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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]

Airborne laser scanning (ALS) has emerged as a technology capable of generating descriptors of vegetation structure and best available terrain information. Research and operational implementations of ALS data have highlighted their value for characterizing forest structure and generating spatially explicit and objective spatial coverages and mapping products for forest management. Continued emphasis to enhance forest stewardship is promoting novel methods to integrate ALS to detail non-timber ecosystem values like habitat, soil, and water. Standardized criteria and indicator frameworks such as the Canadian Council of Forest Ministers provide a reliable starting point for where ALS has opportunities to characterize ecosystems objectively regardless of location. In this review of primarily Canadian work, we highlight how ALS is becoming an increasingly viable technology for deriving meaningful indicators to meet sustainable forest management criteria. We review and highlight the value of ALS for quantifying indicators of biological diversity, ecosystem condition and productivity, soil and water, and the role of forests in global ecological cycles. We conclude by highlighting the need for increased education, tech transfer, flexible software, and reporting frameworks alongside five key considerations for using ALS to derive meaningful indicators of sustainable forest management. [ABSTRACT FROM AUTHOR]

Forests are dynamic ecosystems, subject to both natural and anthropogenic agents of change. Wildfire, harvesting and other human activities alter the tree-covered area present in forests. From national and international reporting perspectives, forests include areas currently treed, as well as those disturbed forest areas that are not currently treed but will be, given time for regeneration and the advancement of natural successional processes. As a consequence, forest area can be depicted at a particular point in time, informed by a retrospective temporal context. Using time series of Landsat imagery, annual land cover maps can be generated that are informed by knowledge of past disturbance history (such as wildfire and harvesting). In this research, we use over three decades of annual land cover data generated from Landsat time series to generate a spatially explicit estimate of the forest area of Canada in 2010. We demonstrate how land cover and disturbance information can be combined to map the area of 'forest', as defined by the Food and Agricultural Organization of the United Nations (FAO), within Canada's 650 Mha of forested ecozones. Following this approach, we estimated Canada's total forest area in 2010 to be 354.5 Mha. This estimate includes 324.5 Mha of current forest cover in 2010, plus an additional 33.2 Mha (or 9.4 per cent) of temporally informed forest area where tree cover had been temporarily lost due to fire or harvest, less 3.3 Mha that were removed to meet a definitional minimum size (0.5 ha) for contiguous forest area. Using Canada's National Forest Inventory (NFI) as an independent reference source, the spatial agreement between the two estimates of forest area was ~84 per cent overall. Aspatially, the total area of the Landsat-derived estimate of 2010 forest area and the NFI baseline estimates differed by only 3 per cent, with notable regional differences in the wetland-dominated Hudson Plains Ecozone. Satellite-derived time series land cover and change information enable spatially explicit depictions of forest area (distinct from representations of forest cover) in a robust and transparent fashion, producing information of value to science, management and reporting information needs. [ABSTRACT FROM AUTHOR]

The fate of the carbon stored in frozen peatlands in Canada's Hudson Bay Lowlands (HBL) depends strongly on water, with wetter conditions favoring long-term storage. Dynamic surface water products generated from historical Landsat data exist to inform surface water trends in the HBL based on binary classifications of land vs water at 30 m spatial resolution. However, the HBL contains many water features smaller than 30 m, including streams and patterned fens that require a sub-pixel mapping approach to fully capture their dynamics. In this paper, we leverage an existing binary dynamic surface water product to generate a spatially and temporally comprehensive Landsat sub-pixel surface water time-series over the HBL. We first generate Landsat Red, NIR and SWIR composites from 1985 to 2021 and evaluate data-driven machine learning and physical models to derive sub-30 m water fractions across the entire mapping domain. Data-driven models were calibrated on 30 m water fractions obtained from 16 of 17 WorldView scene water fraction maps and assessed on the 17th held-out scene in a jackknife validation. Data-driven models were also calibrated by relating scaled Landsat reflectance to water fractions obtained from binary water masks at corresponding scales from 90 to 270 m. Physical models involved simple linear unmixing using a new approach that samples land endmembers in the vicinity of each pixel to represent land reflectance, and a global water endmember sampled beneath permanent water in each image. The assumption of linear scaling was verified at sub-30 m to 270 m resolution for NIR, SWIR and NDVI, which were all highly correlated with water fraction and had no significant differences among water fraction – spectral feature relationships across the full range of scales. Each of 11 different methods and input spectral features were evaluated against WorldView water fractions and ranked based on nine criteria. We found that machine learning using all spectral features including local NIR land endmembers performed best among data-driven approaches, and linear unmixing of the NIR band using local land endmembers performed best among physical. Both best-performing methods were applied to the entire time-series and surface water area trends were calculated and compared with trends obtained from binary water masks. All three methods agree on the direction of surface water change towards wetting, with sub-pixel inter-annual variation agreeing with stream discharge noted in other studies. • Physical and data-driven Landsat water fraction methods tested in the Hudson Bay Lowlands. • Physical methods perform linear unmixing between local land and global water endmembers. • Data-driven methods consist of machine learning calibrated using high-resolution water. • Best methods applied to 1985–2021 Landsat time-series reveal overall wetting trends. • Spatial trends generally suggest drying in bogs and wetting in fens. [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.)

In this paper, we demonstrate the feasibility of using TanDEM-X (TX) interferometric coherence data for mapping forest height with 25-m pixels across a study area near Fort Simpson, Northwest Territories (NWT), Canada. Our simplified RVOG model locally estimates forest height by combining an optimized estimation of TX interferometric coherence amplitude with the 20-m resolution Canadian Digital Elevation Model (CDEM) accounting for local slope variations. The initial map of TX height estimates provided R2 values of 0.78 and 0.88, mean errors (ME) of 1.66 m and 1.90 m, and root-mean-square errors (RMSE) of 2.7 m and 2.9 m when compared to independent height estimates derived from field plots and airborne LiDAR, respectively. We corrected the bias of TX height estimates using two variants of a LiDAR-based linear model. An application of three cover-specific linear adjustments provided the final TX height map with absolute ME ≤0.05 m and RMSE ≤2.09 m. The approach was tailored to poorly inventoried northern boreal regions through the use of archived TX data, the CDEM, a land cover map and airborne LiDAR transects. Our encouraging results support the perspective of wall-to-wall mapping of forest height across northern boreal forests in the NWT and beyond. [ABSTRACT FROM AUTHOR]

There is currently much interest in developing general approaches for mapping forest aboveground carbon density using structural information contained in airborne LiDAR data. The most widely utilized model in tropical forests assumes that aboveground carbon density is a compound power function of top of canopy height (a metric easily derived from LiDAR), basal area and wood density. Here we derive the model in terms of the geometry of individual tree crowns within forest stands, showing how scaling exponents in the aboveground carbon density model arise from the height−diameter (H−D) and projected crown area−diameter (C−D) allometries of individual trees. We show that a power function relationship emerges when the C−D scaling exponent is close to 2, or when tree diameters follow a Weibull distribution (or other specific distributions) and are invariant across the landscape. In addition, basal area must be closely correlated with canopy height for the approach to work. The efficacy of the model was explored for a managed uneven−aged temperate forest in Ontario, Canada within which stands dominated by sugar maple (Acer saccharum Marsh.) and mixed stands were identified. A much poorer goodness−of−fit was obtained than previously reported for tropical forests (R2 = 0.29 vs. about 0.83). Explanations for the poor predictive power on the model include: (1) basal area was only weakly correlated with top canopy height; (2) tree size distributions varied considerably across the landscape; (3) the allometry exponents are affected by variation in species composition arising from timber management and soil conditions; and (4) the C-D allometric power function was far from 2 (1.28). We conclude that landscape heterogeneity in forest structure and tree allometry reduces the accuracy of general power-function models for predicting aboveground carbon density in managed forests. More studies in different forest types are needed to understand the situations in which power functions of LiDAR height are appropriate for modelling forest carbon stocks. [ABSTRACT FROM AUTHOR]

Mapping Canada's forests is a significant challenge given their extent and the interprovincial differences in forest inventories. We created new sets of nationally consistent forest attribute maps for the years 2001 and 2011 by building upon previously published work with the objective to determine if sequential maps of forest attributes could be used to quantify changes over time. We first refined our previously published methodology of using the k nearest neighbors ( kNN) prediction method and MODIS spectral reflectance data as predictive variables. The maps were generated using an improved reference dataset and a new analytical kNN workflow. We then evaluated 2001 to 2011 changes in two key attributes, aboveground biomass and percent tree cover, on pixels identified from published sources as having undergone fire, harvest, or postdisturbance regrowth during that period. For all three change types, average changes in both aboveground biomass and percent tree cover between 2001 and 2011 matched expectations relative to the dynamics of Canadian forests. Our results support the use of sequential national maps of forest attributes for evaluating regionally aggregated disturbance-related changes in forest properties. The new forest attribute maps are available from ; doi: at . [ABSTRACT FROM AUTHOR]

Forest age is an important variable for assessments of biodiversity and habitat, sustainable forest and land management, as well as forest carbon science and modeling. Tree and stand age are typically measured directly on site, or estimated through visual photo interpretation, with spatially explicit maps of forest age not often produced over large areas. Remote sensing enables the generation of wall-to wall, spatially explicit maps of disturbance events within the satellite record; however, as disturbance is relatively rare on the landscape in a given year, additional means of determining forest age are required. As reviewed herein, the estimation of forest age using optical Earth observation data is challenging due to the limited spectral link to the attribute of interest, especially as forests get older. The temporally dictated multi-method approach to forest age estimation outlined herein acknowledges these limitations, by applying the approach that is best suited to the quality of the information available, depending on the epoch of interest. In this research, we combine three approaches to estimate forest age at a 30-m spatial resolution using Landsat data. The first approach uses change detection protocols to detect disturbance from 1985 to 2019, with time since disturbance used as a proxy for forest age. The second approach uses Landsat surface reflectance composites to identify pixels exhibiting evidence of recovery from a disturbance that occurred within the twenty years prior to 1985, allowing for the extension of forest age estimates to 1965. Finally, given an understanding of the linkage between forest age and canopy height, inverted allometric equations are coupled with maps of forest structure and productivity metrics to model forest age for those pixels that show no evidence of disturbance or recovery to a maximum of 150 years, acknowledging that uncertainty in age estimate increases with increasing age. Combining these three approaches, forest age estimates are made for every treed pixel found within the 650 Mha forested ecosystems of Canada. Nationwide, mean estimated forest age for forests ≤150 years old (representing 94.1% of treed area) was 70 years (standard deviation = 32.1 years). For confidence building, forest age estimates were compared to reported forest age in the National Forest Inventory (NFI) both spatially and aspatially. Nationally, 5.9% of the forested area was estimated to be older than 150 years, while 9.5% of area within in the NFI sample was recorded as older than 150 years. The median estimated forest age for forested pixels ≤150 years old was 68 years while median forest age reported in the NFI was 73 years, with regional variability matching expectations related to disturbance regimes and productivity. Spatially explicit maps of forest age provide important information for understanding forest ecosystems and can be used to inform a wide range of policy, science, and management needs. • Forest age was estimated at 30-m scale across all of Canada's forested ecosystems. • Three approaches were used to estimate age depending on temporal era. • Agreement with forest inventory recorded age decreased as estimated age increased. • Nationally, mean estimated age for forests <150 years old was 70 years. • 5.9% of Canada's forested area has an age of >150 years. [ABSTRACT FROM AUTHOR]

The Earth Observation for Sustainable Development of Forests (EOSD) map is a 25 m resolution thematic map of Canadian forests. Because of its large spatial extent and relatively high resolution the EOSD is difficult to analyze using standard GIS methods. In this paper we propose multi-scale segmentation and regionalization of EOSD as new methods for analyzing EOSD on large spatial scales. Segments, which we refer to as forest land units (FLUs), are delineated as tracts of forest characterized by cohesive patterns of EOSD categories; we delineated from 727 to 91,885 FLUs within the spatial extent of EOSD depending on the selected scale of a pattern. Pattern of EOSD's categories within each FLU is described by 1037 landscape metrics. A shapefile containing boundaries of all FLUs together with an attribute table listing landscape metrics make up an SQL-searchable spatial database providing detailed information on composition and pattern of land cover types in Canadian forest. Shapefile format and extensive attribute table pertaining to the entire legend of EOSD are designed to facilitate broad range of investigations in which assessment of composition and pattern of forest over large areas is needed. We calculated four such databases using different spatial scales of pattern. We illustrate the use of FLU database for producing forest regionalization maps of two Canadian provinces, Quebec and Ontario. Such maps capture the broad scale variability of forest at the spatial scale of the entire province. We also demonstrate how FLU database can be used to map variability of landscape metrics, and thus the character of landscape, over the entire Canada. [ABSTRACT FROM AUTHOR]

Ecological processes are increasingly well understood over smaller areas, yet information regarding interconnections and the hierarchical nature of ecosystems remains less studied and understood. Information on connectivity over large areas with high resolution source information provides for both local detail and regional context. The emerging capacity to apply circuit theory to create maps of omnidirectional connectivity provides an opportunity for improved and quantitative depictions of forest connectivity, supporting the formation and testing of hypotheses about the density of animal movement, ecosystem structure, and related links to natural and anthropogenic forces. In this research, our goal was to delineate regions where connectivity regimes are similar across the boreal region of Canada using new quantitative analyses for characterizing connectivity over large areas (e.g., millions of hectares). Utilizing the Earth Observation for Sustainable Development of forests (EOSD) circa 2000 Landsat-derived land-cover map, we created and analyzed a national-scale map of omnidirectional forest connectivity at 25m resolution over 10000 tiles of 625 km2 each, spanning the forested regions of Canada. Using image recognition software to detect corridors, pinch points, and barriers to movements at multiple spatial scales in each tile, we developed a simple measure of the structural complexity of connectivity patterns in omnidirectional connectivity maps. We then mapped the Circuitscape resistance distance measure and used it in conjunction with the complexity data to study connectivity characteristics in each forested ecozone. Ecozone boundaries masked substantial systematic patterns in connectivity characteristics that are uncovered using a new classification of connectivity patterns that revealed six clear groups of forest connectivity patterns found in Canada. The resulting maps allow exploration of omnidirectional forest connectivity patterns at full resolution while permitting quantitative analyses of connectivity over broad areas, informing modeling, planning and monitoring efforts. [ABSTRACT FROM AUTHOR]

Mapping peatland extent in Canada would contribute important information concerning carbon balance and hydrology. While such mapping, based on air photo interpretation and remote sensing data, has recently improved, maps have been limited to 1:1 million scale. We hypothesized that forest structure information from forest inventory plots could be used to predict the presence of forested and treed peatlands in boreal Canada at the ground plot-level, and that a resulting model could be used to predict the distribution of forested and treed peatlands across Canada. Inventory ground plots from the Canadian National Forest Inventory (NFI) with organic soil depth measurements were used to create a model of the presence of treed to forested (canopy cover ranging from sparse to closed) peatlands (greater than 40 cm organic soil depth) in boreal Canada. The presence of black spruce ( Picea mariana ) or larch ( Larix laricina ), in combination with low stand height and stand age greater than 75 years, were the strongest predictors of the presence of peatlands. Bioclimatic variables related to high diurnal and annual temperature variation, consistent with a continental climate, also contributed to the increased predicted presence of treed peatlands. Both logistic and boosted regression tree models showed similar results, with ∼87% accuracy in the discrimination of treed peatlands when validated against an independent set of ground plots. The boosted regression tree model was propagated across Canada using forest attribute raster data layers at 250 m resolution from the NFI along with bioclimatic layers. Estimates of treed peatland extent agreed with data points from peat cores with 85–95% accuracy in the Boreal Shield ecozone, although prediction was less accurate in the more southern boreal and Great Lakes forest areas. The resulting map can be used as an input to forest carbon modelling, and the improved knowledge of treed peatland extent will be useful in modelling wildfire or peatland drainage. [ABSTRACT FROM AUTHOR]

Deforestation is the direct human-induced conversion of forest to nonforest land uses. It is important for nations to understand and report the extent of their deforestation. Because of the vastness of Canada's forest and the rare and spatially diverse nature of its deforestation, a sampling approach in which deforestation is mapped and then scaled up to represent deforestation for different regions was needed. The effectiveness of different sample designs in capturing the area of deforestation was evaluated using a Monte Carlo approach in which alternate sample designs were applied to simulated forest landscapes representative of different regions and deforestation patterns in Canada. Sampling error as expressed by the standard error in the estimated deforestation level for the sample divided by actual deforestation of the simulated landscape was used as a measure of sample design performance. Results indicated that sampling error was dependent on the characteristics of the deforestation (e.g., amount, shape, size, and distribution). For example, as mean event size increases or the proportion of linear deforestation events (e.g., roads and corridors) decreases, the required sampling intensity to reach a certain level of sampling error increases, and landscapes with a small number of very large events required the largest sampling intensity. To achieve a relative sampling error target (standard error / sample mean) of 10%, given sample designs of square plots on a systematic grid, a sample of 15%-25% of a landscape will be required for most Canadian landscapes, given a 10-year mapping time frame (interval between samples) and assuming a deforestation rate of 0.025% per annum. With mapping over a 5-year period, the required sampling intensity rises to 20%-40%. Also discussed are the consequences of the sampling error of different designs on the uncertainty in estimated greenhouse gas emission resulting from deforestation. [ABSTRACT FROM AUTHOR]

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]

The aim of this study was to develop an imputing method to map forest stand attributes (height and volume) using panchromatic aerial photographs with a spatial resolution of 30 cm and Landsat images. The method was tested on several sites in Que'bec, Canada, that are dominated by black spruce. The method involved four sets of procedures: (i) measuring the shadow fraction from panchromatic aerial photographs, (ii) generating regression models between the shadow fraction and forest attributes, (iii) locally mapping the forest attributes as a grid layer (30 m × 30 m), and (iv) expanding the forest attributes from the local maps to a large study area using an imputing approach. Regression models between the shadow fraction and stand attributes were calibrated with ground sample plots and a series of 73 aerial photographs. General results from linear relationships between the shadow fraction and two stand attributes showed linear shapes but had low values for the goodness of fit (R² adj of 0.24 for height and 0.31 for volume). However, the results improved significantly (R² adj of 0.40 for height and 0.59 for volume) when the portion of the aerial photograph used was restricted to its centre (between 6.28 and 11.38). This restricted area of the aerial photographs produced small local maps of stand attributes from the shadow fraction. These local maps were used as the training dataset for an imputing approach, a k nearest neighbours algorithm, with the aim of mapping forest attributes over a large area. [ABSTRACT FROM AUTHOR]

As the number of satellite-borne synthetic aperture radar (SAR) systems increases, both the availability and the length of multi-temporal (MT) sequences of SAR images have also increased. Previous research on MT SAR sequences suggests that they increase the classification accuracy for all applications over single date images. Yet the presence of speckle noise remains a problem and all images in the sequence must be speckle filtered before acceptable classification accuracy can be attained. Several speckle filters designed specifically for MT sequences have been reported in the literature. Filtering in the spatial domain, as is usually done, reduces the effective spatial resolution of the filtered image. MT speckle filters operate in both the spatial and temporal dimensions, thus the reduction in resolution is not likely to be as severe (although a comparison between MT and spatial filters has not been reported). While this advantage may be useful when extracting spatial features from the image sequence, it is not quite as apparent for classification applications. This research explores the relative performance of spatial and MT speckle filtering for a particular classification application: mapping boreal forest types. We report filter performance using the radiometric resolution as measured by the equivalent number of looks (NL), and classification performance as measured by the classification accuracy. We chose representative spatial and MT filters and found that spatial speckle filters offer the advantage of higher radiometric resolution and higher classification accuracy with lower algorithm complexity. Thus, we confirm that MT filtering offers no advantage for classification applications; spatial speckle filters yield higher overall performance. [ABSTRACT FROM AUTHOR]

Abstract: This study demonstrates a method to map forest stand polygons based on four forest attributes (volume, basal area, height, and crown closure) using shadow fraction values estimated from high spatial resolution QuickBird panchromatic images. The method was tested over three test sites in northeastern Canada that were largely dominated by black spruce (Picea mariana (Mill.) BSP.). The method involved four sets of procedures: (i) estimating shadow fraction from the panchromatic band of QuickBird images, (ii) generating site-specific and global regression models linking shadow fraction with each of the four forest attributes, (iii) mapping the forest attributes as a grid layer (30×30m) for each test site using the global regression models, and (iv) generating stand polygons from the raster layers. Each test site was covered by a QuickBird satellite image with 0.6×0.6m of spatial resolution. Between 2002 and 2004, 108 ground sample plots were acquired to develop local regression models. Goodness of fit (R 2) of the linear regression models between shadow fraction and stand attributes ranged from 0.55 to 0.79. Relative root mean square error and bias estimates were, respectively, 0.32 and 0.15 for volume; 0.29 and 0.14 for basal area; 0.32 and −0.04 for crown closure; and 0.17 and 0.05 for height. Stand volume maps were produced from both the shadow fraction method and conventional forest stand maps (derived from aerial photo-interpretation) for a test site. Volume patterns were similar, and total volume for the test site differed by only 5.6% between the two maps. Lastly, the raster images derived from the shadow fraction method were used to produce a stand map following guidelines similar to those used by provincial inventory. In all cases, our results suggest that the shadow fraction method is a reliable and convenient way to map forest stand polygons and related attributes of black spruce stands of northeastern forests of Canada. [Copyright &y& Elsevier]

Deriving land cover from remotely sensed data is fundamental to many operational mapping and reporting programs as well as providing core information to support science activities. The ability to generate land cover maps has benefited from free and open access to imagery, as well as increased storage and computational power. The accuracy of the land cover maps is directly linked to the calibration (or training) data used, the predictors and ancillary data included in the classification model, and the implementation of the classification, among other factors (e.g., classification algorithm, land cover heterogeneity). Various means for improving calibration data can be implemented, including using independent datasets to further refine training data prior to mapping. Opportunities also arise from a profusion of possible calibration datasets from pre-existing land cover products (static and time series) and forest inventory maps through to observation from airborne and spaceborne lidar observations. In this research, for the 650 Mha forested ecosystems of Canada, we explored approaches to refine calibration data, integrate novel predictors, and optimize classifier implementation. We refined calibration data using measures of forest vertical structure, integrated novel spatial (via distance-to metrics) model predictors, and implemented a regionalized approach for optimizing training data selection and model-building to ensure local relevance of calibration data and capture of regional variability in land cover conditions. We found that additional vetting of training data involved the removal of 44.7% of erroneous samples (e.g. treed vegetation without vertical structure) from the training pool. Nationally, distance to ephemeral waterbodies was a key predictor of land cover, while the importance of distance to permanent water bodies varied on a regional basis. Regionalization of model implementation ensured that classification models used locally relevant descriptors and resulted in improved classification outcomes (overall accuracy: 77.9% ± 1.4%) compared to a generalized, national model (70.3% ± 2.5%). The methodological developments presented herein are portable to other land cover projects, monitoring programs, and remotely sensed data sources. The increasing availability of remotely sensed data for land cover mapping, as well as non-image data for aiding with model development (from calibration data to complementary spatial data layers) provide new opportunities to improve and further automate land cover mapping procedures. • Methodological framework to produce annual land cover from Landsat time series • Training data derived from existing land cover products and refined using lidar data • Novel distance-to surfaces to inform models with spatial-ecological information used • A regionalized approach to training data selection and model development implemented • Open datasets with roles in training and modeling serve to improve land cover maps [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.)

Information on the spatial distribution of dominant tree species is important as input or validation data in large scale ecological studies, such as carbon budget estimation, biodiversity assessment, and vegetation dynamic modelling. We used a 1-km resolution land cover map of Canada produced from 1998 SPOT VGT satellite data as the base map. Fractions of coniferous needleleaf or deciduous broadleaf forests within each 1-km pixel were then estimated by developing relationships with climate using land-cover maps from Landsat images selected as a representative sample. The fraction of coniferous/deciduous forest within each 1-km pixel was further divided into dominant species fractions using species composition information at the sub-ecoregion level, derived from statistics in the Canadian Forest Inventory (CanFI 1991/94 version), from narrative descriptions of ecoregions, and from topographical data. A total of 17 Canada-wide maps of dominant tree species groups at 1 km-resolution were produced. The estimates of this study agree well with CanFI data at the ecoregion level where available, and show good correspondence with published range maps of Canadian tree species. [ABSTRACT FROM AUTHOR]

The identification and classification of mountain pine beetle, Dentroctonus ponderosa (Hopkins), red-attack damage patterns in a mature lodgepole pine (Pinus contorta) forest located in the Fort St. James Forest District, British Columbia, was accomplished using 1999 Landsat TM satellite imagery, 1999 mountain pine beetle field and aerial survey point data, and G15 forest inventory data. Unrelated variance in the observed spectral response at mountain pine beetle field and aerial survey points was reduced following image stratification with the GIS forest inventory data and removal of other factors uncharacteristic of red-attack damage. Locations of known mountain pine beetle infestation were used to train a maximum-likelihood algorithm; overall classification accuracy was 73 percent, based on an assessment of 360 independent validation points. If local stand variability is reduced prior to signature generation, accuracies and map products can be useful for those involved in active forest management decisionmaking regarding mountain pine beetle infestations. [ABSTRACT FROM AUTHOR]

This section offers news briefs on the Canadian Institute of Forestry/Institut Forestier du Canada (CIF/IFC) as of March 2010. CIF/IFC held a press release to call for investment in tools and technologies needed in forest inventory. It has added a module to its Web site that links to various online social networking sites to allow its members to engage in related activities, programs and special projects. It collaborated with the College of Alberta Professional Foresters for the collection of both organizations' dues.

Seabed sediment composition is an important component of benthic habitat and there are many approaches for producing maps that convey sediment information to marine managers. Random Forest is a popular statistical method for thematic seabed sediment mapping using both categorical and quantitative supervised modelling approaches. This study compares the performance and qualities of these Random Forest approaches to predict the distribution of fine-grained sediments from grab samples as one component of a multi-model map of sediment classes in Frobisher Bay, Nunavut, Canada. The second component predicts the presence of coarse substrates from underwater video. Spatial and non-spatial cross-validations were conducted to evaluate the performance of categorical and quantitative Random Forest models and maps were compared to determine differences in predictions. While both approaches seemed highly accurate, the non-spatial cross-validation suggested greater accuracy using the categorical approach. Using a spatial cross-validation, there was little difference between approaches—both showed poor extrapolative performance. Spatial cross-validation methods also suggested evidence of overfitting in the coarse sediment model caused by the spatial dependence of transect samples. The quantitative modelling approach was able to predict rare and unsampled sediment classes but the flexibility of probabilistic predictions from the categorical approach allowed for tuning to maximize extrapolative performance. Results demonstrate that the apparent accuracies of these models failed to convey important differences between map predictions and that spatially explicit evaluation strategies may be necessary for evaluating extrapolative performance. Differentiating extrapolative from interpolative prediction can aid in selecting appropriate modelling methods. [ABSTRACT FROM AUTHOR]

The article discusses the release in September 2006 of comprehensive map-based tools that allow groups to manage the northern forests of Alaska, Canada and Russia. The World Resources Institute and Global Forest Watch developed three sets of electronic maps that trace the frontiers of human influence across these forests. The three maps are called "Mapping Undisturbed Landscapes in Alaska," "Canada's Large Intact Forest Landscapes and Canada's Forest Landscape Fragments," and "Mapping High Conservation Value Forests of Primorsky Kray, Russia Far East." A discussion of forest ecosystem policies that rely on maps for their implementation is also included.