Canadian Forest Service Publications

Combining Area-Based and Individual Tree Metrics for Improving Merchantable and Non-Merchantable Wood Volume Estimates in Coastal Douglas-Fir Forests

Year: 2022

Issued by: Pacific Forestry Centre

Catalog ID: 40734

Language: English

Availability: PDF (request by e-mail)

Mark record


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.

Plain Language Summary

Development of enhanced forest inventory geospatial tools using remote sensing data has greatly improved pre-harvest estimates of merchantable stem wood. However geospatial tools to quantify pre-harvest non-merchantable stem wood are less well developed. In some forests, harvest residues can be equivalent to 7-11% of the merchantable wood. Residues could be better used for bioenergy or other purposes if amounts were known as harvests are planned. In this study a hybrid area-based and individual tree approach was used to analyse remote sensing data and develop a model. The model was trained on pre-harvest and post-harvest data from 10 blocks of Douglas fir and then tested on 8 other blocks. Model estimates of merchantable and non-merchantable wood were compared to actual harvest scale and residue volumes for the block. In training blocks model estimates of merchantable wood were within 9-34% of harvest scale and similar in the test blocks (15-20%). Non-merchantable model estimates were similar in both training and test blocks. Results show the promise of hybrid methods to improve pre-harvest estimates of merchantable and non-merchantable stem-wood.