Important Airborne Lidar Metrics of Canopy Structure for Estimating Snow Interception

Abstract

Forest canopies exert significant controls over the spatial distribution of snow cover. Canopy snow interception efficiency is controlled by intrinsic processes (e.g., canopy structure), extrinsic processes (e.g., meteorological conditions), and the interaction of intrinsic-extrinsic factors (i.e., air temperature and branch stiffness). In hydrological models, intrinsic processes governing snow interception are typically represented by two-dimensional metrics like the leaf area index (LAI). To improve snow interception estimates and their scalability, new approaches are needed for better characterizing the three-dimensional distribution of canopy elements. Airborne laser scanning (ALS) provides a potential means of achieving this, with recent research focused on using ALS-derived metrics that describe forest spacing to predict interception storage. A wide range of canopy structural metrics that describe individual trees can also be extracted from ALS, although relatively little is known about which of them, and in what combination, best describes intrinsic canopy properties known to affect snow interception. The overarching goal of this study was to identify important ALS-derived canopy structural metrics that could help to further improve our ability to characterize intrinsic factors affecting snow interception. Specifically, we sought to determine how much variance in canopy intercepted snow volume can be explained by ALS-derived crown metrics, and what suite of existing and novel crown metrics most strongly affects canopy intercepted snow volume. To achieve this, we first used terrestrial laser scanning (TLS) to quantify snow interception on 14 trees. We then used these snow interception measurements to fit a random forest model with ALS-derived crown metrics as predictors. Next, we bootstrapped 1000 calculations of variable importance (percent increase in mean squared error when a given explanatory variable is removed), keeping nine canopy metrics for the final model that exceeded a variable importance threshold of 0.2. ALS-derived canopy metrics describing intrinsic tree structure explained approximately two-thirds of the snow interception variability (R2 ≥ 0.65, RMSE ≤ 0.52 m3, relative RMSE ≤ 48%) in our study when extrinsic factors were kept as constant as possible. For comparison, a generalized linear mixed-effects model predicting snow interception volume from LAI alone had a marginal R2 = 0.01. The three most important predictor variables were canopy length, whole-tree volume, and unobstructed returns (a novel metric). These results suggest that a suite of intrinsic variables may be used to map interception potential across larger areas and provide an improvement to interception estimates based on LAI.

Spatio-temporal Snow Variability in a Sub-Alpine Forest predicted by Machine Learning and UAV-based LiDAR Snow Depth Maps

“…We observed a reduction of the overall accumulation rate between 28% and 36% for the forested, compared to the open clusters. This observation, dominated by the interception of snow in the canopy, corresponds in its magnitude to existing literature values (Moeser et al, 2015;Russell et al, 2021). The high correlations between individual accumulation events (R: 0.81 -0.83) also agree with findings by Mazzotti et al (2022).…”

“…Assuming low wind speeds, spatial variability of snowfall (accumulation) events over forested areas are dominated by interception and subsequent sublimation processes of snow in the tree canopy. Its magnitude depends strongly on the three-dimensional (3D) structure of the canopy (Moeser et al, 2015;Russell et al, 2021). Interception reduces accumulation in coniferous forests by 30-40%, depending on vegetation characteristics and meteorological conditions (Broxton et al, 2014;Jost et al, 2007;Varhola et al, 2010a).…”

“…As commercially available LiDAR sensors are becoming light-weight and affordable, an increasing number of studies have been published using LiDAR-systems mounted on multi-rotor Unmanned Aerial Vehicles (UAV) instead of airplanes (Harder et al, 2020;Jacobs et al, 2021;Koutantou et al, 2022;Rathmann et al, 2021). Compared to LiDAR data acquired using airplanes, UAVs allow reduced revisiting time between surveys (Koutantou et al, 2022), increase point densities and help analyze snow processes at very high spatial resolutions (Russell et al, 2021). The drawback of UAVs is the short flight duration and thus low spatial coverage, making airborne systems still important for larger-scale applications (Kostadinov et al, 2019).…”

Impact of Forest Canopy Closure on Snow Processes in the Changbai Mountains, Northeast China

“…More importantly, at the forest stand level (40 m wide) of our study, θ showed a good correlation relationship with all the snow sub-processes (interception, sublimation, and snowmelt). Similar to previous studies on the relationship between canopy closure (or LAI) and snow processes as determined using different scales and methods by Krogh et al (2020), Broxton et al (2021), andRussell et al (2021), we also found that θ was an ideal canopy index factor to explain the variations in SD, Ic, S s , S r , and SWE in the mixed forests of the Changbai Mountains.…”

“…A variety of forest structure indicators have been used to reveal the impact of forests on snow processes, such as forest cover (Varhola et al, 2010;Pomeroy et al, 2012;Varhola and Coops, 2013), canopy cover (Pomeroy et al, 2002), leaf area index (Gelfan et al, 2004;Woods et al, 2006;Rutter et al, 2009;Lendzioch et al, 2019), and canopy closure (Broxton et al, 2021). In the meantime, as technology continues to progress, various methods have been applied comprehensively, such as forest snow sampling survey (Watson et al, 2006;Parajuli et al, 2020), snow model simulation (Pomeroy et al, 2007;Rutter et al, 2009;Krinner et al, 2018;Napoly et al, 2020), statistical modeling (López-Moreno andNogués-Bravo, 2006), snow remote sensing (Zhang et al, 2010;Frei et al, 2012;Hojatimalekshah et al, 2021), LiDAR technology (Harpold et al, 2014;Broxton et al, 2021;Russell et al, 2021), UAV remote sensing (Lendzioch et al, 2019), and delayed photography (Parajka et al, 2012;Dong and Menzel, 2017). When studying forest snow process in specific areas, the most appropriate method needs to be selected and balanced, as each method has certain advantages and limitations.…”