Abstract
Surveying of woody debris left over from harvesting operations on managed forests is an important step in monitoring site quality, managing the extraction of residues and reconciling differences in pre-harvest inventories and actual timber yields. Traditional methods for post-harvest survey involving manual assessment of debris on the ground over small sample plots are labor-intensive, time-consuming, and do not scale well to heterogeneous landscapes. In this paper, we propose and evaluate new automated methods for the collection and interpretation of high-resolution, Unmanned Aerial Vehicle (UAV)-borne imagery over post-harvested forests for estimating quantities of fine and coarse woody debris. Using high-resolution, geo-registered color mosaics generated from UAV-borne images, we develop manual and automated processing methods for detecting, segmenting and counting both fine and coarse woody debris, including tree stumps, exploiting state-of-the-art machine learning and image processing techniques. Results are presented using imagery over a post-harvested compartment in a Pinus radiata plantation and demonstrate the capacity for both manual image annotations and automated image processing to accurately detect and quantify coarse woody debris and stumps left over after harvest, providing a cost-effective and scalable survey method for forest managers.
Funder
Forest and Wood Products Australia
Subject
General Earth and Planetary Sciences
Reference33 articles.
1. Effects of Logging Residue Removal on Forest Sites: A Literature Review;Hacker,2005
2. Effects of whole-tree and stem-only clearcutting on post-harvest hydrologic losses, nutrient capital and regrowth;Mann;For. Sci.,1988
3. Bundled Slash: A Potential New Biomass Resource for Fuels and Chemicals
4. LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada
5. A Framework for CTL Method-Based Wood Procurement Logistics
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