Forest fertilization is common in coastal British Columbia as a means to increase wood production and potentially enhance carbon sequestration. Generally, the effects of fertilization are determined by measuring sample plots pre- and post-treatment, resulting in fertilization effects being determined for a limited portion of the treatment area. Applications of remote sensing-based enhanced forest inventories have allowed for estimations to expand to the wider forested area. However, these applications have not focused on monitoring the effects of silvicultural treatments. The objective of this research was to examine if a multi-temporal application of the LiDAR area-based method can be used to detect the fertilization effects on volume, biomass, and height in a second-growth Douglas-fir (Pseudotsuga menziesii) stand. The study area on Vancouver Island was fertilized in January 2007, and sample plots were established in 2011. LiDAR acquisitions were made in 2004, prior to fertilization, and in 2008, 2011, and 2016, covering both treated and untreated areas. A total of 29 paired LiDAR blocks, comprised of four 20 m resolution raster cells, were selected on either side of the fertilization boundary for analysis of the effects across several different stand types differing in the percentage of Douglas-fir, site index, and age. Random forest (RF) plot-level models were developed to estimate total stem volume and total stem biomass for each year of LiDAR acquisition using an area-based approach. Plot level results showed an increase in stem volume by 13% fertilized over control from 2005 to 2011, which was similar to a 14% increase in above-ground carbon stocks estimated using a tree-ring stand reconstruction approach. Plot-level RF models showed R2 values of 0.86 (volume) and 0.92 (biomass) with relative cross-validated root mean square errors of 12.5% (volume) and 11.9% (biomass). For both the sample plots and LiDAR blocks, statistical results indicated no significant differences in volume or biomass between treatments. However, significant differences in height increments were detected between treatments in LiDAR blocks. The results from this research highlight the promising potential for the use of enhanced forest inventory methods to rapidly expand the assessment of treatment effects beyond sample plots to the stand, block, or landscape level.

Forest management practices can increase climate change mitigation potential through applications focused on carbon budgets. One such application involves utilizing non-merchantable material (i.e., logging residues typically piled and burned) for bio-energy. However, limited remote sensing data is available for estimating wood residues until after timber has been harvested, at which point recovery of residual wood is of little financial interest. This research utilizes a hybrid method to develop models that provide pre-harvest estimates of the amount of merchantable and non-merchantable material that would result from harvesting and investigates the scalability and transferability of such measures to the harvest block level. Models were trained using 38 plots across two sites dominated by Douglas-fir, then expanded to ten harvest blocks, and transferred to eight blocks from two sites without training data before being compared against multiple independent block-level estimates. Model results showed root mean square errors of 35% and 38% for merchantable and non-merchantable volumes, respectively. Merchantable volume estimates in blocks with training had average absolute differences from the harvest scale (9–34%) similar to transferred blocks without training (15–20%). Non-merchantable model results were also similar in both trained and transferred harvest blocks, with the pre-harvest model results having lower differences from the post-harvest geospatial versus field surveys. The results from this study show promise for hybrid methods to improve estimates of merchantable wood volume compared to conventional forest cover data approaches, and provide the ability to predict non-merchantable volumes within the range of accuracy of post-harvest residue survey methods.

The authors wish to make the following corrections to their paper [...]

Postharvest woody residues are measured to estimate billable waste, bioenergy potential, fuel loadings, and carbon budgets. In fall 2014, a waste and residue survey (WRS) established twenty-nine 0.4 ha plots in the dispersed residue stratum on two cutblocks on Vancouver Island, British Columbia, and measured woody residue “logs” ≥ 10 cm inside-bark diameter and ≥ 20 cm in length. A line-intersect sampling (LIS), in spring 2015, measured all woody debris ≥ 10 cm diameter outside bark (DOB) on 18 plots. High-resolution (2 cm) photography was acquired in summer 2015, orthophotomosaics were prepared and analyzed for residue “logs” ≥ 10 cm DOB in 29 plots using semi-automated “log” delineation (SLD) and manual heads-up “log” digitization (MLD). After adjustment for bark thickness, SLD values were still higher than WRS values, due to inclusion of non-log pieces, though MLD values were not. LIS values were not different from WRS values once adjusted for bark thickness, transect overlaps, and decayed or non-log pieces excluded. The LIS and preharvest forest cover species composition differed from the WRS. While the SLD geospatial method can census ≥ 10 cm diameter residues in entire cutblocks, it was biased. Field-based methods may be required to correct SLD bias and measure species composition to determine bark thickness and wood densities to calculate biomass from residue volumes.