Search

There is a need to quantify relationships among vegetation structure and soil properties in coastal areas to better understand resilience, erosion, land use impacts, eutrophication, and mobility in this terrain. Remote sensing observations have been shown to effectively capture both vegetation and soil properties. However, identifying linked vegetation-soil properties and inferring soil properties from remote sensing when vegetation obscures the pixels remains challenging. Leveraging multiscale and multisensor remote sensing data and fusion techniques, we investigated the capability to identify linked vegetation-soil properties and infer the strength and stability of a barrier island along a swath where the soil surface is bare to partially to fully obscured by vegetation. To this end, we asked (1) Which remote sensing and field measured vegetation properties relate most strongly to soil properties, especially strength and moisture? (2) Do observations of soil conditions, geotechnical descriptors, vegetation species and health, and sensible hyperspectral signatures (HSI) allow accurate characterization of the combined soil-vegetation complex? We used multilevel and multi-sensor ground-, uncrewed aerial system (UAS)-, and satellite-based observations, namely HSI, lidar, ground optical, and geotechnical measurements, to test the variability and relationships among remote sensing-based measurements and geotechnical measurements, such as soil bearing strength. Firstly, we found high capability of HSI data to discriminate soil and soil moisture when any soil was exposed (beach, foredune, back dune, and marsh) and discrimination of vegetation at UAS and satellite scale. We found strong relationships among relative vegetation structure and soil properties, namely biomass estimates and soil strength, when using combined ground-based observations and UAS-based HSI observations and relative high accuracy upscaling to high-resolution satellite level maps of soil and vegetation properties. In addition, we found that adding slope and aspect data moderately enhanced the assessment of soil strength parameters in vegetated areas, although improvement of lidar data collection protocols in subsequent data collections promise further improvements in upcoming studies. In areas with tallest vegetation or soils that were highly saturated (inundation), results were mixed, likely due to poorer inference of soil background from remote sensing and soil strength from field measurements approaching zero, respectively. However, using a combination of shortwave infrared data, full spectra for analyses (spectral unmixing, dimensionality analyses, and supervised classification techniques), water-specific indices, and vegetation type information, wetland soil delineation was improved. Differences in soil and vegetation properties detected using field optical measurements were used to test upscaling techniques, i.e. training, for UAS-based HSI. With the help from ground-based data, a framework of mapping vegetation and soil specific properties was developed which enabled finer spatial analyses to be carried out with respect to the interdependence of vegetation and soil properties from remote sensing observations on a coastal barrier island.

Improved understanding of vegetation impacts on soil strength could improve erosion and landslide assessment, stabilization and resilience efforts, and off-road vehicle mobility efforts while minimizing damage to vegetation or soil surfaces. It has been shown that belowground biomass, along with soil moisture and type, influences soil strength. Standard handheld geotechnical instruments, such as cone penetrometers, vane and torvane, typically used for measurements of soil strength were developed for civil engineering projects. These devices have been applied to studies in natural areas to determine shear strength profiles, namely wetlands, stream banks, slopes, and coastal dunes. However, in cases where soils are soft or complex, can be saturated, or where vegetation exists, such as coastal areas or wetlands, traditional geotechnical instruments provide uncertain and highly variable results, can miss vegetation contributions to soil strength, and are difficult to compare to one another. A few efforts exist to address these issues. In the case of wetlands, Sasser et al (2018) developed a Wetland Soil Strength Tester (WSST), which measures the torque required to shear the combined soil-vegetation wetland matrix using a four-pin design inserted into the wetland soil 15 cm. In our ongoing work to use remote sensing to map soil and vegetation properties, even when soil is obscured by vegetation, we found that the geotechnical measurements insufficiently capture soil strength in the presence of vegetation. We adapted the WSST design and systematically tested against traditional geotechnical measurements broadly (variety of vegetated terrain, mostly grasses and shrubs, ranging from low to high heights), not necessarily just wetlands, to investigate the utility of the measurements in applications where soil strength is a primary parameter. Soil types included sand, loam, and clay, and each were tested in wet and dry conditions. Measurements of peak torque were recorded at intervals of 90 degrees up to four revolutions with and without surface vegetation present to account for more soil properties than shear failure. Results of the analyses showed that the method can account for a greater variation in initial shear strength than the cone penetrometer-based cone index in soft to medium hard soils, such as wet and dry sands with vegetation and moist to wet clays. Analyses of torque-based measurements beyond initial shear, greater than 90 degrees, revealed a non-uniform and non-linear change in force required as sheared vegetation-soil matrix is further manipulated. Hard soils, especially dry clays were beyond the maximum limits of torque measurements and did not add valuable information beyond traditional geotechnical measurements. Based on these results, a preliminary model of the relationship between the torque-based measurement and the cone index was developed, terrain maps were enhanced, and further applications are being analyzed. The torque weighted index soil strength test techniques have potential application in stability, erosion, and mobility studies as well as our ongoing research in using remote sensing for indirect inferences of soil properties in vegetated areas.

An airborne scanning topographic lidar is shown to detect a local sea-level change induced by tidally forced flow over a 60-m-deep estuarine sill. As the flow speeds up over the sill, the sea level can be expected to decrease to conserve total flow energy according to the Bernoulli equation. The measurements resolve this decrease, showing a drop in sea level over the sill crest of about 0.1 m near the start of flood tide, increasing to about 0.25 m an hour later. These values are shown to be consistent with the Bernoulli effect, but only if a fraction (about 60%) of the upstream water column flows over the sill. By inference, the remaining fraction must deflect horizontally around the sill to flow through an adjacent channel, an effect consistent with laboratory studies of stratified flow past a three-dimensional (3-D) barrier with a gap. The lidar measurements, therefore, provide some insight into the 3-D flow response.

This paper describes a portable hyperspectral goniometer system for measurement of hemispherical conical reflectance factor (HCRF) data for terrestrial applications, especially in the coastal zone. This system, the Goniometer for Portable Hyperspectral Earth Reflectance (GOPHER), consists of a computer-controlled Spectra Vista Corporation HR-1024 full-range spectrometer mounted on a rotating arc and track assembly, allowing complete coverage in zenith and azimuth of a full hemisphere for recording HCRF. The control software allows customized scan patterns to be quickly modified in the field, providing for flexibility in recording HCRF and the opposition effect with varying grid sizes and scan ranges in both azimuth and zenith directions. The spectrometer track can be raised and lowered on a mast to accommodate variations in terrain and land cover. To minimize the effect of variations in illumination during GOPHER scan cycles, a dual-spectrometer approach has been adapted to link records of irradiance recorded by a second spectrometer during the GOPHER HCRF scan cycle. Examples of field data illustrate the utility of the instrument for coastal studies.

In June 2011, a multi-sensor airborne remote sensing campaign was flown at the Virginia Coast Reserve Long Term Ecological Research site with coordinated ground and water calibration and validation (cal/val) measurements. Remote sensing imagery acquired during the ten day exercise included hyperspectral imagery (CASI-1500), topographic LiDAR, and thermal infra-red imagery, all simultaneously from the same aircraft. Airborne synthetic aperture radar (SAR) data acquisition for a smaller subset of sites occurred in September 2011 (VCR'11). Focus areas for VCR'11 were properties of beaches and tidal flats and barrier island vegetation and, in the water column, shallow water bathymetry. On land, cal/val emphasized tidal flat and beach grain size distributions, density, moisture content, and other geotechnical properties such as shear and bearing strength (dynamic deflection modulus), which were related to hyperspectral BRDF measurements taken with the new NRL Goniometer for Outdoor Portable Hyperspectral Earth Reflectance (GOPHER). This builds on our earlier work at this site in 2007 related to beach properties and shallow water bathymetry. A priority for VCR'11 was to collect and model relationships between hyperspectral imagery, acquired from the aircraft at a variety of different phase angles, and geotechnical properties of beaches and tidal flats. One aspect of this effort was a demonstration that sand density differences are observable and consistent in reflectance spectra from GOPHER data, in CASI hyperspectral imagery, as well as in hyperspectral goniometer measurements conducted in our laboratory after VCR'11.

Our multi-sensor experiments are focused on coastal characterization from hyperspectral, thermal, LiDAR, and SAR. Cal/val activities include new instrumentation such as a field portable hyperspectral goniometer.