Understanding the impact of tree structure on snow depth and extent is important in order to make predictions of snow amounts and how changes in forest cover may affect future water resources. In this work, we investigate snow depth under tree canopies and in open areas to quantify the role of tree structure in controlling snow depth, as well as the controls from wind and topography. We use fine-scale terrestrial laser scanning (TLS) data collected across Grand Mesa, Colorado, USA (winter 2016–2017), to measure the snow depth and extract horizontal and vertical tree descriptors (metrics) at six sites. We utilize these descriptors along with topographical metrics in multiple linear and decision tree regressions to investigate snow depth variations under the canopy and in open areas. Canopy, topography, and snow interaction results indicate that vegetation structural metrics (specifically foliage height diversity; FHD) along with local-scale processes like wind and topography are highly influential in snow depth variation. Our study specifies that windward slopes show greater impact on snow accumulation than vegetation metrics. In addition, the results indicate that FHD can explain up to 27 % of sub-canopy snow depth variation at sites where the effect of topography and wind is negligible. Solar radiation and elevation are the dominant controls on snow depth in open areas. Fine-scale analysis from TLS provides information on local-scale controls and provides an opportunity to be readily coupled with lidar or photogrammetry from uncrewed aerial systems (UASs) as well as airborne and spaceborne platforms to investigate larger-scale controls on snow depth.

The Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment has operated five whole‐ecosystem warming manipulations (+0, +2.25, +4.5, +6.75, and +9°C) with paired ambient and elevated CO₂ atmospheres (eCO₂, +500 ppm) for 8 full calendar years (since August 2015). We tracked shrub‐layer vegetation responses to the treatments using annual destructive plot sampling. Tree (Picea and Larix) responses were assessed annually using nondestructive dimensional analyses and allometric conversions. Shrub community changes were assessed for key ericaceous shrubs (Rhododendron, Chamaedaphne, and Kalmia), two Vaccinium species (V. angustifolium, V. oxycoccos), graminoid species (mostly Eriophorum), and one common forb (Maianthemum trifolium), plus minor understory species. We tracked annual aboveground net primary production (ANPP) for vascular plant species in gC m⁻² y⁻¹ and overall stand contribution in dry mass. We observed a linear increase in shrub‐layer aboveground biomass accumulation with warming over time due primarily to an increase in ericaceous shrub abundance. Cumulative biomass increases across the shrub community showed overall positive responses to eCO₂ after 8 years. Community composition also changed with warming, with increases in woody shrub density, and the reduction or loss of forbs. The tree community showed minimal initial responses to warming early in the treatments, but since 2020, has shown significant increases in ANPP and individual tree growth with warming. The main driver of change in the vascular plant community was temperature, with less pronounced effects of eCO₂ evident. These results indicate an overall increase in ANPP with warming from both the tree and shrub layers of peatland vegetation.

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems.

In much of the northern Great Basin of the western United States, rangelands, and semi‐arid ecosystems 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 the 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 unoccupied aerial systems (UAS) and instrumentation for vegetation monitoring enable greater understanding of vegetation in many ecosystems. Research to understand the relationship of traditional field measurements with remotely sensed data in rangeland environments is growing rapidly, and there is increasing interest in the use of aerial platforms to quantify AGB and fine‐fuel load at pasture and landscape scales. Our study uses relatively inexpensive handheld photography with custom quadrat 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 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 with validation ground surface reconstructions. This finding is significant given that our study site is characterized by a dense litter layer covering the ground surface, making reconstruction challenging. Overall, our best 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.

Snow albedo is a crucial component to the energy balance of our seasonal snowpack on Earth, reflecting incoming solar irradiance and altering the Earth system. Seasonal snow surface properties undergo constant change (e.g., melt-freeze cycle, faceting, sublimation, and windblown compaction) and have high spatial variability, especially in mountain regions, making it difficult to scale ground measurements. Snow albedo, fractional snow-covered area, and snow-specific surface area can be modeled using top of atmosphere radiance measurements from spaceborne imaging spectroscopy. We model these snow properties by testing inversions geared specifically for complex topography, as well as incorporating the impacts of the mixed snow pixel. Additionally, we avoid computing every pixel in an image by creating tens-of-thousands of k-means clusters based on the rounded values of the cosine of the local illumination angle to the nearest ten-thousandth digit. This computation is further sped up by leveraging the message passing interface to scale with more nodes. We present this work as an open-source algorithm, which we refer to as Global Optical Snow properties via High-speed Algorithm With K-means clustering (GOSHAWK). We validate our algorithm with PRecursore IperSpettrale della Missione Applicativa L 1 radiance imagery across eight sites in the Northern Hemisphere from 2021 to 2023 and compare outputs with field spectroscopy, specific surface area measurements, airborne LiDAR surveys, and four-component net radiometers. More work in algorithm development and calibration–validation work is needed in steep terrain and dense canopy to improve snow property retrieval prior to the Surface Biology and Geology and Copernicus Hyperspectral imaging mission for the environment missions.