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

Modeling Longitudinal Data Containing Non-Normal Within Subject Errors

The mission of the National Aeronautics and Space Administrations (NASA) human research program is to advance safe human spaceflight. This involves conducting experiments, collecting data, and analyzing data. The data are longitudinal and result from a relatively few number of subjects; typically 10 20. A longitudinal study refers to an investigation where participant outcomes and possibly treatments are collected at multiple follow-up times. Standard statistical designs such as mean regression with random effects and mixedeffects regression are inadequate for such data because the population is typically not approximately normally distributed. Hence, more advanced data analysis methods are necessary. This research focuses on four such methods for longitudinal data analysis: the recently proposed linear quantile mixed models (lqmm) by Geraci and Bottai (2013), quantile regression, multilevel mixedeffects linear regression, and robust regression. This research also provides computational algorithms for longitudinal data that scientists can directly use for human spaceflight and other longitudinal data applications, then presents statistical evidence that verifies which method is best for specific situations. This advances the study of longitudinal data in a broad range of applications including applications in the sciences, technology, engineering and mathematics fields

Leveraging Google Earth Engine to Couple Landsat and MODIS for Detecting Phenological Changes in Semi-Arid Ecosystems

High spatial and temporal imagery are necessary to monitor phenological changes in semi-arid regions, but it is challenging to obtain this coverage using public satellites. Moderate Resolution Imaging Spectroradiometer (MODIS) has a repeat interval of one to two days, but coarse spatial resolutions up to 1000 m. Landsat has a higher spatial resolution of 30 m, but a 16-day period. StarFM algorithm combines multi-resolution satellite systems to interpolate data with MODIS temporal and Landsat spatial scales. We use Google Earth Engine (GEE) to preprocess the data. This improved imagery can be leveraged to classify vegetation species with short phenological cycles

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

RSVP: Remote Sensing Visualization Platform for Data Fusion

Remote sensing involves the acquisition of data in terms of images, point clouds and so on. One of the major challenges with remote sensing datasets is managing and understanding the massive amounts of data that is collected. In many instances, scientists acquire data for the same region using varied sensing devices. Scientists would like to fuse and examine this data acquired from different sensing devices to further explore the region under investigation. Immersive visualization has emerged as an ideal solution for three-dimensional exploration of multimodal remote sensing data. The ability to manipulate data interactively in true 3D (using stereo) with interfaces designed specifically for the immersive environment can significantly speed up the exploration process. We have developed a visualization platform that facilitates the fusion of multiple modalities of remote sensing data and allows a scientist to learn more about the data obtained from different sensing devices. It is currently being used in research labs at Idaho State University and at the Idaho National Labs

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

Applying Cloud-Based Computing and Emerging Remote Sensing Technologies to Inform Land Management Decisions

Who: Boise State University and Mountain Home Air Force Base What: Creating a species level classification map through the use of Google Earth Engine (GEE), a cloud-based computing platform, to map invasive species When: In-situ data collected in Summer 2018, a continuation of data collected in Summer 2016. Classification was created in Fall 2018. Unmanned aerial vehicles (UAV) flights in August 2018. Where: Mountain Home Air Force Base (MHAFB) in southwest Idaho, ecosystem is in the Great Basin Range (GBR) Why: The introduction of exotic species like cheatgrass (Bromus tectorum) has drastically altered the fire cycle of the Northern Great Basin (NGB) from 50 – 100 year burn intervals to 10 year intervals (1). Factors such as soil, elevation, temperature, and precipitation can affect the resilience of a sagebrush steppe ecosystem to invasive species. Remote sensing techniques allow large scale analysis of invasive encroachment and assessment of conservation efforts and land management

Vegetation Mapping in a Dryland Ecosystem Using Multi-Temporal Sentinel-2 Imagery and Ensemble Learning

Remote sensing of dryland ecosystem vegetation is notably problematic due to the low canopy cover and fugacious growing seasons. Relatively high temporal, spatial, and spectral resolution of Sentinel-2 imagery can address these difficulties. In this study, we combined vegetation indices with robust field data and used a Random Forests ensemble learning model to impute landcover over the study area. The resulting vegetation map product will be used by land managers, and the mapping approaches will serve as a basis for future remote sensing projects using Sentinel-2 imagery and machine learning

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