Geology topics

Dar Roberts

Publications and source records attributed to Dar Roberts.

5 recordsLinked to original sources

USGS2025

Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands

Coastal soil salinization patterns are changing due to drought, sea level rise (SLR), and changing freshwater inflow. These changes are expected to impact coastal wetland plant health and ecosystem function, such as changes to biomass and productivity. These impacts have led to greater interest in how we monitor soil salinization across spatial and temporal scales. Remote sensing is a promising tool for estimating soil salinity at the spatial scales required for decision making by land managers. However, the development of a remote sensing estimation approach for wetland soil salinity must account for two factors: (1) the high spatial and temporal heterogeneity of coastal wetlands and (2) the fact that soil salinity is the result of multiple historical land use, hydrological, and geomorphic processes. In spring 2022, a combined airborne-field campaign, known as SHIFT, collected a weekly time series of airborne visible to shortwave infrared (VSWIR) image spectroscopy data. This dataset provides a unique opportunity to assess the application of fine spatial (5 m) and temporal (weekly) resolution VSWIR data to estimate root zone soil salinity; when combined with environmental variables such as elevation, these data can account for some of these factors. In this study, we utilized VSWIR and elevation datasets in a random forest regression to predict and map soil salinity in an intermittently tidal estuary, Devereux Slough, located in Santa Barbara County, California. The final model combined spectral indices with elevation to better capture soil salinity dynamics despite lower correlation ( r = 0.85) than solely using elevation ( r = 0.92). This research demonstrates the utility of remote sensing datasets, namely, elevation and the modified Anthocyanin Reflectance Index (mARI), for predicting root zone soil salinity in intermittently tidal coastal wetlands. These findings are an important step in advancing coastal remote sensing by creating a gridded salinity dataset that can be used for salinity monitoring and other coastal applications, such as modeling change in vegetation communities or ecosystems facing the impacts of climatic variability and change.

California↗

USGS2025

Brief communication: Not as dirty as they look, flawed airborne and satellite snow spectra

Key to the success of spaceborne missions is understanding snowmelt in our warming climate, as this has implications for nearly 2 billion people. An obstacle is that surface reflectance products over snow show an erroneous hook with decreases in the visible wavelengths, causing per-band and broadband reflectance errors of up to 33 % and 11 %, respectively. This hook is sometimes mistaken for soot or dust but can result from three artifacts: (1) background reflectance that is too dark, (2) an assumption of level terrain, or (3) differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are being implemented.

The Cryosphere↗

USGS2020

Climate sensitivity of water use by riparian woodlands at landscape scales

Semi‐arid riparian woodlands face threats from increasing extractive water demand and climate change in dryland landscapes worldwide. Improved landscape‐scale understanding of riparian woodland water use (evapotranspiration, ET) and its sensitivity to climate variables is needed to strategically manage water resources, as well as to create successful ecosystem conservation and restoration plans for potential climate futures. In this work, we assess the spatial and temporal variability of Cottonwood ( Populus fremontii )‐Willow ( Salix gooddingii ) riparian gallery woodland ET and its relationships to vegetation structure and climate variables for 80 km of the San Pedro River corridor in southeastern Arizona, USA, between 2014 and 2019. We use a novel combination of publicly available remote sensing, climate and hydrological datasets: cloud‐based Landsat thermal remote sensing data products for ET (Google Earth Engine EEFlux), Landsat multispectral imagery and field data‐based calibrations to vegetation structure (leaf‐area index, LAI), and open‐source climate and hydrological data. We show that at landscape scales, daily ET rates (6–10 mm day −1 ) and growing season ET totals (400–1,400 mm) matched rates of published field data, and modelled reach‐scale average LAI (0.80–1.70) matched lower ranges of published field data. Over 6 years, the spatial variability of total growing season ET (CV = 0.18) exceeded that of temporal variability (CV = 0.10), indicating the importance of reach‐scale vegetation and hydrological conditions for controlling ET dynamics. Responses of ET to climate differed between perennial and intermittent‐flow stream reaches. At perennial‐flow reaches, ET correlated significantly with temperature, whilst at intermittent‐flow sites ET correlated significantly with rainfall and stream discharge. Amongst reaches studied in detail, we found positive but differing logarithmic relationships between LAI and ET. By documenting patterns of high spatial variability of ET at basin scales, these results underscore the importance of accurately accounting for differences in woodland vegetation structure and hydrological conditions for assessing water‐use requirements. Results also suggest that the climate sensitivity of ET may be used as a remote indicator of subsurface water resources relative to vegetation demand, and an indicator for informing conservation management priorities.

Arizona↗

USGS2010

A method for qualitative mapping of thick oil spills using imaging spectroscopy

A method is described to create qualitative images of thick oil in oil spills on water using near-infrared imaging spectroscopy data. The method uses simple 'three-point-band depths' computed for each pixel in an imaging spectrometer image cube using the organic absorption features due to chemical bonds in aliphatic hydrocarbons at 1.2, 1.7, and 2.3 microns. The method is not quantitative because sub-pixel mixing and layering effects are not considered, which are necessary to make a quantitative volume estimate of oil.

Open-File Report↗

USGS2010

A method for quantitative mapping of thick oil spills using imaging spectroscopy

In response to the Deepwater Horizon oil spill in the Gulf of Mexico, a method of near-infrared imaging spectroscopic analysis was developed to map the locations of thick oil floating on water. Specifically, this method can be used to derive, in each image pixel, the oil-to-water ratio in oil emulsions, the sub-pixel areal fraction, and its thicknesses and volume within the limits of light penetration into the oil (up to a few millimeters). The method uses the shape of near-infrared (NIR) absorption features and the variations in the spectral continuum due to organic compounds found in oil to identify different oil chemistries, including its weathering state and thickness. The method is insensitive to complicating conditions such as moderate aerosol scattering and reflectance level changes from other conditions, including moderate sun glint. Data for this analysis were collected by the NASA Airborne Visual Infrared Imaging Spectrometer (AVIRIS) instrument, which was flown over the oil spill on May 17, 2010. Because of the large extent of the spill, AVIRIS flight lines could cover only a portion of the spill on this relatively calm, nearly cloud-free day. Derived lower limits for oil volumes within the top few millimeters of the ocean surface directly probed with the near-infrared light detected in the AVIRIS scenes were 19,000 (conservative assumptions) to 34,000 (aggressive assumptions) barrels of oil. AVIRIS covered about 30 percent of the core spill area, which consisted of emulsion plumes and oil sheens. Areas of oil sheen but lacking oil emulsion plumes outside of the core spill were not evaluated for oil volume in this study. If the core spill areas not covered by flight lines contained similar amounts of oil and oil-water emulsions, then extrapolation to the entire core spill area defined by a MODIS (Terra) image collected on the same day indicates a minimum of 66,000 to 120,000 barrels of oil was floating on the surface. These estimates are preliminary and subject to revision pending further analysis. Based on laboratory measurements, near-infrared (NIR) photons penetrate only a few millimeters into oil-water emulsions. As such, the oil volumes derived with this method are lower limits. Further, the detection is only of thick surface oil and does not include sheens, underwater oil, or oil that had already washed onto beaches and wetlands, oil that had been burned or evaporated as of May 17. Because NIR light penetration within emulsions is limited, and having made field observations that oil emulsions sometimes exceeded 20 millimeters in thickness, we estimate that the volume of oil, including oil thicker than can be probed in the AVIRIS imagery, is possibly as high as 150,000 barrels in the AVIRIS scenes. When this value is projected to the entire spill, it gives a volume of about 500,000 barrels for thick oil remaining on the sea surface as of May 17. AVIRIS data cannot be used to confirm this higher volume, and additional field work including more in-situ measurements of oil thickness would be required to confirm this higher oil volume. Both the directly detected minimum range of oil volume, and the higher possible volume projection for oil thicker than can be probed with NIR spectroscopy imply a significantly higher total volume of oil relative to that implied by the early NOAA (National Oceanic and Atmospheric Administration) estimate of 5,000 barrels per day reported on their Web site.

Open-File Report↗