Using field-based, photogrammetric point cloud, orthophoto and LiDAR-derived metrics to assess forest structure–snowpack relationships in the Great Lakes–St. Lawrence Forest region

Abstract

The efficacy of field-based, photogrammetric point cloud, orthophoto and light detection and ranging datasets to describe forest structure and resolve forest–snowpack relationships in a mixed forest region was evaluated over two years at the point and transect scales. Hemispheric photo-derived canopy metrics correlated well with remotely sensed metrics, but tree bole metrics were not effectively derived from remotely sensed data. Significant differences in melt rate and snow-free date were found across forest type at the transect scale. Field and remotely sensed estimates of canopy cover were highly correlated with melt rate and snow-free date at the point scale, which aligns with previous literature and understanding of snowmelt processes. However, significant correlations were only present during the 2016 study year, which was attributed to canopy-controlled solar radiation-driven melt in 2016 versus more spatially uniform turbulent flux-driven melt in 2017. Peak snow water equivalent metrics were not correlated well with canopy or tree height metrics, contrary to previous research. This was likely due to mid-winter melt events throughout both study years, where a mix of accumulation and melt processes confounded forest–snowpack relationships. This study demonstrates that widely available remotely sensed data with a broad coverage can be used to: (i) describe forest–snowpack relationships in mixed hardwood, coniferous forests and (ii) elucidate the variability of forest–snowpack relationships under different climate conditions in this environment.