Regional Sensitivities of Seasonal Snowpack to Elevation, Aspect, and Vegetation Cover in Western North America

In mountains with seasonal snow cover, the effects of climate change on snowpack will be constrained by landscape-vegetation interactions with the atmosphere. Airborne lidar surveys used to estimate snow depth, topography, and vegetation were coupled with reanalysis climate products to quantify these interactions and to highlight potential snowpack sensitivities to climate and vegetation change across the western U.S. at Rocky Mountain (RM), Northern Basin and Range (NBR), and Sierra Nevada (SNV) sites. In forest and shrub areas, elevation captured the greatest amount of variability in snow depth (16–79%) but aspect explained more variability (11–40%) in alpine areas. Aspect was most important at RM sites where incoming shortwave to incoming net radiation (SW:NetR↓) was highest (∼0.5), capturing 17–37% of snow depth variability in forests and 32–37% in shrub areas. Forest vegetation height exhibited negative relationships with snow depth and explained 3–6% of its variability at sites with greater longwave inputs (NBR and SNV). Variability in the importance of physiography suggests differential sensitivities of snowpack to climate and vegetation change. The high SW:NetR↓ and importance of aspect suggests RM sites may be more responsive to decreases in SW:NetR↓ driven by warming or increases in humidity or cloud cover. Reduced canopy-cover could increase snow depths at SNV sites, and NBR and SNV sites are currently more sensitive to shifts from snow to rain. The consistent importance of aspect and elevation indicates that changes in SW:NetR↓ and the elevation of the rain/snow transition zone could have widespread and varied effects on western U.S. snowpacks

Comparing Aerial Lidar Observations with Terrestrial Lidar and Snow-Probe Transects from NASA\u27s 2017 SnowEx Campaign

NASA\u27s 2017 SnowEx field campaign at Grand Mesa, CO, generated Airborne Laser Scans (ALS), Terrestrial Laser Scans (TLS), and snow‐probe transects, which allowed for a comparison between snow depth measurement techniques. At six locations, comparisons between gridded ALS and TLS observations, at 1‐m resolution, had a median snow depth difference of 5 cm, root‐mean‐square difference of 16 cm, mean‐absolute difference of 10 cm, and 3‐cm difference in standard deviation. ALS generally had greater but similar snow depth values to TLS, and results were not sensitive to the gridded cell size between 0.5 and 5 m. The greatest disagreements were where snow‐off TLS scans had shrubs and high incidence angles, leading to deeper snow depths (\u3e10 cm) from ALS than TLS. The low vegetation and oblique angles caused occlusion in the TLS data and thus produced higher snow‐off bare Earth models relative to the ALS. Furthermore, in subcanopy areas where both ALS and TLS data existed, snow depth differences were comparable to differences in the open. Meanwhile, median values from 52 snow‐probe transects and near‐coincident ALS data had a mean difference of 6 cm, root‐mean‐square difference of 8 cm, mean‐absolute difference of 7 cm, and a mean difference in the standard deviation of 1 cm. Snow depth probes had greater but similar snow depth values to ALS. Therefore, based on comparisons with TLS and snow depth probes, ALS captured snow depth magnitude with better than or equal agreement to what has been reported in previous studies and showed the ability to capture high‐resolution spatial variability

Imaging Spectroscopic Analysis of Biochemical Traits for Shrub Species in Great Basin, USA

The biochemical traits of plant canopies are important predictors of photosynthetic capacity and nutrient cycling. However, remote sensing of biochemical traits in shrub species in dryland ecosystems has been limited mainly due to the sparse vegetation cover, manifold shrub structures, and complex light interaction between the land surface and canopy. In order to examine the performance of airborne imaging spectroscopy for retrieving biochemical traits in shrub species, we collected Airborne Visible Infrared Imaging Spectrometer—Next Generation (AVIRIS-NG) images and surveyed four foliar biochemical traits (leaf mass per area, water content, nitrogen content and carbon) of sagebrush (Artemesia tridentata) and bitterbrush (Purshia tridentata) in the Great Basin semi-desert ecoregion, USA, in October 2014 and May 2015. We examined the correlations between biochemical traits and developed partial least square regression (PLSR) models to compare spectral correlations with biochemical traits at canopy and plot levels. PLSR models for sagebrush showed comparable performance between calibration (R2: LMA = 0.66, water = 0.7, nitrogen = 0.42, carbon = 0.6) and validation (R2: LMA = 0.52, water = 0.41, nitrogen = 0.23, carbon = 0.57), while prediction for bitterbrush remained a challenge. Our results demonstrate the potential for airborne imaging spectroscopy to measure shrub biochemical traits over large shrubland regions. We also highlight challenges when estimating biochemical traits with airborne imaging spectroscopy data

2013 Morley Nelson Snake River Birds of Prey National Conservation Area RapidEye 7m Landcover Classification

Boise State University conducted an area-wide vegetation classification of the Orchard Combat Training Center (OCTC) for the Idaho National Guard/IDARNG and expanded the classification to cover areas of the Morley Nelson Snake River Birds of Prey National Conservation Area for the Bureau of Land Management. This report documents the field data collection and processing, image acquisition and processing, and image classification. Work was performed between January 2012 – October 2015

Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales

Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass data, allowing us to scale shrub biomass from small field sites (1 m subplots and 1 ha plots) to a larger landscape. A series of airborne Lidar-derived vegetation metrics were trained and linked with the field-measured biomass in Random Forests (RF) regression models. A Stepwise Multiple Regression (SMR) model was also explored as a comparison. Our results demonstrated that the important predictors from Lidar-derived metrics had a strong correlation with field-measured biomass in the RF regression models with a pseudo R2 of 0.76 and RMSE of 125 g/m2 for shrub biomass and a pseudo R2 of 0.74 and RMSE of 141 g/m2 for total biomass, and a weak correlation with field-measured herbaceous biomass. The SMR results were similar but slightly better than RF, explaining 77–79% of the variance, with RMSE ranging from 120 to 129 g/m2 for shrub and total biomass, respectively. We further explored the computational efficiency and relative accuracies of using point cloud and raster Lidar metrics at different resolutions (1 m to 1 ha). Metrics derived from the Lidar point cloud processing led to improved biomass estimates at nearly all resolutions in comparison to raster-derived Lidar metrics. Only at 1 m were the results from the point cloud and raster products nearly equivalent. The best Lidar prediction models of biomass at the plot-level (1 ha) were achieved when Lidar metrics were derived from an average of fine resolution (1 m) metrics to minimize boundary effects and to smooth variability. Overall, both RF and SMR methods explained more than 74% of the variance in biomass, with the most important Lidar variables being associated with vegetation structure and statistical measures of this structure (e.g., standard deviation of height was a strong predictor of biomass). Using our model results, we developed spatially-explicit Lidar estimates of total and shrub biomass across our study site in the Great Basin, U.S.A., for monitoring and planning in this imperiled ecosystem

Developing and Optimizing Shrub Parameters Representing Sagebrush (\u3ci\u3eArtemisia\u3c/i\u3e spp.) Ecosystems in the Northern Great Basin Using the Ecosystem Demography (EDv2.2) Model

Ecosystem dynamic models are useful for understanding ecosystem characteristics over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. Their application, however, is challenging due to internal model uncertainties and complexities arising from distinct qualities of the ecosystems being analyzed. The sagebrush-steppe ecosystem in western North America, for example, has substantial spatial and temporal heterogeneity as well as variability due to anthropogenic disturbance, invasive species, climate change, and altered fire regimes, which collectively make modeling dynamic ecosystem processes difficult. Ecosystem Demography (EDv2.2) is a robust ecosystem dynamic model, initially developed for tropical forests, that simulates energy, water, and carbon fluxes at fine scales. Although EDv2.2 has since been tested on different ecosystems via development of different plant functional types (PFT), it still lacks a shrub PFT. In this study, we developed and parameterized a shrub PFT representative of sagebrush (Artemisia spp.) ecosystems in order to initialize and test it within EDv2.2, and to promote future broad-scale analysis of restoration activities, climate change, and fire regimes in the sagebrushsteppe ecosystem. Specifically, we parameterized the sagebrush PFT within EDv2.2 to estimate gross primary production (GPP) using data from two sagebrush study sites in the northern Great Basin. To accomplish this, we employed a three-tier approach. (1) To initially parameterize the sagebrush PFT, we fitted allometric relationships for sagebrush using field-collected data, information from existing sagebrush literature, and parameters from other land models. (2) To determine influential parameters in GPP prediction, we used a sensitivity analysis to identify the five most sensitive parameters. (3) To improve model performance and validate results, we optimized these five parameters using an exhaustive search method to estimate GPP, and compared results with observations from two eddy covariance (EC) sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent. Our finding on preliminary parameterization of the sagebrush shrub PFT is an important step towards subsequent studies on shrubland ecosystems using EDv2.2 or any other process-based ecosystem model

Semi-Arid Ecosystem Plant Functional Type and LAI from Small Footprint Waveform Lidar

Plant functional traits such as vegetation structure, density, and composition are indicators of ecosystem response to climate and human driven disturbances. We used small footprint waveform lidar with an ensemble random forest approach to differentiate the functional traits in a western US semi-arid ecosystem. We introduced a new gap fraction based leaf area index (LAI) estimator using lidar derived parameters. Results showed 60% - 89% accuracies discriminating plant functional types and estimating shrub LAI. These results imply the potential of waveform lidar to quantify plant functional traits in low-stature vegetation which is useful to assess climate impact in semi-arid ecosystems

Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain

Water-limited ecosystems encompass approximately 40% of terrestrial land mass and play a critical role in modulating Earth’s climate and provisioning ecosystem services to humanity. Spaceborne remote sensing is a critical tool for characterizing ecohydrologic patterns and advancing the understanding of the interactions between atmospheric forcings and ecohydrologic responses. Fine to medium scale spatial and temporal resolutions are needed to capture the spatial heterogeneity and the temporally intermittent response of these ecosystems to environmental forcings. Techniques combining complementary remote sensing datasets have been developed, but the heterogeneous nature of these regions present significant challenges. Here we investigate the capacity of one such approach, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, to map Normalized Difference Vegetation Index (NDVI) at 30 m spatial resolution and at a daily temporal resolution in an experimental watershed in southwest Idaho, USA. The Dry Creek Experimental Watershed captures an ecotone from a sagebrush steppe ecosystem to evergreen needle-leaf forests along an approximately 1000 m elevation gradient. We used STARFM to fuse NDVI retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) and Landsat during the course of a growing season (April to September). Specifically we input to STARFM a pair of Landsat NDVI retrievals bracketing a sequence of daily MODIS NDVI retrievals to yield daily estimates of NDVI at resolutions of 30 m. In a suite of data denial experiments we compared these STARFM predictions against corresponding Landsat NDVI retrievals and characterized errors in predicted NDVI. We investigated how errors vary as a function of vegetation functional type and topographic aspect. We find that errors in predicting NDVI were highest during green-up and senescence and lowest during the middle of the growing season. Absolute errors were generally greatest in tree-covered portions of the watershed and lowest in locations characterized by grasses/bare ground. On average, relative errors in predicted average NDVI were greatest in grass/bare ground regions, on south-facing aspects, and at the height of the growing season. We present several ramifications revealed in this study for the use of multi-sensor remote sensing data for the study of spatiotemporal ecohydrologic patterns in dryland ecosystems

Aboveground Total and Green Biomass of Dryland Shrub Derived from Terrestrial Laser Scanning

Sagebrush (Artemisia tridentata), a dominant shrub species in the sagebrush-steppe ecosystem of the western US, is declining from its historical distribution due to feedbacks between climate and land use change, fire, and invasive species. Quantifying aboveground biomass of sagebrush is important for assessing carbon storage and monitoring the presence and distribution of this rapidly changing dryland ecosystem. Models of shrub canopy volume, derived from terrestrial laser scanning (TLS) point clouds, were used to accurately estimate aboveground sagebrush biomass. Ninety-one sagebrush plants were scanned and sampled across three study sites in the Great Basin, USA. Half of the plants were scanned and destructively sampled in the spring (n = 46), while the other half were scanned again in the fall before destructive sampling (n = 45). The latter set of sagebrush plants was scanned during both spring and fall to further test the ability of the TLS to quantify seasonal changes in green biomass. Sagebrush biomass was estimated using both a voxel and a 3-D convex hull approach applied to TLS point cloud data. The 3-D convex hull model estimated total and green biomass more accurately (R2 = 0.92 and R2 = 0.83, respectively) than the voxel-based method (R2 = 0.86 and R2 = 0.73, respectively). Seasonal differences in TLS-predicted green biomass were detected at two of the sites (p \u3c 0.001 and p = 0.029), elucidating the amount of ephemeral leaf loss in the face of summer drought. The methods presented herein are directly transferable to other dryland shrubs, and implementation of the convex hull model with similar sagebrush species is straightforward