This work describes a study using multi-view hyperspectral imagery to retrieve sediment filling factor through inversion of a modified version of the Hapke radiative transfer model. We collected multi-view hyperspectral imagery from a hyperspectral imaging system mounted atop a telescopic mast from multiple locations and viewing angles of a salt panne on a barrier island at the Virginia Coast Reserve Long-Term Ecological Research site. We also collected ground truth data, including sediment bulk density and moisture content, within the common field of view of the collected hyperspectral imagery. For samples below a density threshold for coherent effects, originally predicted by Hapke, the retrieved sediment filling factor correlates well with directly measured sediment bulk density ( R 2 = 0.85 ). The majority of collected samples satisfied this condition. The onset of the threshold occurs at significantly higher filling factors than Hapke’s predictions for dry sediments because the salt panne sediment has significant moisture content. We applied our validated inversion model to successfully map sediment filling factor across the common region of overlap of the multi-view hyperspectral imagery of the salt panne.

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.

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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.