The ability to expand the use of predictive Airborne Laser Scanning (ALS)-derived Forest Resource Inventory (FRI) models to broader regional scales is crucial for supporting large scale sustainable forest management. This research examined the transferability of ALS-based FRI attributes between two forest estates located in the eastern and western boreal forest regions of Canada. The sites were structurally diverse due to a strong east-to-west gradient in climate conditions and disturbance regimes. We first examined the ALS–FRI attribute relationships between the sites. Second, we applied Ordinary Least Squares regressions and Random Forest, to predict four FRI attributes. Third, we tested if the inclusion of calibration data from the target location improved the performance of the transferred models. As the sites were located on opposing sides of a bioclimatic gradient, inclusion of target calibration data improved transferred model performance. However, attribute prediction accuracy varied with modeling approach, attribute, and site. The best transferability models fell within a ± 5% relative RMSE of the local predictive models but increased up to 10% in relative bias. These results have implications for forest researchers and managers on both the number, and location, of FRI plots when considering undertaking forest inventories over large disparate areas.

Over the last decade, spatially-explicit modeling of landscape-scale forest attributes for forest inventories has greatly benefitted from airborne laser scanning (ALS) and the area-based approach (ABA) to derive wall-to-wall maps of these forest attributes. Which ALS-derived metrics to include when modeling forest inventory attributes, and how prediction accuracies vary over forest types depends largely on the structural complexity of the forest(s) being studied. Hence, the purpose of this study was to (i) examine the usefulness of adding texture and intensity metrics to height-based ALS metrics for the prediction of several forest resource inventory (FRI) attributes in one boreal and two Great Lakes, St. Lawrence (GLSL) forest region sites in Ontario and (ii) quantify and compare the site and forest type variability within the context of the FRI prediction accuracies. Basal area (BA), quadratic mean diameter-at-breast height (QMD), and stem density (S) were predicted using the ABA and a nonparametric Random Forests (RF) regression model. At the site level, prediction accuracies (i.e., expressed as RMSE (Root Mean Square Error), bias, and R²) improved at the three sites when texture and intensity metrics were included in the predictor set, even though no significant differences (p > 0.05) could be detected using the nonparametric RMANOVA test. Stem density benefitted the most from the inclusion of texture and intensity, particularly in the GLSL sites (% RMSE improved up to 6%). Combining site and forest type results indicated that improvements in site level predictions, due to the addition of texture and intensity metrics to the ALS predictor set, were the result of changes in prediction accuracy in some but not all forest types present at a site and that these changes in prediction accuracy were site and FRI attribute specific. The nonparametric Kruskal–Wallis test indicated that prediction errors between the different forest types were significantly different (p ≤ 0.01). In the boreal site, prediction accuracies for conifer forest types were higher than for deciduous and mixedwoods. Such patterns in prediction accuracy among forest types and FRI attributes could not be observed in the GLSL sites. In the Petawawa Research Forest (PRF), we did detect the impact of silvicultural treatments especially on QMD and S predictions.

Climate warming has led to an urgent need for improved estimates of carbon accumulation in uneven-aged, mixed temperate forests, where high uncertainty remains. We investigated the feasibility of using LiDAR-derived forest attributes to initialize a growth and yield (G&Y) model in complex stands at the Petawawa Research Forest (PRF) in eastern Ontario, Canada; i.e., can G&Y models based on LiDAR provide accurate predictions of aboveground carbon accumulation in complex forests compared to traditional inventory-based estimates? Applying a local G&Y model, we forecasted aboveground carbon stock (tons/ha) and accumulation (tons/ha/yr) using recurring plot measurements from 2012–2016, FVS1. We applied statistical predictors derived from LiDAR to predict stem density (SD), stem diameter distribution (SDD), and basal area distribution (BA_dist). These data, along with measured species abundance, were used to initialize a second model (FVS2). A third model was tested using LiDAR-initialized tree lists and photo-interpreted estimates of species abundance (i.e., FVS3). The carbon stock projections for 2016 from the inventory-based G&Y model) were equivalent to validation carbon stocks measured in 2016 at all size-class levels (p < 0.05), while LiDAR-based G&Y models were not. None of the models were equivalent to validation data for accumulation (p > 0.05). At the plot level, LiDAR-based predictions of carbon accumulation over a nine-year period did not differ when using either inventory or photo-interpreted species (p < 0.05). Using a constant mortality rate, we also found statistical equivalency of inventory and photo-interpreted accumulation models for all size classes ≥17 cm. These results suggest that more precise information is needed on tree characteristics than we could derive from LiDAR, but that plot-level species information is not as critical for predictions of carbon accumulation in mixed-species forests. Further work is needed on the use of LiDAR to quantify stand properties before this technique can be used to replace recurring plot measurements to quantify carbon accumulation.

The Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, Nunavut (74°55′N, 109°34′W) was established in 2003 to examine Arctic ecosystem processes that would be impacted by climate warming and permafrost degradation. This paper provides a synthesis of how remote sensing has contributed to biogeophysical modelling and monitoring at the CBAWO from 2003 to 2023. Given the location and isolated nature of the CBAWO in the Canadian High Arctic, remote sensing data and derivatives have been instrumental for studies examining ecosystem structure and function at local and landscape scales. In combination with field measurements, remote sensing data facilitated mapping and modelling of vegetation types, % vegetation cover and aboveground phytomass, soil moisture, carbon exchange rates, and permafrost degradation and disturbance. It has been demonstrated that even in an environment with limited vegetation cover and phytomass, spectral vegetation indices (e.g., the normalized difference vegetation index) are able to model various biogeophysical variables. These applications are feasible for research sites such as the CBAWO using high spatial resolution remote sensing data across the visible, infrared, and microwave regions of the electromagnetic spectrum. Furthermore, as the satellite record continues to expand, we will gain a greater understanding of the impacts arising from the expected continued warming at northern latitudes. Although the logistics for research in the Arctic remain challenging, today's technologies (e.g., high spatial resolution satellite remote sensing, automated in situ sensors and data loggers, and wireless communication systems) can support a host of scientific endeavours in the Arctic (and other remote sites) through modelling and monitoring of biogeophysical variables and Earth surface processes with limited but critical field campaigns. The research synthesized here for the CBAWO highlights the essential role of remote sensing of terrestrial ecosystems in the Canadian Arctic.