Canopy Effects on Snow Accumulation: Observations from Lidar, Canonical-View Photos, and Continuous Ground Measurements from Sensor Networks

Zheng, Ziqiong; Ma, Qin; Qian, Kun; Bales, Roger

doi:10.3390/rs10111769

Cited by 16 publications

(13 citation statements)

References 42 publications

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“…Mazzotti and colleagues [30] utilized ALS to extract a spatially continuous canopy metric (distance to canopy edge), finding that it performed better than CC for a continental snow environment and could be aggregated to the watershed scale. Zheng et al [31] found that canopy metrics extracted from ALS (e.g., mean canopy height, canopy cover fractions, etc.) explained approximately 50% of the variability of snow accumulation on the ground.…”

Section: Introductionmentioning

confidence: 99%

Important Airborne Lidar Metrics of Canopy Structure for Estimating Snow Interception

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

Important Airborne Lidar Metrics of Canopy Structure for Estimating Snow Interception

et al. 2021

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“…NASA's Airborne Snow Observatory), their point density is too low for the under-canopy analysis we did here. We have smaller lidar data sets from other areas and dates, but they are much more restricted in elevation and canopy variables, limiting our ability for the space-time substitution in this 2010 data set (Zheng et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

Canopy and Terrain Interactions Affecting Snowpack Spatial Patterns in the Sierra Nevada of California

Zheng

Jin

et al. 2019

Water Resources Research

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Airborne light detection and ranging is an emerging measurement tool for snowpack estimation, and data are now emerging to better assess multiscale snow depth patterns. We used airborne light detection and ranging measurements from four sites in the southern Sierra Nevada to determine how snow depth varies with canopy structure and the interactions between canopies and terrain. We processed the point clouds into snow depth rasters at 0.5×0.5‐m2 resolution and performed statistical analysis on the processed snow depth data, terrain attributes, and vegetation attributes, including the individual tree bole locations, canopy crown area, and canopy height. We studied the snow depth at such a fine scale due in part to the spatial heterogeneity introduced by canopy interception and enhanced melting caused by tree trunks in forested areas. We found that the dominant direction of a tree well, the area around the tree bole that has shallower snowpack, is correlated with the local aspect of the terrain, and the gradient of the snow surface in a tree well is correlated with the tree's crown area. The regression‐tree based XGBoost model was fitted with the topographic variables and canopy variables, and about 71% of snow depth variability can be explained by the model.

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“…Indeed, one notices, for instance, a range of wireless sensor networks deployed for habitat and environment monitoring applications; see, e.g., the review paper (Ruiz-Garcia et al 2009) on the use of smart and low-cost sensors in agriculture, food, and related applications. Zheng et al (2018) along with moving sprinkling system for continuous monitoring of crop canopy temperature. Zhou et al (2017) put forward a scalable field cost-effective IoTpowered phenotyping platform, referred CropQuant, for crop monitoring and trait measurement in a way to predict vegetation growth.…”

Section: Soil Pressure and Vegetation Heightmentioning

confidence: 99%

Vegetation height estimation using ubiquitous foot-based wearable platform

Nasim

Oussalah

Kløve

et al. 2020

Environ Monit Assess

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Vegetation height plays a key role in many environmental applications such as landscape characterization, conservation planning and disaster management, and biodiversity assessment and monitoring. Traditionally, in situ measurements and airborne Light Detection and Ranging (LiDAR) sensors are among the commonly employed methods for vegetation height estimation. However, such methods are known for their high incurred labor, time, and infrastructure cost. The emergence of wearable technology offers a promising alternative, especially in rural environments and underdeveloped countries. A method for a locally designed data acquisition ubiquitous wearable platform has been put forward and implemented. Next, a regression model to learn vegetation height on the basis of attributes associated with a pressure sensor has been developed and tested. The proposed method has been tested in Oulu region. The results have proven particularly effective in a region where the land has a forestry structure. The linear regression model yields (r2 = 0.81 and RSME = 16.73 cm), while the use of a multi-regression model yields (r2 = 0.82 and RSME = 15.73 cm). The developed approach indicates a promising alternative in vegetation height estimation where in situ measurement, LiDAR data, or wireless sensor network is either not available or not affordable, thus facilitating and reducing the cost of ecological monitoring and environmental sustainability planning tasks.

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Canopy Effects on Snow Accumulation: Observations from Lidar, Canonical-View Photos, and Continuous Ground Measurements from Sensor Networks

Cited by 16 publications

References 42 publications

Important Airborne Lidar Metrics of Canopy Structure for Estimating Snow Interception

Important Airborne Lidar Metrics of Canopy Structure for Estimating Snow Interception

Canopy and Terrain Interactions Affecting Snowpack Spatial Patterns in the Sierra Nevada of California

Vegetation height estimation using ubiquitous foot-based wearable platform

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