Salt marsh vegetation density varies considerably on short spatial scales, complicating attempts to evaluate plant characteristics using airborne remote sensing approaches. In this study, we used a mast-mounted hyperspectral imaging system to obtain cm-scale imagery of a salt marsh chronosequence on Hog Island, VA, where the morphology and biomass of the dominant plant species, Spartina alterniflora, varies widely. The high-resolution hyperspectral imagery allowed the detailed delineation of variations in above-ground biomass, which we retrieved from the imagery using the PROSAIL radiative transfer model. The retrieved biomass estimates correlated well with contemporaneously collected in situ biomass ground truth data ( $R^2=0.73$ ). In this study, we also rescaled our hyperspectral imagery and retrieved PROSAIL salt marsh biomass to determine the applicability of the method across spatial scales. Histograms of retrieved biomass changed considerably in characteristic marsh regions as the spatial scale of the imagery was progressively degraded. This rescaling revealed a loss of spatial detail and a shift in the mean retrieved biomass. This shift is indicative of the loss of accuracy that may occur when scaling up through a simple averaging approach that does not account for the detail found in the landscape at the natural scale of variation of the salt marsh system. This illustrated the importance of developing methodologies to appropriately scale results from very fine scale resolution up to the more coarse-scale resolutions commonly obtained in airborne and satellite remote sensing.

We measured and compared hyperspectral bidirectional reflectance factors of a sand sample of varying roughness levels using the Goniometer of the Rochester Institute of Technology-Two. We developed a geotechnical routine to generate sand samples of approximately constant density and grain size distribution, but varying roughness levels. In addition, we developed sand samples of two different classes of surface roughness: wave-like and normally distributed. The samples exhibiting normally distributed roughness met several criterion outlined by Hapke in the roughness correction to his photometric model for a smooth sediment surface. We developed a method to empirically forward model the photometric model for a rough surface using roughness metrics derived from digital elevation models of the surface. Our results from empirically forward modeling the correction factor indicate that Hapke's shadowing function does not adequately describe the effects of macroscopic roughness at a subcentimeter scale. In addition, we also performed experiments in which we oriented the surface waves of the wave-like roughness profiles in different orientations to the principal plane of illumination. These results indicate that future photometric models of wave-like roughness should include a description of how multiple scattering increases reflectance within cavities, reducing the effects of shadowing within the cavities. Our results also suggest that since Hapke's model correction for macroscopic roughness assumes that the underlying distribution of surface slopes does not depend on azimuth, it cannot adequately characterize surface roughness when it is both structured and has a preferred orientation.

The increased sensitivity of modern hyperspectral line-scanning systems has led to the development of imaging systems that can acquire each line of hyperspectral pixels at very high data rates (in the 200–400 Hz range). These data acquisition rates present an opportunity to acquire full hyperspectral scenes at rapid rates, enabling the use of traditional push-broom imaging systems as low-rate video hyperspectral imaging systems. This paper provides an overview of the design of an integrated system that produces low-rate video hyperspectral image sequences by merging a hyperspectral line scanner, operating in the visible and near infra-red, with a high-speed pan-tilt system and an integrated IMU-GPS that provides system pointing. The integrated unit is operated from atop a telescopic mast, which also allows imaging of the same surface area or objects from multiple view zenith directions, useful for bi-directional reflectance data acquisition and analysis. The telescopic mast platform also enables stereo hyperspectral image acquisition, and therefore, the ability to construct a digital elevation model of the surface. Imaging near the shoreline in a coastal setting, we provide an example of hyperspectral imagery time series acquired during a field experiment in July 2017 with our integrated system, which produced hyperspectral image sequences with 371 spectral bands, spatial dimensions of 1600 × 212, and 16 bits per pixel, every 0.67 s. A second example times series acquired during a rooftop experiment conducted on the Rochester Institute of Technology campus in August 2017 illustrates a second application, moving vehicle imaging, with 371 spectral bands, 16 bit dynamic range, and 1600 × 300 spatial dimensions every second.

The leaf area index (LAI) is a structural parameter that plays a major role in modeling the exchange of energy, carbon dioxide, water, and other matter between plants and their surrounding environment. Direct means of measuring LAI are time-intensive and laborious, making indirect LAI retrieval methods desirable. In this study, we developed a method for retrieving structural metrics of erectophile plants that relies on open-source structure from motion software. We achieved this using a novel goniometer system to simultaneously retrieve high-resolution digital imagery and bidirectional spectroscopy measurements of Spartina alterniflora in a laboratory environment during both peak and late growing season. Retrieved LAI metrics correlated well with directional measurements of the Modified Chlorophyll Absorption Ratio Index 2 (MCARI2). Across all planting densities, the magnitude of the MCARI2 exhibited a high degree of variance, which depended on sensor viewing angle. MCARI2 values in the peak growing season were significantly higher. This result suggests that the MCARI2 metric is sensitive to seasonal changes in plant growth and senescence. We observed a high correlation between the LAIcontact values and the Total MCARI2 metric for the peak growth state plantings of R ² = 0.9, suggesting that summing multiple directional measurements of MCARI2 potentially provides a more accurate assessment of LAI and compensates for observed variance in MCARI2 as a function of sensor orientation. Future work will focus on validating the observed effects across a broader range of plant species.

Change in the coastal zone is accelerating with external forcing by sea-level rise, nutrient loading, drought, and over-harvest, leading to significant stress on the foundation plant species of coastal salt marshes. The rapid evolution of marsh state induced by these drivers makes the ability to detect stressors prior to marsh loss important. However, field work in coastal salt marshes can be challenging due to limited access and their fragile nature. Thus, remote sensing approaches hold promise for rapid and accurate determination of marsh state across multiple spatial scales. In this study, we evaluated the use of remote sensing tools to detect three dominant stressors on Spartina alterniflora. We took advantage of a barrier island salt marsh chronosequence in Virginia, USA, where marshes of different ages and level of stressor exist side by side. We collected hyperspectral imagery of plants along with salinity, sediment redox potential, and foliar nitrogen content in the field. We also conducted a greenhouse study where we manipulated environmental conditions. We found that models developed for stressors based on plant spectral response correlated well with salinity and foliar nitrogen within the greenhouse and field data, but were not transferable from lab to field, likely due to the limited range of conditions explored within the greenhouse experiments and the coincidence of multiple stressors in the field. This study is an important step towards the development of a remote sensing tool for tracking of ecosystem development, marsh health, and future ecosystem services.