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.

Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R² of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions.

The severity of a burn for post-fire ecological effects has been assessed with the composite burn index (CBI) and the differenced Normalized Burn Ratio (dNBR). This study assessed the relationship between these two variables across recently burned areas located in the western Canadian boreal, a region not extensively evaluated in previous studies. Of particular interest was to evaluate the nature of the CBI–dNBR relationship from the perspectives of modelling, the influence of fire behaviour prediction (FBP) fuel type, and how field observations could be incorporated into the burn severity mapping process. A non-linear model form best represented the relationship between these variables for the fires evaluated, and a similar statistical performance was achieved when data from all fires were pooled into a single dataset. Results from this study suggest the potential to develop a single model for application over the western region of the boreal, but further evaluation is necessary. This evaluation could include stratification by FBP fuel type due to study results that document its apparent influence on dNBR values. A new approach for burn severity mapping was introduced by defining severity thresholds through field assessment of CBI, and from which development of new models could be incorporated directly into the mapping process.

Wildfires are a dominant disturbance to boreal forests, and in North America, they typically cause widespread tree mortality. Forest fire burn severity is often measured at a plot scale using the Composite Burn Index (CBI), which was originally developed as a means of assigning severity levels to the Normalized Burn Ratio (NBR) computed from Landsat satellite imagery. Our study investigated the potential to map biophysical indicators of burn severity (residual green vegetation and charred organic surface) at very high (3 cm) resolution, using color orthomosaics and vegetation height models derived from UAV-based photographic surveys and Structure from Motion methods. These indicators were scaled to 30 m resolution Landsat pixel footprints and compared to the post-burn NBR (post-NBR) and differenced NBR (dNBR) ratios computed from pre- and post-fire Landsat imagery. The post-NBR showed the strongest relationship to both the fraction of charred surface (exponential R² = 0.79) and the fraction of green crown vegetation above 5 m (exponential R² = 0.81), while the dNBR was more closely related to the total green vegetation fraction (exponential R² = 0.69). Additionally, the UAV green fraction and Landsat indices could individually explain more than 50% of the variance in the overall CBI measured in 39 plots. These results provide a proof-of-concept for using low-cost UAV photogrammetric mapping to quantify key measures of boreal burn severity at landscape scales, which could be used to calibrate and assign a biophysical meaning to Landsat spectral indices for mapping severity at regional scales.

A study was undertaken to assess Envisat Advanced Synthetic Aperture Radar (ASAR) ScanSAR data for quantifying forest growing stock volume (GSV) across three boreal regions with varying forest types, composition, and structure (Sweden, Central Siberia, and Québec). Estimates of GSV were obtained using hyper-temporal observations of the radar backscatter acquired by Envisat ASAR with the BIOMASAR algorithm. In total, 5.3×10⁶ km² were mapped with a 0.01° pixel size to obtain estimates representative for the year of 2005. Comparing the SAR-based estimates to spatially explicit datasets of GSV, generated from forest field inventory and/or Earth Observation data, revealed similar spatial distributions of GSV. Nonetheless, the weak sensitivity of C-band backscatter to forest structural parameters introduced significant uncertainty to the estimated GSV at full resolution. Further discrepancies were observed in the case of different scales of the ASAR and the reference GSV and in areas of fragmented landscapes. Aggregation to 0.1° and 0.5° was then undertaken to generate coarse scale estimates of GSV. The agreement between ASAR and the reference GSV datasets improved; the relative difference at 0.5° was consistently within a magnitude of 20–30%. The results indicate an improvement of the characterization of forest GSV in the boreal zone with respect to currently available information.