Geology topics

Amy Braverman

Publications and source records attributed to Amy Braverman.

3 recordsLinked to original sources

USGS2020

Quantifying uncertainty for remote spectroscopy of surface composition

Remote surface measurements by imaging spectrometers play an important role in planetary and Earth science. To make these measurements, investigators calibrate instrument data to absolute units, invert physical models to estimate atmospheric effects, and then determine surface properties from the spectral reflectance. This study quantifies the uncertainty in this process. Global missions demand predictive uncertainty models that can estimate future errors for varied environments and observing conditions. Here we validate uncertainty predictions with remote surface composition retrievals and in situ measurements in a field analogue of Earth and planetary exploration. We consider rover transects at Cuprite, Nevada, and remote observations by NASA's Next- Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). We show that accounting for input uncertainties can benefit mineral detection methods such as constrained spectrum fitting. This suggests that operational uncertainty estimates could improve future NASA missions like the Earth Mineral dust source InvesTigation (EMIT) and the Lunar Trailblazer mission, as well as NASA's Decadal Surface Biology and Geology (SBG) Investigation.

Remote Sensing of Environment↗

USGS2018

Ecosystem responses to elevated CO2 using airborne remote sensing at Mammoth Mountain, California

We present an exploratory study examining the use of airborne remote-sensing observations to detect ecological responses to elevated CO 2 emissions from active volcanic systems. To evaluate these ecosystem responses, existing spectroscopic, thermal, and lidar data acquired over forest ecosystems on Mammoth Mountain volcano, California, were exploited, along with in situ measurements of persistent volcanic soil CO 2 fluxes. The elevated CO 2 response was used to statistically model ecosystem structure, composition, and function, evaluated via data products including biomass, plant foliar traits and vegetation indices, and evapotranspiration (ET). Using regression ensemble models, we found that soil CO 2 flux was a significant predictor for ecological variables, including canopy greenness (normalized vegetation difference index, NDVI), canopy nitrogen, ET, and biomass. With increasing CO 2 , we found a decrease in ET and an increase in canopy nitrogen, both consistent with theory, suggesting more water- and nutrient-use-efficient canopies. However, we also observed a decrease in NDVI with increasing CO 2 (a mean NDVI of 0.27 at 200 g m −2 d −1 CO 2 reduced to a mean NDVI of 0.10 at 800 g m −2 d −1 CO 2 ). This is inconsistent with theory though consistent with increased efficiency of fewer leaves. We found a decrease in above-ground biomass with increasing CO 2 , also inconsistent with theory, but we did also find a decrease in biomass variance, pointing to a long-term homogenization of structure with elevated CO 2 . Additionally, the relationships between ecological variables changed with elevated CO 2 , suggesting a shift in coupling/decoupling among ecosystem structure, composition, and function synergies. For example, ET and biomass were significantly correlated for areas without elevated CO 2 flux but decoupled with elevated CO 2 flux. This study demonstrates that (a) volcanic systems show great potential as a means to study the properties of ecosystems and their responses to elevated CO 2 emissions and (b) these ecosystem responses are measurable using a suite of airborne remotely sensed data.

California↗

USGS2012

A terrain-based site characterization map of California with implications for the contiguous United States

We present an approach based on geomorphometry to predict material properties and characterize site conditions using the V S 30 parameter (time‐averaged shear‐wave velocity to a depth of 30 m). Our framework consists of an automated terrain classification scheme based on taxonomic criteria (slope gradient, local convexity, and surface texture) that systematically identifies 16 terrain types from 1‐km spatial resolution (30 arcsec) Shuttle Radar Topography Mission digital elevation models (SRTMDEMs). Using 853 V S 30 values from California, we apply a simulation‐based statistical method to determine the mean V S 30 for each terrain type in California. We then compare the V S 30 values with models based on individual proxies, such as mapped surface geology and topographic slope, and show that our systematic terrain‐based approach consistently performs better than semiempirical estimates based on individual proxies. To further evaluate our model, we apply our California‐based estimates to terrains of the contiguous United States. Comparisons of our estimates with 325 V S 30 measurements outside of California, as well as estimates based on the topographic slope model, indicate our method to be statistically robust and more accurate. Our approach thus provides an objective and robust method for extending estimates of V S 30 for regions where in situ measurements are sparse or not readily available.

California↗