An automated approach to detecting instream wood using airborne laser scanning in small coastal streams

Abstract

Instream wood is a critical component of proper aquatic ecosystem function. The accurate detection and mapping of instream wood is of key importance for sustainable forest management due to the impact that instream wood features have on stream morphology, sediment distribution, and habitat availability for numerous aquatic species. The increasing availability of Airborne Laser Scanning (ALS) data allows for the development of methods to automatically detect and map instream wood. Herein, we develop and test a novel framework to map instream wood using ALS in coastal forested watersheds, and investigate the effects of wood properties and riparian vegetation characteristics on the accuracy of instream wood detection. Our focus sites are the Artlish and Nahmint watersheds on Vancouver Island, British Columbia, Canada. The location, length, width, submerged depth, and position relative to stream banks of instream wood were measured in nine streams within the watersheds. The method has three key steps: point cloud filtering, skeletonization, and validation. Our method uses advanced ALS processing to filter point clouds based on point height, intensity, classification, and the linear relationship to neighbouring points. Results indicated that our method was able to delineate instream wood with moderate overall accuracy ranging from 37 to 87%, (and a mean of 63%). Logjams were detected with high overall accuracy (83%) and individual wood pieces with an average accuracy of 49%. We found that the percentage of ALS returns classified as ground and the submerged depth of the wood had a significant effect of the detection accuracy of instream wood (p < 0.05). The detected instream wood features could be used as inputs for fish habitat modeling and to assess how different management practices impact the distribution of these features.