John C. Brock

Sea-cliff erosion as a function of beach changes and extreme wave runup during the 1997–1998 El Niño

Abstract Over time scales of hundreds to thousands of years, the net longshore sand transport direction along the central California coast has been driven to the south by North Pacific winter swell. In contrast, during the El Nino winter of 1997–1998, comparisons of before and after airborne lidar surveys showed sand was transported from south to north and accumulated on the south sides of resistant headlands bordering pocket beaches. This resulted in significant beach erosion at the south ends of pocket beaches and deposition in the north ends. Coincident with the south-to-north redistribution of sand, shoreline morphology became prominently cuspate with longshore wavelengths of 400–700 m. The width and elevation of beaches were least where maximum shoreline erosion occurred, preferentially exposing cliffs to wave attack. The resulting erosional hotspots typically were located in the embayments of giant cusps in the southern end of the pocket beaches. The observed magnitude of sea cliff retreat, which reached 14 m, varied with the number of hours that extreme wave runup exceeded certain thresholds representing the protective capacity of the beach during the El Nino winter. A threshold representing the width of the beach performed better than a threshold representing the elevation of the beach. The magnitude of cliff erosion can be scaled using a simple model based on the cross-shore distance that extreme wave runup exceeded the pre-winter cliff position. Cliff erosion appears to be a balance between terrestrial mass wasting processes, which tend to decrease the cliff slope, and wave attack, which removes debris and erodes the cliff base increasing the cliff slope.

Prospects for quantifying structure, floristic composition and species richness of tropical forests

Airborne spectral and light detection and ranging (lidar) sensors have been used to quantify biophysical characteristics of tropical forests. Lidar sensors have provided high-resolution data on forest height, canopy topography, volume, and gap size; and provided estimates on number of strata in a forest, successional status of forests, and above-ground biomass. Spectral sensors have provided data on vegetation types, foliar biochemistry content of forest canopies, tree and canopy phenology, and spectral signatures for selected tree species. A number of advances are theoretically possible with individual and combined spectral and lidar sensors for the study of forest structure, floristic composition and species richness. Delineating individual canopies of over-storey trees with small footprint lidar and discrimination of tree architectural types with waveform distributions is possible and would provide scientists with a new method to study tropical forest structure. Combined spectral and lidar data can be used to identify selected tree species and identify the successional status of tropical forest fragments in order to rank forest patches by levels of species richness. It should be possible in the near future to quantify selected patterns of tropical forests at a higher resolution than can currently be undertaken in the field or from space.

A Multiscale Analysis of Coral Reef Topographic Complexity Using Lidar-Derived Bathymetry

Abstract Coral reefs represent one of the most irregular substrates in the marine environment. This roughness or topographic complexity is an important structural characteristic of reef habitats that affects a number of ecological and environmental attributes, including species diversity and water circulation. Little is known about the range of topographic complexity exhibited within a reef or between different reef systems. The objective of this study was to quantify topographic complexity for a 5-km x 5-km reefscape along the northern Florida Keys reef tract, over spatial scales ranging from meters to hundreds of meters. The underlying dataset was a 1-m spatial resolution, digital elevation model constructed from lidar measurements. Topographic complexity was quantified using a fractal algorithm, which provided a multi-scale characterization of reef roughness. The computed fractal dimensions (D) are a measure of substrate irregularity and are bounded between values of 2 and 3. Spatial patterns in D were positively correlated with known reef zonation in the area. Landward regions of the study site contain relatively smooth (D ≈ 2.35) flat-topped patch reefs, which give way to rougher (D ≈ 2.5), deep, knoll-shaped patch reefs. The seaward boundary contains a mixture of substrate features, including discontinuous shelf-edge reefs, and exhibits a corresponding range of roughness values (2.28 ≤ D ≤ 2.61).

Economic Vulnerability to Sea-Level Rise Along the Northern U.S. Gulf Coast

ABSTRACT Thatcher, C.A.; Brock J.C., and Pendleton, E.A., 2013. Economic Vulnerability to Sea-Level Rise Along the Northern U.S. Gulf Coast. In: Brock, J.C.; Barras, J.A., and Williams, S.J. (eds.), Understanding and Predicting Change in the Coastal Ecosystems of the Northern Gulf of Mexico, Journal of Coastal Research, Special Issue No. 63, pp. 234–243, Coconut Creek (Florida), ISSN 0749–0208. The northern Gulf of Mexico coast of the United States has been identified as highly vulnerable to sea-level rise, based on a combination of physical and societal factors. Vulnerability of human populations and infrastructure to projected increases in sea level is a critical area of uncertainty for communities in the extremely low-lying and flat northern gulf coastal zone. A rapidly growing population along some parts of the northern Gulf of Mexico coastline is further increasing the potential societal and economic impacts of projected sea-level rise in the region, where observed relative rise rates range from 0.75 to 9.95 mm per year on the Gulf coasts of Texas, Louisiana, Mississippi, Alabama, and Florida. A 1-m elevation threshold was chosen as an inclusive designation of the coastal zone vulnerable to relative sea-level rise, because of uncertainty associated with sea-level rise projections. This study applies a Coastal Economic Vulnerability Index (CEVI) to the northern Gulf of Mexico region, which includes both physical and economic factors that contribute to societal risk of impacts from rising sea level. The economic variables incorporated in the CEVI include human population, urban land cover, economic value of key types of infrastructure, and residential and commercial building values. The variables are standardized and combined to produce a quantitative index value for each 1-km coastal segment, highlighting areas where human populations and the built environment are most at risk. This information can be used by coastal managers as they allocate limited resources for ecosystem restoration, beach nourishment, and coastal-protection infrastructure. The study indicates a large amount of variability in index values along the northern Gulf of Mexico coastline, and highlights areas where long-term planning to enhance resiliency is particularly needed.

Accuracy assessment of a mobile terrestrial lidar survey at Padre Island National Seashore

The higher point density and mobility of terrestrial laser scanning (light detection and ranging (lidar)) is desired when extremely detailed elevation data are needed for mapping vertically orientated complex features such as levees, dunes, and cliffs, or when highly accurate data are needed for monitoring geomorphic changes. Mobile terrestrial lidar scanners have the capability for rapid data collection on a larger spatial scale compared with tripod-based terrestrial lidar, but few studies have examined the accuracy of this relatively new mapping technology. For this reason, we conducted a field test at Padre Island National Seashore of a mobile lidar scanner mounted on a sport utility vehicle and integrated with a position and orientation system. The purpose of the study was to assess the vertical and horizontal accuracy of data collected by the mobile terrestrial lidar system, which is georeferenced to the Universal Transverse Mercator coordinate system and the North American Vertical Datum of 1988. To accomplish the study objectives, independent elevation data were collected by conducting a high-accuracy global positioning system survey to establish the coordinates and elevations of 12 targets spaced throughout the 12 km transect. These independent ground control data were compared to the lidar scanner-derived elevations to quantify the accuracy of the mobile lidar system. The performance of the mobile lidar system was also tested at various vehicle speeds and scan density settings (e.g. field of view and linear point spacing) to estimate the optimal parameters for desired point density. After adjustment of the lever arm parameters, the final point cloud accuracy was 0.060 m (east), 0.095 m (north), and 0.053 m (height). The very high density of the resulting point cloud was sufficient to map fine-scale topographic features, such as the complex shape of the sand dunes.

Multi-Scale Remote Sensing of Coral Reefs

Institut de Recherche pour le Developpement, BP A5, 98848 Noumea, New Caledonia University of Hawaii, School of Ocean and Earth Science and Technology, Hawaii Institute of Marine Biology, P.O. Box 1346, Kaneohe, HI, 96744 USA Institute for Marine Remote Sensing, College of Marine Science, University of South Florida, 140 7th Ave. South, St Petersburg, FL, 33701 USA USGS Center for Coastal and Watershed Studies, 600 4th Street South, St. Petersburg, FL, 33701 USA

Barrier Island Morphodynamic Classification Based on Lidar Metrics for North Assateague Island, Maryland

Abstract In order to reap the potential of airborne lidar surveys to provide geological information useful in understanding coastal sedimentary processes acting on various time scales, a new set of analysis methods are needed. This paper presents a multi-temporal lidar analysis of north Assateague Island, Maryland, and demonstrates the calculation of lidar metrics that condense barrier island morphology and morphological change into attributed linear features that may be used to analyze trends in coastal evolution. The new methods proposed in this paper are also of significant practical value, because lidar metric analysis reduces large volumes of point elevations into linear features attributed with essential morphological variables that are ideally suited for inclusion in Geographic Information Systems. A morphodynamic classification of north Assategue Island for a recent 10 month time period that is based on the recognition of simple patterns described by lidar change metrics is presented. Such morphodynamic classification reveals the relative magnitude and the fine scale alongshore variation in the importance of coastal changes over the study area during a defined time period. More generally, through the presentation of this morphodynamic classification of north Assateague Island, the value of lidar metrics in both examining large lidar data sets for coherent trends and in building hypotheses regarding processes driving barrier evolution is demonstrated.

Land Loss Due to Recent Hurricanes in Coastal Louisiana, U.S.A.

ABSTRACT Palaneasu-Lovejoy, M.; Kranenburg, C.; Barras, J.A., and Brock, J.C., 2013. Land loss due to recent hurricanes in coastal Louisiana, U.S.A.. In: Brock, J.C.; Barras, J.A., and Williams, S.J. (eds.), Understanding and Predicting Change in the Coastal Ecosystems of the Northern Gulf of Mexico, Journal of Coastal Research, Special Issue No. 63, pp. 97–109, Coconut Creek (Florida), ISSN 0749-0208. The aim of this study is to improve estimates of wetland land loss in two study regions of coastal Louisiana, U.S.A., due to the extreme storms that impacted the region between 2004 and 2009. The estimates are based on change-detection-mapping analysis that incorporates pre and postlandfall (Hurricanes Katrina, Rita, Gustav, and Ike) fractional-water classifications using a combination of high-resolution (<5 m) QuickBird, IKONOS, and GeoEye-1, and medium-resolution (30 m) Landsat Thematic Mapper satellite imagery. This process was applied in two study areas: the Hackberry area located in the southwestern part of chenier plain that was impacted by Hurricanes Rita (September 24, 2005) and Ike (September 13, 2008), and the Delacroix area located in the eastern delta plain that was impacted by Hurricanes Katrina (August 29, 2005) and Gustav (September 1, 2008). In both areas, effects of the hurricanes include enlargement of existing bodies of open water and erosion of fringing marsh areas. Surge-removed marsh was easily identified in stable marshes but was difficult to identify in degraded or flooded marshes. Persistent land loss in the Hackberry area due to Hurricane Rita was approximately 5.8% and increased by an additional 7.9% due to Hurricane Ike, although this additional area may yet recover. About 80% of the Hackberry study area remained unchanged since 2003. In the Delacroix area, persistent land loss due to Hurricane Katrina measured approximately 4.9% of the study area, while Hurricane Gustav caused minimal impact of 0.6% land loss by November 2009. Continued recovery in this area may further erase Hurricane Gustavs impact in the absence of new storm events.

Utility of Shallow-Water ATRIS Images in Defining Biogeologic Processes and Self-Similarity in Skeletal Scleractinia, Florida Reefs

Abstract A recently developed remote-sensing instrument acquires high-quality digital photographs in shallow-marine settings within water depths of 15 m. The technology, known as the Along-Track Reef-Imaging System, provides remarkably clear, georeferenced imagery that allows visual interpretation of benthic class (substrates, organisms) for mapping coral reef habitats, as intended. Unforeseen, however, are functions new to the initial technologic purpose: interpretable evidence for real-time biogeologic processes and for perception of scaled-up skeletal self-similarity of scleractinian microstructure. Florida reef sea trials lacked the grid structure required to map contiguous habitat and submarine topography. Thus, only general observations could be made relative to times and sites of imagery. Degradation of corals was nearly universal; absence of reef fish was profound. However, ∼1% of more than 23,600 sea-trial images examined provided visual evidence for local environs and processes. Clarity in many images was so exceptional that small tracks left by organisms traversing fine-grained carbonate sand were visible. Other images revealed a compelling sense, not yet fully understood, of the microscopic wall structure characteristic of scleractinian corals. Conclusions drawn from classifiable images are that demersal marine animals, where imaged, are oblivious to the equipment and that the technology has strong capabilities beyond mapping habitat. Imagery acquired along predetermined transects that cross a variety of geomorphic features within depth limits will (1) facilitate construction of accurate contour maps of habitat and bathymetry without need for ground-truthing, (2) contain a strong geologic component of interpreted real-time processes as they relate to imaged topography and regional geomorphology, and (3) allow cost-effective monitoring of regional-and local-scale changes in an ecosystem by use of existing-image global-positioning system coordinates to re-image areas. Details revealed in the modern setting have taphonomic implications for what is often found in the geologic record.

An efficient method for change detection of soil, vegetation and water in the Northern Gulf of Mexico wetland ecosystem

Mapping and monitoring wetland ecosystems over large geographic areas based on remote sensing is challenging because of the spatial and spectral complexities of the inherent ecosystem dynamics. The main objective of this research was to develop and evaluate a new method for detecting and quantifying wetland changes in the Northern Gulf of Mexico (NGOM) region using multitemporal, multispectral, and multisensor remotely sensed data. The abundance of three land- cover types (water, vegetation, and soil) was quantified for each Landsat 30 m pixel for 1987, 2004, 2005, and 2006 using a regression tree algorithm. The performance of the algorithm was evaluated using an independent reference data set derived from a high-resolution QuickBird image, and several statistics including average error (AE), relative error (RE), and the Pearson correlation coefficient (r). For per-pixel percentage estimation, the AE is under 10% for water prediction, 9.5–11.4% for vegetation, and 9–11.1% for soil. The correlation coefficients between predicted and reference data range from 0.90 to 0.96 for water, from 0.80 to 0.89 for vegetation, and from 0.79 to 0.86 for soil. The high accuracy achieved by this method is attributed to the high quality of training data and the rigorous calibrations applied to multisensor and multitemporal satellite imagery. Based on the multitemporal estimation of the three land-cover components, spatial and temporal changes of the land-cover types from 1987 to 2006 were quantified and analysed. The study demonstrates that the method provided useful information on the abundance and changes of the key land-cover types in the NGOM region where long-term disturbances and episodic events occurred. Such information is valuable for monitoring land and vegetation loss and recovery processes, and for understanding possible drivers of the coastal wetland evolution in the region.