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Sustainable forest management practices allow for a range of harvest prescriptions, including clearcut, clearcut with residual, and partial or selective cutting, which are largely distinguished by the amount of canopy cover removed. The different prescriptions are aimed to emulate natural disturbance, encourage regeneration (seed trees), or offer other ecosystem services, such as the maintenance of local biodiversity or habitat features. Using remotely sensed data, stand-replacing disturbance associated with clearcutting is commonly accurately detected. Novel time series-based change detection products offer an opportunity to determine the capacity to detect and label a wider range of harvest practices. In this research, we demonstrate the capacity of time series imagery, spectral metrics, and related attributed change products, to distinguish between different harvesting practices over a study area in central British Columbia, Canada. Producer's accuracy of harvest attribution was 79%, with 93% of harvest blocks >5 ha accurately identified. In relation to the amount of canopy cover removed, clearcut harvesting was the most accurately classified (84%), followed by clearcut with residual (79%), and partial cut (64%). Applying detailed spectral metrics derived from Landsat data revealed clearcut and partial cuts to be spectrally distinct. The annual nature of the Landsat time series also offers spatial harvest information within typical, often decadal, forest inventory update cycles. The statistically significant (p < 0.05) relationship between harvest practices and Landsat spectral information indicates a capacity to add increased attribution richness to remote sensing depictions of forest harvest.

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Coarse woody debris (CWD) is a meaningful contributor to forest carbon cycles, wildlife habitat, and biodiversity and can influence wildfire behavior. Using airborne laser scanning (ALS), we map CWD across a range of natural forest stand types in north-central British Columbia, Canada, providing forest managers with spatially detailed information on the presence and volume of ground-level woody biomass. We describe a novel methodology that isolates CWD returns from large diameter logs (&gt;30cm) using a refined grounding algorithm, a mixture of height and pulse-based filters and linear pattern recognition, to transform ALS returns into measurable, vectorized shapes. We then assess the accuracy of CWD detection at the individual log level and predict CWD volume at the plot level. We detected 64% of CWD logs and 79% of CWD volume within our plots. Increased elevation of CWD significantly aided detection (P = 0.04), whereas advanced stages of decay hindered detection (P = 0.04). ALS-predicted CWD volume totals were compared against field-measured CWD and displayed a strong correlation (R = 0.81), allowing us to expand the methodology to map CWD over a larger region. The expanded CWD volume map compared ALS volume predictions between stands and suggests greater volume in stands with older and more heterogeneous stand structure. Study Implications A methodology is presented to extract returns associated with large diameter coarse woody debris (CWD) directly from an ALS point cloud. These returns are transformed into measurable shapes and their volume estimated based on the height of the returns. The procedure is implemented over a large forested area to produce a map of local CWD volume. Production of these maps can be used to generate inventory of CWD over a range of natural forest stands to support a more well-rounded understanding of carbon levels associated with downed trees, wildlife habitat attributes, and fuel loading in the terrestrial biosphere.

Nighttime lights (NTL) are the procurement of remotely sensed artificial illumination from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite. NTL provides a unique perspective on anthropogenic activity by characterizing spatial and temporal patterns related to economic trends and human development. In this study, we assess the ability of NTL to characterize trends associated with industrial lumber production in British Columbia, Canada. We establish the presence of a logarithmic relationship between NTL and lumber mill production capacity (R2 = 0.69–0.82). The ability of NTL to temporally identify mill closures is then demonstrated by differentiating pairs of active and closed mills. We also identify Granger causality and co-integration between NTL and monthly lumber production, highlighting the predictive capability of NTL to forecast production. We then utilize this relationship to build linear regression models that utilize NTL data to estimate monthly (R2 = 0.33), quarterly (R2 = 0.58), and annual (R2 = 0.90) lumber production without reported data.