User Profile: Dr. Nancy Glenn
Who uses NASA Earth science data? Dr. Nancy Glenn, to study dryland ecosystems.
Dr. Nancy Glenn, Professor, Department of Geosciences and Director, Boise Center Aerospace Laboratory, Boise State University
Research interests: Using remotely sensed data to analyze and characterize ecosystem responses to human activity with a focus on dryland ecosystems and the response of these areas to disturbance and climate change.
Research highlights: It is easy to overlook waxflower (Jamesia tetrapetala). This fragrant flowering shrub clings to alpine and subalpine cliffs and rock slopes at elevations between roughly 2,100 and 3,400 meters (about 6,900 and 11,100 feet) and stands about 3-10 decimeters (about 1-3 feet) tall. But if you happen across this small shrub with the four-petaled white flowers in the wild you know you are in the Great Basin of the United States. Waxflower is one of about six plant species found only in the Great Basin, and nowhere else in the world, according to the U.S. Forest Service (USFS). This region, and the plant and animal species within it, is extremely sensitive to the effects of climate change, human development, and excessive water use. Dr. Nancy Glenn conducts research and leads research teams to help quantify the sensitivity of the Great Basin to these effects.
A much more common Great Basin plant species, one that is impossible to overlook, is sagebrush (Artemisia sp.), which is the dominant plant across much of the region and helps define the region. With its waxy, hairy silver-gray leaves and deep, broad root system, sagebrush, like waxflower, has adapted to the harsh conditions of this high desert environment, which is characterized by hot, dry summers; cold, snowy winters; and an average of 230-300 millimeters (about 9-12 inches) of precipitation a year.
Precipitation also helps define the Great Basin, since all precipitation that falls in the Great Basin does not naturally flow into an ocean, gulf, or sea. In fact, the Great Basin is the largest area of contiguous contained watersheds in North America.
Collecting data to study the ecology of an area encompassing approximately 541,730 km2 (209,162 square miles) across parts of five western states is a job perfectly suited for remote sensing instruments carried aboard satellites and aircraft. As Director of the Boise Center Aerospace Laboratory (BCAL) at Boise State University, Dr. Glenn uses remotely sensed data to conduct and coordinate research across the region in vegetation, soils, and landscape change. While most of the lab’s research is conducted in the Great Basin, BCAL projects also include studies in the peatlands of northern Minnesota and in wetland areas along the Lower Colorado River.
Much of the lab’s work utilizes LIDAR and hyperspectral remote sensing with instruments on ground-based, airborne, and satellite platforms. LIDAR, which is an acronym for “light detection and ranging,” is a remote sensing technique that uses laser beam pulses to measure the distance of objects from the LIDAR instrument. When paired with a global positioning system (GPS) receiver, LIDAR can be used to create extremely accurate 3-D measurements of Earth. LIDAR is most commonly carried aboard an aircraft to provide measurements over broad areas, and can be used to measure features on land (topography) or under water (bathymetry).
In a recent study, Dr. Glenn and her colleagues used ground-based and airborne LIDAR to calculate above-ground biomass estimates at regional scales for sagebrush in drylands. Biomass measurements provide critical data for ecological modeling. These dryland plant communities, however, are often low-lying and sparse, which makes them difficult to measure. Dr. Glenn and her colleagues wanted to see how LIDAR improves both the ease of making these measurements and the accuracy of the resulting biomass estimates.
In a similar study, Dr. Glenn used ground-based LIDAR to estimate sagebrush leaf area index (LAI), which is an important indicator of energy, water, and carbon exchange between vegetation and the atmosphere. LIDAR was used to determine height, canopy cover, and volume for sagebrush in study sites in the Snake River Plain, Idaho. These LIDAR data were then used to estimate LAIs, which were compared with results made using a field method for estimating LAI called point-intercept sampling. Point-intercept sampling is a more time-intensive method that samples plant species variation within a sample plot and quantifies changes in plant species cover, height, and/or ground cover over time.
Along with ground-based and airborne instruments, sensors aboard orbiting satellites also are vital tools for studying ecosystems. In another recent study, Dr. Glenn and her colleagues investigated the use of data from the Landsat series of satellites to quantify the distribution of above-ground carbon and for long-term, large-area studies to calculate changes in above-ground biomass and vegetation cover, especially in dryland ecosystems. The research team looked specifically at the possibility of coupling these Landsat measurements with similar measurements that will be available from NASA’s upcoming Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which is tentatively scheduled for launch in 2018.
In their study, Dr. Glenn and her colleagues documented the capabilities of Landsat 8’s Operational Land Imager (OLI) instrument to provide better predictions of vegetation characteristics relative to similar data collected by Landsat 5’s Thematic Mapper (TM) instrument (the specific data products are noted in the Data Products Used section, below). The research team also examined how the integration of OLI and LIDAR data could improve estimates of vegetation structure. Finally, Dr. Glenn and her colleagues looked at the potential for using a combination of OLI and ICESat-2 data for vegetation analysis. The ability to couple Landsat and ICESat-2 data will allow researchers to examine many ecological variables, including quantifying fuel loads, investigating habitat quality, estimating biodiversity, measuring carbon cycling, and monitoring desertification. While data from ICESat-2’s Advanced Topographic Laser Altimeter System (ATLAS) instrument won’t be available until after launch, the research team used simulated ATLAS data from the Multiple Altimeter Beam Experimental LIDAR (MABEL) instrument, which was developed by NASA as a demonstrator instrument for ATLAS.
Along with her personal research, Dr. Glenn oversees and coordinates numerous student-centered BCAL research projects, which give students project-based learning opportunities. Current student projects include research in radiative transfer modeling and full-waveform analysis in order to measure biophysical traits (such as height and biomass) and functional traits (such as nitrogen use). These data help determine the stability of plant communities and enable teams to track how plants respond to environmental changes, including how these changes may allow non-native plant species to invade. Dr. Glenn's students also are participating in the upcoming NASA SnowEx campaign to quantify how vegetation affects snow distribution and melt. Other BCAL remote sensing projects help land managers with restoration planning, and Dr. Glenn and her students are working with the U.S. Bureau of Land Management, Bureau of Reclamation, and the military to develop multi-year monitoring efforts to prioritize and track these restoration efforts.
Data products used:
- Data products from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard NASA’s Terra and Aqua Earth observing satellites, specifically Vegetation Indices (Terra products: MOD13A1, MOD13A2, MOD13A3; Aqua products: MYD13A1, MYD13A2, MYD13A3) and Land Surface Reflectance (Terra products: MOD09A1, MOD09GA; Aqua products: MYD09A1, MYD09GA); available through NASA’s Land Processes Distributed Active Archive Center (LP DAAC)
- Various data products from NASA’s Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG); available through NASA’s Jet Propulsion Laboratory (JPL)
- Data from NASA’s Airborne Snow Observatory (ASO); available through JPL
- MABEL data; available through NASA’s ICESat-2 portal
- Landsat OLI and TM data, including Simple Vegetation Index (SVI), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI2), Moisture Stress Index (MSI), MSI2, Normalized Difference Water Index (NDWI), and NDWI2; available through the U.S. Geological Survey’s EarthExplorer data visualization and download interface, which is accessible through LP DAAC
Research findings: In her sagebrush biomass study, Dr. Glenn and her colleagues found that the integration of ground-based and airborne LIDAR measurements enhance estimates of aboveground biomass. The research team notes that while the model developed in this study is optimized for sagebrush-steppe environments, it may be readily applied to other shrub-dominated drylands in the Great Basin and similar shrub-dominated ecosystems globally.
Ground-based LIDAR also was found to be a promising tool for estimating shrub LAI. Dr. Glenn and her colleagues note the relative ease of use of this technique for measuring LAI at the shrub-level. Based on this initial research, the team recommends further work expanding these measurements to larger scale study plots using airborne lasers or regional-level studies using satellite-borne sensors.
In her comparison of Landsat sensor data with the potential for coupling these data with measurements from the upcoming ICESat-2 mission, Dr. Glenn and her colleagues found that data from Landsat’s OLI and TM sensors may be used together for long-term time-series analysis. The team notes that OLI does a good job predicting shrub and herbaceous cover and that a combination of OLI with LIDAR improves these estimates and reduces their uncertainty. To evaluate the potential use of OLI and ICESat-2 data, the team used simulated ICESat-2 data from MABEL to predict vegetation structure. While the team note that the low stature and sparse cover of dryland ecosystems present a challenge for utilizing ICESat-2 data, the team found that, overall, a combination of Landsat 8 and ICESat-2 data may improve estimates of above-ground biomass and carbon storage in drylands meeting certain minimum thresholds, which the team concludes is at minimum 30% vegetation cover at 1 meter vegetation height. One of Dr. Glenn's graduate students is investigating similar relationships through a NASA fellowship at NASA's Goddard Space Flight Center in Greenbelt, MD, in support of the upcoming Global Ecosystem Dynamics Investigation (GEDI) LIDAR mission.
Research findings in some of the general BCAL projects include better documentation in changes from native to non-native vegetation in the Great Basin and the causal relationships of fire and disturbance to these changes. In addition, BCAL teams identified priority areas for restoration planning based on a multi-temporal remote sensing-based map of native and non-native species that they compiled. Finally, BCAL teams recently completed work identifying the vegetation structure necessary for restoration on the Lower Colorado River for several key bird species. The students are now working with partners to apply these methods to other areas along the Lower Colorado River.
Read about the research:
Glenn, N.F., Neuenschwander, A., Vierling, A.A., Spaete, L.P., Li, A., Shinneman, D., Pilliod, D.S., Arkle, R. & McIlroy, S. (2016). “Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass.” Remote Sensing of Environment, 185: 233-242 [doi: 10.1016/j.rse.2016.02.039].
Olsoy, P.J., Mitchell, J.J., Levia, D.F., Clark, P.E. & Glenn, N.F. (2016) “Estimation of big sagebrush leaf area index with terrestrial laser scanning.” Ecological Indicators, 61(2): 815-21 [doi: 10.1016/j.ecolind.2015.10.034].
Li, A., Glenn, N.F., Olsoy, P.J., Mitchell, J.J. & Shrestha, R. (2015). “Aboveground biomass estimates of sagebrush using terrestrial and airborne LIDAR data in a dryland ecosystem.” Agricultural & Forest Meteorology, 213: 138-47 [doi: 10.1016/j.agrformet.2015.06.005].
Last Updated: Nov 8, 2018 at 1:16 PM EST