Victoria Hill

Biodiversity and Biogeography of the Lower Trophic Taxa of the Pacific Arctic Region: Sensitivities to Climate Change

The lower trophic level taxa underpin the marine ecosystems of the Pacific Arctic Region (PAR). Recent field observations indicate that range shifts, and changes in the relative abundance of particular taxa have occurred within the last decade. Here we provide a region wide survey of the diversity and distribution of viruses, bacteria, archaea, auto- and heterotrophic protists, as well as metazoan zooplankton and benthic organisms in the PAR. Our aim is to provide a foundation for the assessment of the changes within the lower trophic level taxa of the PAR and to document such change when possible. Sensitivities to the effects of climate change are also discussed. Our vision is to enable data-based predictions regarding ecological succession in the PAR under current climate scenarios, and to deepen our understanding regarding what the future holds for higher trophic level organisms and the carbon cycle.

Bathymetry Retrieval from Hyperspectral Imagery in the Very Shallow Water Limit: A Case Study from the 2007 Virginia Coast Reserve (VCR'07) Multi-Sensor Campaign

We focus on the validation of a simplified approach to bathymetry retrieval from hyperspectral imagery (HSI) in the very shallow water limit (less than 1–2 m), where many existing bathymetric LIDAR sensors perform poorly. In this depth regime, near infra-red (NIR) reflectance depends primarily on water depth (water absorption) and bottom type, with suspended constituents playing a secondary role. Our processing framework exploits two optimal regions where a simple model depending on bottom type and water depth can be applied in the very shallow limit. These two optimal spectral regions are at a local maximum in the near infra-red reflectance near 810 nm, corresponding to a local minimum in absorption, and a maximum in the first derivative of the reflectance near 720 nm. These two regions correspond to peaks in spectral correlation with bathymetry at these depths.

Very Shallow Water Bathymetry Retrieval from Hyperspectral Imagery at the Virginia Coast Reserve (VCR'07) Multi-Sensor Campaign

A number of institutions, including the Naval Research Laboratory (NRL), have developed look up tables for remote retrieval of bathymetry and in-water optical properties from hyperspectral imagery (HSI) [6]. For bathymetry retrieval, the lower limit is the very shallow water case (here defined as < 2m), a depth zone which is not well resolved by many existing bathymetric LIDAR sensors, such as SHOALS [4]. The ability to rapidly model these shallow water depths from HSI directly has potential benefits for combined HSI/LIDAR systems such as the Compact Hydrographic Airborne Rapid Total Survey (CHARTS) [10]. In this study, we focused on the validation of a near infra-red feature, corresponding to a local minimum in absorption (and therefore a local peak in reflectance), which can be correlated directly to bathymetry with a high degree of confidence. Compared to other VNIR wavelengths, this particular near-IR feature corresponds to a peak in the correlation with depth in this very shallow water regime, and this is a spectral range where reflectance depends primarily on water depth (water absorption) and bottom type, with suspended constituents playing a secondary role.

Detection of seagrass scars using sparse coding and morphological filter

We present a two-step algorithm for the detection of seafloor propeller seagrass scars in shallow water using panchromatic images. The first step is to classify image pixels into scar and non-scar categories based on a sparse coding algorithm. The first step produces an initial scar map in which false positive scar pixels may be present. In the second step, local orientation of each detected scar pixel is computed using the morphological directional profile, which is defined as outputs of a directional filter with a varying orientation parameter. The profile is then utilized to eliminate false positives and generate the final scar detection map. We applied the algorithm to a panchromatic image captured at the Deckle Beach, Florida using the WorldView2 orbiting satellite. Our results show that the proposed method can achieve <90% accuracy on the detection of seagrass scars.

Subsea LIDAR systems

Abstract: Oceanographic light detection and ranging (LIDAR) maps scattering layers, and relates their distributions to oceanographic processes. However, demonstrable links between this experimental technology and water column optical properties remain elusive. Theory shows the returned laser power (Pr) decays logarithmically as a function of depth-varying optical properties along the path of the beam. The decay slope is related to beam attenuation and diffuse attenuation coefficients, and the abundance of biogeochemical constituents, including phytoplankton. LIDAR quantification of phytoplankton distributions will improve predictions of ocean productivity, particularly in highly variable and rapidly changing polar environments. However, few observations exist to verify these relationships. Future ocean LIDAR development requires commercial systems capable of deployment in many environments to routinely profile the euphotic zone. Quantifying the distribution, abundance, and nature of scattering particles will improve estimates of ocean productivity, particle flux, and ocean biogeochemistry that are critical to understanding our changing ocean climate.

Light Availability and Phytoplankton Growth Beneath Arctic Sea Ice: Integrating Observations and Modeling

Observations of the seasonal light field in the upper Arctic Ocean are critical to understanding the impacts of changing Arctic ice conditions on phytoplankton growth in the water column. Here we discuss data from a new sensor system, deployed in seasonal ice cover north-east of Utqiaġvik, Alaska in March 2014. The system was designed to provide observations of light and phytoplankton biomass in the water column during the formation of surface melt ponds and the transition from ice to open water. Hourly observations of downwelling irradiance beneath the ice (at 2.9, 6.9, and 17.9 m depths) and phytoplankton biomass (at 2.9 m depth) were transmitted via Iridium satellite from 9 March to 10 November 2014. Evidence of an under-ice phytoplankton bloom (Chl a 8 mg m) was seen in June and July. Increases in light intensity observed by the buoy likely resulted from the loss of snow cover and development of surface melt ponds. A bio-optical model of phytoplankton production supported this probable trigger for the rapid onset of under-ice phytoplankton growth. Once under-ice light was no longer a limiting factor for photosynthesis, open water exposure almost marginally increased daily phytoplankton production compared to populations that remained under the adjacent ice. As strong effects of climate change continue to be documented in the Arctic, the insight derived from autonomous buoys will play an increasing role in understanding the dynamics of primary productivity where ice and cloud cover limit the utility of ocean color satellite observations.