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Open AccessArticle

Automated Mapping of Woody Debris over Harvested Forest Plantations Using UAVs, High-Resolution Imagery, and Machine Learning

1
Australian Centre for Field Robotics, University of Sydney, Sydney 2006, Australia
2
Department of Primary Industries-Forestry, Parramatta 2150, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(6), 733; https://doi.org/10.3390/rs11060733
Received: 8 February 2019 / Revised: 19 March 2019 / Accepted: 20 March 2019 / Published: 26 March 2019
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. View Full-Text
Keywords: Unmanned Aerial Vehicles (UAVs); computer vision; forestry; Coarse Woody Debris (CWD); Convolutional Neural Networks (CNNs) Unmanned Aerial Vehicles (UAVs); computer vision; forestry; Coarse Woody Debris (CWD); Convolutional Neural Networks (CNNs)
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MDPI and ACS Style

Windrim, L.; Bryson, M.; McLean, M.; Randle, J.; Stone, C. Automated Mapping of Woody Debris over Harvested Forest Plantations Using UAVs, High-Resolution Imagery, and Machine Learning. Remote Sens. 2019, 11, 733.

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