. We investigate the performance of multi-polarized and multi-pass TSX X-Band SAR data in monitoring SCE and FSC in small Arctic tundra catchments of Qikiqtaruk (Herschel Island) off the Yukon Coast in the Western Canadian Arctic. We applied a threshold based segmentation on ratio images between TSX images with wet snow and a dry snow reference, and tested the performance of two different thresholds. We quantitatively compared TSX- and Landsat 8-derived SCE maps using confusion matrices and analyzed the spatiotemporal dynamics of snowmelt from 2015 to 2017 using TSX, Landsat 8 and in situ time lapse data. Our data showed that the quality of SCE maps from TSX X-Band data is strongly influenced by polarization and to a lesser degree by incidence angle. VH polarized TSX data performed best in deriving SCE when compared to Landsat 8. TSX derived SCE maps from VH polarization detected late lying snow patches that were not detected by Landsat 8. Results of a local assessment of TSX FSC against the in situ data showed that TSX FSC accurately captured the temporal dynamics of different snow melt regimes that were related to topographic characteristics of the studied catchments. Both in situ and TSX FSC showed a longer snowmelt period in a catchment with higher contributions of steep valleys and a shorter snowmelt period in a catchment with higher contributions of upland terrain. Landsat 8 had fundamental data gaps during the snowmelt period in all 3 years due to cloud cover. The results also revealed that by choosing a positive threshold of 1 dB, detection of ice layers due to diurnal temperature variations resulted in a more accurate estimation of snow cover than a negative threshold that detects wet snow alone. We find that TSX X-Band data in VH polarization performs at a comparable quality to Landsat 8 in deriving SCE maps when a positive threshold is used. We conclude that TSX data polarization can be used to accurately monitor snowmelt events at high temporal and spatial resolution, overcoming limitations of Landsat 8, which due to cloud related data gaps generally only indicated the onset and end of snowmelt. [ABSTRACT FROM AUTHOR]

Outbreaks of insect pests periodically cause large losses of volume in Canada’s forests. Compounded with climate change, outbreaks create significant challenges for managing the sustainable delivery of ecosystem services. Current methods to monitor damage by these pests involve both field and aerial surveys. While relatively cost effective and timely, aerial survey consistency and spatial coverage may be insufficient for detailed monitoring across Canada’s vast forest-land base. Remote sensing can augment these methods and extend monitoring capabilities in time and space by incorporating knowledge of pest-host interactions and of how damage translates into a remote sensing signal for detection and mapping. This review provides a brief introduction to major forest insect pests in Canada (two bark beetles (Coleoptera: Curculionidae) and six defoliators) and the damage they cause, a synthesis of the literature involving aerial survey and remote sensing, and a discussion of how these two approaches could be integrated into future pest monitoring from a Canadian perspective. We offer some lessons learned, outline roles that remote sensing could serve in a management context, and discuss what ongoing and upcoming technological advances may offer to future forest health monitoring. [ABSTRACT FROM AUTHOR]