Enhanced watershed status evaluation: Towards an integrated framework to assess fish habitat in forested watersheds using airborne laser scanning data

Sustainable forest management is a key component of modern forestry practices; however, an often-overlooked component is watershed health. To support forest management objectives, practitioners need information on the long-term health of riparian and stream ecosystems. Herein we present a framework to assess the condition of, and risks to, riparian and stream ecosystems within two forested fish-bearing watersheds, the Nahmint river and Artlish river, in British Columbia, Canada using airborne laser scanning (ALS) data. To do so, we first, compare the current British Columbia Tier 1 geographic information system (GIS)-based watershed condition evaluation protocol supplemented with a number of stream and watershed attributes derived from ALS data. Second, we use these ALS derived attributes to develop an enhanced Tier 2 watershed status evaluation framework that can be applied to stream reaches, which is a much finer spatial scale than is considered in current assessment approaches. We found that supplementing the existing watershed condition evaluation protocol with ALS-derived stream location, elevation, and canopy height information consistently increased the risk of a reduction in functioning condition being determined by the Tier 1 watershed assessment protocol. Further, we found that the Nahmint watershed had 28%, 65% and 7% of stream reaches with good, fair, and poor overall rankings of habitat condition, respectively. We conclude that ALS derived steam reach attributes can integrate into existing watershed evaluation protocols and provide detail that is critical to making informed management decisions and mitigating the cumulative effects on for example salmon bearing streams. As ALS data become increasingly available, value-added applications of these data, such as the integrated watershed assessment protocol demonstrated herein, can provide useful information to support forest management. •ALS derived risk indicators consistently ranked in the higher risk category.•28%, 65%, and 7% of stream reaches ranked as “good”, “fair”, and “poor” for habitat condition.•ALS integrates into existing watershed management frameworks.