Lidar and Deep Learning Reveal Forest Structural Controls on Snowpack

Forest structure has a strong relationship with abiotic components of the environment. For example, canopy morphology controls snow depth through interception and modifies incoming thermal radiation. In turn, snow water availability affects forest growth, carbon sequestration, and nutrient cycling. We investigated how structural diversity and topography affect snow depth patterns across scales. The study site, Grand Mesa, Colorado, is representative of many areas worldwide where declining snowpack and its consequences for forest ecosystems are increasingly an environmental concern. On the basis of a convolution neural network model (R2 of 0.64; root mean squared error of 0.13 m), we found that forest structural and topographic metrics from airborne light detection and ranging (lidar) at fine scales significantly influence snow depth during the accumulation season. Moreover, complex vertically arranged foliage intercepts more snow and results in shallower snow depths below the canopy. Assessing forest structural controls on snow distribution and depth will aid efforts to improve understanding of the ecological and hydrological impacts of changing snow patterns

Remote Sensing to Advance Understanding of Snow-Vegetation Relationships and Quantify Snow Depth and Snow Water Equivalent

Snowpack is an important source of freshwater in mountainous regions. Understanding the role of different controls on snow properties (depth, distribution, and snow water equivalent (SWE)) and processes (accumulation and ablation) is important to predict available freshwater. Snow processes vary with respect to the predominant local controls in different landscapes. In many mountainous landscapes, controls on snow properties and processes are highly correlated with vegetation properties. In this dissertation, to elucidate the relationships between snow and vegetation, I use terrestrial laser scanning to explore how forest canopy structure affects snow depth distribution. In addition, I examine different vegetation metrics to find what measure of vegetation best describes snow under the canopy. By leveraging airborne lidar and deep learning, I investigate vegetation and topographical descriptors and their scale of influence on snow depth and pattern. Finally, I use radar remote sensing and machine learning techniques to estimate snow density and snow water equivalent in a mountainous western watershed

Dataset for 1 m Resolution Snow Depth, Topographical, and Vegetation Structural Metrics

The dataset is a collection of 1m resolution snow depth, elevation, aspect, slope, canopy percent cover, canopy height, foliage height diversity (FHD) with 0.5 m, 1 m, and 2 m voxel sizes. We processed lidar data of Grand Mesa, Colorado, representing a data collection effort by the NASA SnowEx campaign. Using the data we investigate how structural diversity and topography affect snow depth patterns. Snow depth is computed by snow-off and snow-on dataset from September 2016 and February 2020, respectively. Snow-off lidar data were collected by the Airborne Snow Observatory (ASO), lidar system created by NASA/JPL. Snow-on lidar data are provided by Quantum Spatial as a part of the NASA SnowEx (https://snow.nasa.gov/campaigns/snowex) campaign in 2020

Dataset for Fine Fuels and Vegetation Point Clouds from Close-Range Structure-from-Motion

Rangelands and semi-arid ecosystems are subject to increasing changes in ecologic makeup from a collection of factors. In much of the northern Great Basin of the western United States, rangelands invaded by exotic annual grasses such as cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae) are experiencing an increasingly short fire cycle, which is compounding and persistent. Improving and expanding ground-based field methods for measuring above-ground biomass (AGB) may enable more sample collections across a landscape and over succession regimes, and better harmonize with other remote sensing techniques. Developments and increased adoption of uncrewed aerial vehicles and instrumentation for vegetation monitoring are enabling greater understanding of vegetation in many ecosystems. Research towards understanding the relationship of traditional field measurements with newer aerial platforms in rangeland environments is growing rapidly, and there is increasing interest in exploring the potential use both to quantify AGB and fine fuel load at pasture and landscape scales. Our study here uses relatively inexpensive handheld photography with custom sampling frames to collect and automatically reconstruct 3D-models of the vegetation within 0.2 m2 quadrats (n = 288). Next, we examine the relationship between volumetric estimates of vegetation to compare with biomass. We found that volumes calculated with 0.5 cm voxel sizes (0.125 cm3) most closely represented the range of biomass weights. We further develop methods to classify ground points, finding a 2% reduction in predictive ability compared to using the true ground surface. Overall, our reconstruction workflow had an R2 of 0.42, further emphasizing the importance of high-resolution imagery and reconstruction techniques. Ultimately, we conclude that more work is needed of increasing extents (such as from UAS) to better understand and constrain uncertainties in volumetric estimations of biomass in ecosystems with high amounts of invasive annual grasses and fine fuel litter