Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data

Abstract

The retrieval of detailed, co‐located snow depth and canopy cover information from airborne lidar has advanced our understanding of links between forest snow distribution and canopy structure. In this study, we present two recent high‐resolution (1 m) lidar data sets acquired in (i) a 2017 mission in the Eastern Swiss Alps and (ii) NASA's 2017 SnowEx field campaign at Grand Mesa, Colorado. Validation of derived snow depth maps against extensive manual measurements revealed a RMSE of 6 and 3 cm for plot‐level mean and standard deviation of snow depth, respectively, demonstrating that within‐stand snow distribution patterns were captured reliably. Lidar data were further processed to obtain canopy structure metrics. To this end, we developed a novel approach involving a continuous measure of local distance to canopy edge (DCE), which enabled creating spatially aggregated nondirectional and directional descriptors of the canopy structure. DCE‐based canopy metrics were correlated to mean and standard deviation of snow depth over areas representing grid‐cell sizes typical of watershed and regional model applications (20–200 m). Snow depth increased along the DCE gradient from dense canopy to the center of canopy gaps for all sites and acquisition times, while directional effects particularly evolved during the ablation season. These findings highlight the control of canopy gap distribution on snow distribution in discontinuous forests, with higher snow depths where the open fraction is concentrated in few large gaps rather than many fragmented small gaps. In these environments, dedicated canopy structure metrics such as DCE should advance spatially distributed snow modeling.

Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning

“…Similar to previous work (e.g. Moeser et al, 2015a;Mazzotti et al, 2019), we found that measures of distance from canopy are important correlates with snow depth. However, most of the previous work was performed with airborne lidar across larger spatial extents and lower vertical resolutions in the canopy.…”

“…Following previous studies that showed a directional relationship with snow depths (e.g. Mazzotti et al, 2019; Lundquist, 2018), we found significantly different snow depths between the north and south sides of trees at site A, K, and O, but not other sites. This may be due to the local topography and wind at sites A and O.…”

Variable effects of forest canopies on snow processes in a valley of the central Spanish Pyrenees

“…Therefore, our experimental design, which employed intensive field work during 3 years, daily SWE estimations at snow poles, and bi‐weekly spatially distributed measurements, confirmed that accurate local data could be representative of a larger area at the plot scale. The combination of such field measurements with spatially distributed modelling of energy and mass balance of snow (Bair, Davis, & Dozier, ; Liu et al, ) and emerging techniques of measurements based on airborne LIDAR (Laser Imaging Detection and Ranging) or UAVs (Unmanned Aerial Vehicle) (Bühler, Adams, Bösch, & Stoffel, ; Currier et al, ; Mazzotti et al, ; Painter et al, ; Webster & Jonas, ) will be key to obtaining reliable large‐scale results in the future.…”

Shallow snow depth mapping with unmanned aerial systems lidar observations: A case study in Durham, New Hampshire, United States

“…ALS also has issues with observation gaps in forested regions (Broxton et al, 2015;Currier and Lundquist, 2018;Mazzotti et al, 2019) but possibly to a lesser extent than TLS . For shallow snowpacks, the typical vertical accuracies from these platforms, on the order of 10 cm (Kraus et al, 2011;Deems et al, 2013), as well as relatively low 75 return density (~10 returns/m 2 ) (Cook et al, 2013) are not adequate to observe spatial variations from micro to field scales of shallow snowpacks.…”