Molly Reif

State of the Art Satellite and Airborne Marine Oil Spill Remote Sensing: Application to the BP Deepwater Horizon Oil Spill

Abstract The vast and persistent Deepwater Horizon (DWH) spill challenged response capabilities, which required accurate, quantitative oil assessment at synoptic and operational scales. Although experienced observers are a spill responses mainstay, few trained observers and confounding factors including weather, oil emulsification, and scene illumination geometry present challenges. DWH spill and impact monitoring was aided by extensive airborne and spaceborne passive and active remote sensing. Oil slick thickness and oil-to-water emulsion ratios are key spill response parameters for containment/cleanup and were derived quantitatively for thick (>xa00.1xa0mm) slicks from AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) data using a spectral library approach based on the shape and depth of near infrared spectral absorption features. MODIS (Moderate Resolution Imaging Spectroradiometer) satellite, visible-spectrum broadband data of surface-slick modulation of sunglint reflection allowed extrapolation to the total slick. A multispectral expert system used a neural network approach to provide Rapid Response thickness class maps. Airborne and satellite synthetic aperture radar (SAR) provides synoptic data under all-sky conditions; however, SAR generally cannot discriminate thick (>xa0100xa0μm) oil slicks from thin sheens (to 0.1xa0μm). The UAVSARs (Uninhabited Aerial Vehicle SAR) significantly greater signal-to-noise ratio and finer spatial resolution allowed successful pattern discrimination related to a combination of oil slick thickness, fractional surface coverage, and emulsification. In situ burning and smoke plumes were studied with AVIRIS and corroborated spaceborne CALIPSO (Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation) observations of combustion aerosols. CALIPSO and bathymetry lidar data documented shallow subsurface oil, although ancillary data were required for confirmation. Airborne hyperspectral, thermal infrared data have nighttime and overcast collection advantages and were collected as well as MODIS thermal data. However, interpretation challenges and a lack of Rapid Response Products prevented significant use. Rapid Response Products were key to response utilization—data needs are time critical; thus, a high technological readiness level is critical to operational use of remote sensing products. DWHs experience demonstrated that development and operationalization of new spill response remote sensing tools must precede the next major oil spill.

A Spatially Explicit, Multi-Criteria Decision Support Model for Loggerhead Sea Turtle Nesting Habitat Suitability: A Remote Sensing-Based Approach

Nesting habitat for the federally endangered loggerhead sea turtle (Caretta caretta) were designated as critical in 2014 for beaches along the Atlantic Coast and Gulf of Mexico. Nesting suitability is routinely determined based on site specific information. Given the expansive geographic location of the designated critical C. caretta nesting habitat and the highly dynamic coastal environment, understanding nesting suitability on a regional scale is essential for monitoring the changing status of the coast as a result of hydrodynamic forces and maintenance efforts. The increasing spatial resolution and temporal frequency of remote sensing data offers the opportunity to study this dynamic environment on a regional scale. Remote sensing data were used as input into the spatially-explicit, multi-criteria decision support model to determine nesting habitat suitability. Results from the study indicate that the morphological parameters used as input into the model are well suited to provide a regional level approach with the results from the optimized model having sensitivity and detection prevalence values greater than 80% and the detection rate being greater than 70%. The approach can be implemented in various geographic locations to better communicate priorities and evaluate management strategies as a result of changes to the dynamic coastal environment.

Post-Katrina Land-Cover, Elevation, and Volume Change Assessment along the South Shore of Lake Pontchartrain, Louisiana, U.S.A.

Abstract Advances in remote-sensing technology have led to its increased use for posthurricane disaster response and assessment; however, the use of the technology is underutilized in the recovery phase of the disaster management cycle. This study illustrates an example of a postdisaster recovery assessment by detecting coastal land cover, elevation, and volume changes using 3 years of post-Katrina hyperspectral and light detection and ranging data collected along the south shore of Lake Pontchartrain, Louisiana. Digital elevation models and basic land-cover classifications were generated for a 34-km2 study area for 2005, 2006, and 2007. A change detection method was used to assess postdisaster land-cover, elevation, and volume changes. Results showed that the vegetation classes had area increases, whereas bare ground/roads and structures classes had area decreases. Overall estimated volume changes included a net volume decrease of 1.6 × 106 m3 in 2005 to 2006 and a net volume decrease of 2.1 × 106 m3 in 2006 to 2007 within the study area. More specifically, low vegetation and bare ground/roads classes had net volume increases, whereas medium and tall vegetation and structures classes had net volume decreases. These changes in land cover, elevation, and volume illustrate some of the major physical impacts of the disaster and ensuing recovery. This study demonstrates an innovative image fusion approach to assess physical changes and postdisaster recovery in a residential, coastal environment.

Evaluating the portability of satellite derived chlorophyll- a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations

This study evaluated the performances of twenty-nine algorithms that use satellite-based spectral imager data to derive estimates of chlorophyll-a concentrations that, in turn, can be used as an indicator of the general status of algal cell densities and the potential for a harmful algal bloom (HAB). The performance assessment was based on making relative comparisons between two temperate inland lakes: Harsha Lake (7.99u202fkm2) in Southwest Ohio and Taylorsville Lake (11.88u202fkm2) in central Kentucky. Of interest was identifying algorithm-imager combinations that had high correlation with coincident chlorophyll-a surface observations for both lakes, as this suggests portability for regional HAB monitoring. The spectral data utilized to estimate surface water chlorophyll-a concentrations were derived from the airborne Compact Airborne Spectral Imager (CASI) 1500 hyperspectral imager, that was then used to derive synthetic versions of currently operational satellite-based imagers using spatial resampling and spectral binning. The synthetic data mimics the configurations of spectral imagers on current satellites in earths orbit including, WorldView-2/3, Sentinel-2, Landsat-8, Moderate-resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS). High correlations were found between the direct measurement and the imagery-estimated chlorophyll-a concentrations at both lakes. The results determined that eleven out of the twenty-nine algorithms were considered portable, with r2 values greater than 0.5 for both lakes. Even though the two lakes are different in terms of background water quality, size and shape, with Taylorsville being generally less impaired, larger, but much narrower throughout, the results support the portability of utilizing a suite of certain algorithms across multiple sensors to detect potential algal blooms through the use of chlorophyll-a as a proxy. Furthermore, the strong performance of the Sentinel-2 algorithms is exceptionally promising, due to the recent launch of the second satellite in the constellation, which will provide higher temporal resolution for temperate inland water bodies. Additionally, scripts were written for the open-source statistical software R that automate much of the spectral data processing steps. This allows for the simultaneous consideration of numerous algorithms across multiple imagers over an expedited time frame for the near real-time monitoring required for detecting algal blooms and mitigating their adverse impacts.

POST-KATRINA LAND CHANGE ASSESSMENT ALONG THE SOUTH SHORE OF LAKE PONTCHARTRAIN, LOUISIANA, USA: A FOUR-YEAR PERSPECTIVE, 2005-2009

In response to hurricane Katrina, image acquisition was extensive to survey damage caused by wind, flood, and storm surge. Less prevalent, however, is the use of remotely sensed imagery and Geographic Information Systems (GIS) for assessment of long-term recovery. Building upon a previous study, the current work extends further into the recovery phase of the disaster management cycle and assesses land cover, elevation, and volume changes in a 20 square kilometer area along the south shore of Lake Pontchartrain, Louisiana, 2005 to 2009. Using an innovative hyperspectral and lidar fusion approach to develop basic land cover classification as well as Digital Elevation Models (DEMs), change detection revealed that some negative trends in land cover and net volume estimates immediately following the disaster in 2005 to 2007 may be either losing pace or reversing for more positive signs of recovery in 2007 to 2009.