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1 December 2016 Creating a Coastal National Elevation Database (CoNED) for Science and Conservation Applications
Cindy A. Thatcher, John C. Brock, Jeffrey J. Danielson, Sandra K. Poppenga, Dean B. Gesch, Monica E. Palaseanu-Lovejoy, John A. Barras, Gayla A. Evans, Ann E. Gibbs
Author Affiliations +
Abstract

Thatcher, C.A.; Brock, J.C.; Danielson, J.J.; Poppenga, S.K.; Gesch, D.B.; Palaseanu-Lovejoy, M.E.; Barras, J.A.; Evans, G.A., and Gibbs, A.E., 2016. Creating a Coastal National Elevation Database (CoNED) for science and conservation applications. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 64–74. Coconut Creek (Florida), ISSN 0749-0208.

The U.S. Geological Survey is creating the Coastal National Elevation Database, an expanding set of topobathymetric elevation models that extend seamlessly across coastal regions of high societal or ecological significance in the United States that are undergoing rapid change or are threatened by inundation hazards. Topobathymetric elevation models are raster datasets useful for inundation prediction and other earth science applications, such as the development of sediment-transport and storm surge models. These topobathymetric elevation models are being constructed by the broad regional assimilation of numerous topographic and bathymetric datasets, and are intended to fulfill the pressing needs of decision makers establishing policies for hazard mitigation and emergency preparedness, coastal managers tasked with coastal planning compatible with predictions of inundation due to sea-level rise, and scientists investigating processes of coastal geomorphic change. A key priority of this coastal elevation mapping effort is to foster collaborative lidar acquisitions that meet the standards of the USGS National Geospatial Program's 3D Elevation Program, a nationwide initiative to systematically collect high-quality elevation data. The focus regions are located in highly dynamic environments, for example in areas subject to shoreline change, rapid wetland loss, hurricane impacts such as overwash and wave scouring, and/or human-induced changes to coastal topography.

INTRODUCTION

During the coming decades, coastlines will respond to widely predicted sea-level rise (Wong et al., 2014), and detailed, accurate coastal elevation information is a key variable in understanding the likely impacts of this global hazard. Vulnerability maps that depict regions prone to flooding as sea level rises are essential to planners and managers responsible for mitigating the associated risks and costs to both human communities and ecosystems. Accurate, comprehensive, and up-to-date elevation data are needed to develop inundation maps, and to inform coastal restoration and flood risk reduction efforts along the highly dynamic and low-lying coastal regions of the United States.

The 3D Elevation Program (3DEP) bare earth digital elevation model (DEM) layer (formerly known as the National Elevation Database) is the primary topographic data product produced and distributed by the U.S. Geological Survey (USGS; Gesch, 2007). 3DEP is derived from diverse data sources, and provides a seamless raster elevation model for the conterminous United States, Alaska, Hawaii, and United States island territories. As the authoritative topography layer of The National Map (Kelmelis et al., 2003) of the United States, the 3DEP DEM enables a broad range of earth science studies and conservation applications. Starting with Mobile Bay, Alabama, as a pilot study site in 2010, the USGS began geographically extending and enhancing the 3DEP DEM within the coastal zone by creating integrated topobathymetric digital elevation models, known as the Coastal National Elevation Database (CoNED).

Topobathymetric digital elevation models (TBDEMs) are three-dimensional (3D) merged renderings of both topography (land elevation) and bathymetry (water depth) that provide a seamless integrated elevation product (Gesch and Wilson, 2001) useful for inundation prediction and other earth science applications, such as the development of sediment-transport and storm surge models. The National Research Council (2004) first recommended collaborative efforts among federal agencies to produce such seamless merged products for all U.S. coastal regions, and the Obama Administration's National Ocean Policy Implementation Plan (2013) recently renewed the call for a seamless representation of coastal elevations to improve coastal change analysis products. The CoNED TBDEMs address these science requirements for improved coastal elevation information. The goal of these efforts is to create TBDEMs at selected sites of high scientific interest and/or hazard vulnerability, and to extend cross-shoreline data assimilation by developing enhanced elevation products designed for coastal scientific applications within an expanding set of geographic regions.

A large component of TBDEM development focuses on updating and improving existing elevation data using airborne light detection and ranging (lidar), an active remote sensing technique used to acquire elevation data with a high spatial resolution over large areas. The minimum vertical accuracy standards for lidar acquired for the 3DEP initiative specify a vertical error of 10 cm RMSE or better, and a maximum nominal pulse spacing of 0.7 m (Sugarbaker et al., 2014). Lidar provides the regional topographic detail required for accurately mapping elevation in low-lying coastal regions where even small topographic variations can influence the spatial extent and magnitude of coastal hazards such as inundation from riverine and tidal flooding, storm surge, and sea-level rise.

A priority of this coastal elevation mapping effort is to foster collaborative lidar acquisitions that meet the standards of the USGS National Geospatial Program's (NGP) 3DEP (Snyder, 2013), a nationwide initiative to systematically collect high-quality lidar data. The NGP is partnering with the USGS Coastal Marine and Geology Program (CMGP) to acquire topographic lidar in coastal areas for inclusion in coastal topobathymetric models. The focus regions are coastal areas subject to shoreline change, rapid wetland loss, and hurricane impacts such as overwash and wave scouring. Human activities such as beach nourishment projects and the construction of artificial dunes can also result in changes to coastal topography. In these rapidly changing environments, up-to-date topobathymetric models are required to support the development of hazard mitigation policies and emergency preparedness, coastal redevelopment planning, inundation analyses, and studies of coastal change. Periodic updates are planned for the regional TBDEMs to allow the most recent and best available data to be incorporated in accord with the 3DEP 8-year nationwide lidar acquisition planning cycle or more frequently for coastal reaches where extreme storms or other events result in significant coastal change.

Regional TBDEMs also provide essential base information for comprehensive marine planning, a means to implement an integrated, ecosystem-based decision-making process on activities in the ocean, coasts, and Great Lakes (National Oceanic and Atmospheric Administration [NOAA], 2014a). Comprehensive marine planning resolves conflicts among differing resource uses (i.e., energy extraction, fishing, shipping corridors, aquatic wildlife habitat) by reliance on robust science, data, and mapping tools that are a focus of the National Ocean Policy Implementation Plan (National Ocean Council, 2013). These efforts rely upon integrating ocean and coastal data to populate innovative visualization and decision-support tools, and thus regional TBDEMs fulfill a fundamental need of marine planning for the integration of multiple-source cross-ecosystem elevation data.

CoNED builds upon earlier work (Gesch and Wilson, 2001; Tyler et al., 2007) aimed at the creation of robust integrated topobathymetric models, and extends across the land-water interface by seamlessly merging subaerial topography with adjacent submerged bathymetry. In selected focal areas, the DEMs will incorporate the following additional characteristics:

  • Hydrologic-enforcement of anthropogenic features such as culverts and bridges within DEMs (to remove artificial impediments to enable the correct modeling of downslope flow) in selected flood-prone coastal watersheds

  • Enhanced vertical accuracy and/or enhanced spatial resolution within shoreline-adjacent extreme inundation hazard regions and vulnerable urban areas

  • Multitemporal topobathymetric coverage over coastal subregions experiencing rapid geomorphic change

Interagency Collaboration

The USGS works through the National Science and Technology Council Interagency Working Group on Ocean and Coastal Mapping (IWG-OCM) to ensure close cooperation with NOAA, the U.S. Army Corps of Engineers (USACE), and other federal agencies on coastal mapping activities. For example, the IWG-OCM National Coastal Mapping Strategy, co-authored by a multi-agency interdisciplinary team, sets forth quality-level definitions for topographic and bathymetric lidar to establish common data standards and to facilitate the interagency coordination of coastal topographic and bathymetric surveys (Parrish et al., in draft). To reduce data acquisition costs and redundancy, the USGS works with NOAA, USACE, and other federal agencies to plan lidar and bathymetry surveys using a NOAA-sponsored web mapping application ( http://www.seasketch.org/#projecthomepage/5272840f6ec5f42d210016e4).

The USGS is collaborating with NOAA's National Centers for Environmental Information (NCEI) on the development of a common methodology and multi-resolution spatial framework for creating integrated topobathymetric elevation models. Focus areas for this collaboration include the northern Gulf of Mexico Coast and the Hurricane Sandy impact region. Hurricane Sandy was a devastating storm with a width of 1,600 km and storm tides greater than 5.8 m as it made landfall in New Jersey in October 2012 (Buxton et al., 2013). This storm was particularly damaging because it impacted some of the most densely populated areas of the United States, including the New York City metropolitan area. In the aftermath of the storm, coastal scientists, managers, and stakeholders recognized the need for comprehensive onshore and offshore elevation data to serve as a baseline to support redevelopment planning, emergency preparedness and disaster response efforts, and hazard mitigation efforts (USGS, 2013). In response, an interagency effort to survey coastal waters, shorelines, and coastal watersheds has resulted in large amounts of new geospatial data for the Hurricane Sandy impact region including airborne topographic and topobathymetric lidar, and ship-mounted acoustic side-scan and multibeam sonar.

METHODS

The principal methodology for developing integrated topobathymetric elevation models includes the compilation and collection of elevation and bathymetric datasets, mass processing of these datasets, and spatial integration of disparate datasets to create the seamless fused elevation model. Details about the methods are found in Danielson et al. (2016).

In preparing to construct topobathymetric elevation models, the first step is to undertake comprehensive spatial gap analyses of the coverage, age, specifications, and quality of lidar and bathymetric datasets across the study area. Through this process, the USGS also fosters the targeted collection of new elevation datasets by 1) sharing the results of the spatial “gap” analyses to guide future investments in coastal mapping, and 2) facilitating the development of lidar acquisition partnerships through involvement with the USGS 3DEP and IWG-OCM member agencies.

To promote consistency among topobathymetric models regardless of spatial scale, the USGS is collaborating with NOAA NCEI on the development of a common methodology for creating integrated elevation models. NCEI has a long history of developing topobathymetric elevation models at a relatively coarse scale (spatial resolutions ranging from 1/3 arc-second [~10 m] to 36 arc-seconds [~1 km]) for science applications such as tsunami forecasting and simulation models (NOAA, 2014b). NCEI also serves as the long-term archive and access point for the hydrographic data for the United States obtained by various acoustic sonar mapping methods ( http://maps.ngdc.noaa.gov/viewers/bathymetry/). NCEI compiles offshore bathymetry data for coastal and open-ocean areas, whereas USGS is compiling bathymetry data for other open-water areas in the coastal zone, such as estuaries and river channels, and subaerial topographic data across broad coastal zones. One long-term goal of this interagency collaboration is to develop methods, data structures, multi-resolution spatial frameworks, geospatial tools, and best practices that ensure high-quality, consistent topobathymetric elevation data are available for coastal regions of the United States, regardless of the type of source data or the agency of origin (Eakins and Grothe, 2014).

The increasing availability of water penetrating, green-laser topobathymetric lidar instruments has also resulted in improved capability to accurately map across the land-water interface within the intertidal zone. The USGS Experimental Advanced Airborne Research Lidar (EAARL-B; Wright et al., 2014a) and the USACE Coastal Zone Mapping and Imaging Lidar (CZMIL; Tuell, Barbor, and Wozencraft, 2010) are examples of mapping systems that provide high-resolution topobathymetric lidar data. These instruments can acquire land elevation and water-depth data simultaneously, which results in data that are ideal inputs for topobathymetric elevation models. Recent advancements demonstrated by the EAARL-B and CZMIL mapping systems, such as higher spatial point densities and greater depth penetration than earlier topobathymetric lidar systems, have resulted in improved ability to map submerged topography in shallow, moderately turbid waters. These lidar instruments are especially useful as they often fill a key gap in mapping shallow water zones where boat-based acoustic surveys are not possible or practical. However, the narrow surf zone continues to be a challenge to map with topobathymetric lidar because air bubbles and suspended sediment caused by breaking waves reduce the ability of the laser to detect the bottom (Monfort and Lippman, 2011). Field surveys of the surf zone using precision real-time kinematic (RTK) Global Positioning Systems (GPS) are one method to map bottom elevations within this narrow gap.

To maximize the scientific utility of the topobathymetric models, critical information about the original data used to create the resulting TBDEMs is provided to the end-users. Spatially referenced metadata is produced, consisting of a group of geospatial polygons that represent the spatial footprint of each original data source with an associated attribute table containing information such as the source of the original data, resolution, acquisition date, vertical accuracy, and data type (topographic lidar, bathymetric lidar, multi-beam bathymetry, single-beam bathymetry, etc.). The CoNED spatially referenced metadata follow the approach first developed and used for the National Elevation Dataset (NED; Gesch, 2007), which allows every elevation value to be traced directly back to its original source.

End users of elevation models consider the accuracy of the elevation values as a primary factor in determining the suitability of the data for a specific application (Gesch, Oimoen, and Evans, 2014). Although the TBDEMs inherit the accuracy of the source data (Gesch, Oimoen, and Evans, 2014), the integration process alters the source data in order to have smooth transitions between the raster data sources or during the process of interpolating point data sources to rasters. Therefore, an accuracy assessment of the TBDEM is conducted by comparing it with accurate, independent reference data. NOAA National Geodetic Survey (NGS) has developed more than 25,000 highly accurate control points that it uses to create NGS geoid models (NGS, 2012a). Another source of control points is the Louisiana Coastal Protection and Restoration Authority's (LACPRA) network of benchmarks. These points are used as an independent comparison dataset with which to assess the absolute vertical accuracy of the TBDEMs in terms of root mean square error (RMSE; Gesch, Oimoen, and Evans, 2014; Maune, Maitra, and McKay 2007). The USGS also collects elevation data with survey-grade GPS equipment on the coast and in adjacent shallow water for additional comparison data to independently quantify the accuracy of recent lidar surveys and of the integrated TBDEMs.

RESULTS

For selected regions, the USGS is developing enhanced elevation products such as hydro-enforced topobathymetric models, high-resolution and high-accuracy elevation models in flood-prone urban areas, and multitemporal elevation models that are optimized for geomorphic change analyses. The focus areas include Mobile Bay, Alabama; San Francisco Bay, California; the Northern Gulf of Mexico coast; the Outer Banks in North Carolina; the Hurricane Sandy impact region; and the Alaska North Slope.

Hydro-enforced Topobathymetric Models

The USGS is developing hydro-enforced TBDEMs for use in riverine flow and storm-surge modeling in the Barnegat Bay, New Jersey, estuary and along a section of the Delaware River watershed in New Jersey and Pennsylvania (Poppenga and Worstell, 2016). High-resolution elevation data derived from lidar are an important base layer used in coastal hydrologic applications. However, lidar-derived DEMs often present challenges for hydrologic modeling, as they typically include raised features such as roads, railroad beds, and bridges that artificially act as dams that impede the natural flow of surface water (Poppenga et al., 2012) in DEM-based hydrologic models. Hydro-enforcement is needed to modify the elevations of artificial impediments (such as roads overlaying culverts) to simulate how man-made drainage structures such as culverts or bridges allow continuous downslope flow (Poppenga et al., 2014). Hydro-enforced topobathymetric data are essential for more realistic and accurate modeling of riverine flow and storm-surge modeling in coastal regions, water inundation mapping and hydrodynamic modeling, climate change studies of coastal sea-level rise in vulnerable land-water interfaces along coastal zones, as well as other earth science applications, such as the development of sediment-transport and debris flow modeling.

Enhanced Elevation Models in Inundation Hazard Zones and Vulnerable Urban Areas

DEMs are being developed with enhancements including improved vertical accuracy, very high spatial resolution (< 1 m), and hydro-enforcement for selected vulnerable urban areas in the Hurricane Sandy impact region. An enhanced topographic model is being developed for Staten Island in New York City, NY, which will enable improved inundation prediction from sea-level rise, storm surge, and tidal flooding.

Field measurements using both precision RTK GPS and a mobile terrestrial lidar scanner were collected in April 2014 in the New York City metropolitan area that will be used to calibrate and validate the regional topobathymetric elevation models in the Hurricane Sandy region. The high-accuracy (2–3 cm root mean square error) field survey was conducted along seawalls, artificial dunes, and other topographic features that impede or direct the flow of water through the urban landscape. GPS data were also collected along shore-normal transects during low tide along the coastline in New York City and Long Island, extending out into the surf zone to a depth of approximately 1 m. The terrestrial lidar point cloud and GPS points will also be used as an independent dataset to quantify the accuracy of the airborne lidar point clouds and to evaluate the ability of aerial lidar to map narrow but important topographic features that may or may not be fully resolved in the airborne-lidar point clouds.

Multitemporal Elevation Models

A new multitemporal framework data structure was developed to stack independent lidar-based DEMs from multiple time periods efficiently and maintain pixel alignment between DEMs. New data structures and procedures needed to support multitemporal topobathymetric elevation models across geomorphically dynamic coastal reaches have been devised, using the Outer Banks in North Carolina as the pilot study area. Various federal and state agencies have collected 20 lidar elevation datasets within the Outer Banks study area from 1996 to 2012. An analysis of the temporal density of the source datasets for the Outer Banks revealed an average overlap of 10 surveys at any given location, with the highest temporal density along the Atlantic shoreline (Figure 1).

Figure 1.

Number of overlapping lidar surveys conducted from 1996 to 2012 that are contained within a multitemporal elevation model for the Outer Banks in North Carolina.

i1551-5036-76-sp1-64-f01.tif

The primary product is a multitemporal topographic/bathymetric raster elevation database, with edged-matched, pixel-aligned rasters compiled on a “best-available” basis that provide efficient access to a time series of lidar data for geomorphic change and coastal modeling applications. Each lidar dataset is gridded (interpolated) using triangular irregular networks (TINs). These overlapping rasters are incorporated into a mosaic dataset, an efficient data structure to store a temporal stack of rasters. The multitemporal series of TBDEMs do not incorporate any blending or averaging techniques in order to retain the integrity of the elevation values in the source DEMs. The more accurate original elevation values are retained to enable time-series display and analyses such as surface volume and cut-and-fill change calculations. Advanced data storage algorithms involving recursive quadtree structures and space filling curves are being investigated as a lossless data storage and compression technique that allows quick data retrieval and minimizes disk storage needs to improve data accessibility to a wide range of end users.

Mobile Bay

A 1/9-arc-second (approximately 3-m) resolution topobathymetric model was developed for Mobile Bay, Alabama (Danielson et al., 2013), the fourth largest estuary in the United States in terms of freshwater inflow (U.S. Environmental Protection Agency, 2007). This integrated TBDEM was the first topobathymetric dataset incorporated into the NED and was included in the 2012 NED release. This dataset, as well as other completed CoNED topobathymetric models, was published as a map series and made available through The National Maphttp://viewer.nationalmap.gov/viewer/) and the USGS Topobathy Viewer ( http://topotools.cr.usgs.gov/topobathy_viewer/). Gesch (2013) used the Mobile Bay TBDEM to identify land vulnerable to inundation from sea-level rise (Figure 2). Gesch (2013) presented methods and best practices for mapping inundation that explicitly consider the vertical accuracy of the elevation data that underpin inundation models. Concepts such as the minimum sea-level rise increment and the minimum planning timeline appropriate for the study based on the vertical accuracy of the TBDEM were demonstrated using a sea-level rise assessment for Mobile Bay as an example. In addition, areas vulnerable to sea-level rise are mapped for a range of elevation values (calculated at 95% confidence) to portray the level of uncertainty in the underlying elevation data and thus the sea-level rise predictions. The study provides guidance on the consideration of vertical uncertainty in elevation-based sea-level rise assessments to provide more complete and reliable information for decision making (Gesch, 2013).

Figure 2.

Minimum and maximum extent of the area vulnerable to inundation following 1.18 m of sea-level rise (at 95% confidence) in the Mobile Bay, Alabama, region. Figure adapted from Gesch (2013).

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San Francisco Bay Region

A 2-m resolution topobathymetric model for San Francisco Bay was included in the June 2013 release of the NED (Figure 3). The data can be used in scientific analyses to address natural hazards and resilience issues in the San Francisco area including earthquakes, tsunami risk, sea-level rise, and estuarine habitat quality. The San Francisco TBDEM was used in a study to devise new methods of mapping seacliff geomorphology (Figure 4). Seacliff erosion is a serious hazard and is often estimated using successive hand digitized cliff tops or bases to assess cliff retreat (Palaseanu-Lovejoy et al., 2016). The study focuses on developing an automated procedure to delineate the cliff top and base from high-resolution DEMs. This technique has advantages over hand-digitizing; the automated method is repeatable and more efficient, and it delineates the cliff top and base at the same level of detail as the resolution of the DEM.

Figure 3.

Depiction of the 2-m resolution topobathymetric model for San Francisco Bay, California, included in the June 2013 release of the National Elevation Dataset.

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Figure 4.

Seacliff geomorphology metrics derived from a bare-earth lidar elevation model of the coast in the San Francisco, California, region.

i1551-5036-76-sp1-64-f04.tif

Northern Gulf of Mexico Coast

The lack of a complete, comprehensive, and accurate elevation model for coastal Louisiana was identified as a major limitation during the development of the 2012 Louisiana Master Plan (State of Louisiana, 2012). This low elevation region is characterized by high rates of land loss (Couvillion et al., 2011), and storm-induced episodic change is a prevalent regional risk (Morton and Barras, 2011). Current high-resolution elevation information is essential for long-term restoration project monitoring, shoreline erosion analyses, ecological, hydrologic, storm-surge, and flood-risk reduction modeling, and other coastal science and management applications. USGS, in collaboration with the LACPRA and other partners, acquired new lidar for large portions of coastal Louisiana from 2010 to 2013 during low-water conditions to ensure capture of low elevation marshes and barrier islands. In addition to these new datasets, the Northern Gulf of Mexico (NGOM) topobathymetric elevation model integrates more than 400 different data sources, including airborne lidar point clouds, hydrographic surveys, side-scan sonar surveys, and multibeam surveys obtained from USGS, NOAA, the State of Louisiana, the USACE, FEMA, and other agencies. The first version of the 3-m resolution model, which covers the Louisiana coast from the Texas border to the Mississippi border, was released publicly in 2015 ( http://topotools.cr.usgs.gov/coned/ngom.php). The cost-shared regional lidar acquisitions form the basis of a long-term acquisition strategy for periodically updating and improving the NGOM TBDEM, which is supported by LACPRA and the USGS.

The NGOM TBDEM is anticipated to contribute significantly to the 2017 Louisiana Master Plan for a Sustainable Coast and other coastal restoration and protection planning, modeling, and monitoring efforts developed by LACPRA. Moreover, 1-m resolution subsections of this broad regional elevation model have also been used to develop levee geomorphology mapping and monitoring techniques (Palaseanu-Lovejoy, Thatcher, and Barras, 2014) that can be applied to the extensive levee system in south Louisiana. Palaseanu-Lovejoy, Thatcher, and Barras (2014) identified an automated methodology to consistently and comprehensively define levee crests using high-resolution lidar derived DEMs (Figure 5). These methods were developed to accommodate all types of levees in this region, despite the large amount of variability in terms of levee morphology, size, and spatial orientation.

Figure 5.

Lidar-based elevation model across a levee in the Atchafalaya Basin in Louisiana that depicts derived metrics (crest and boundaries) and selected profiles. The DEM is referenced to the North American Vertical Datum of 1988 (NAVD88).

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Additional techniques aimed at monitoring the structure of terrestrial ecosystems are being conducted using the lidar point clouds compiled for the NGOM region. In collaboration with the National Park Service (NPS) Inventory and Monitoring Program, USGS is developing 3D vegetation structure metrics based on lidar point clouds acquired in 2006 and 2013 at Jean Lafitte National Historical Park and Preserve in Louisiana. Lidar can quantify the height, vertical structure, and volume of vegetation, which are effective metrics for characterizing biomass, biodiversity, and habitat types (Bergen et al., 2009). The resulting information will be used to quantify changes to ecologically important variables such as percent canopy cover, aboveground biomass, and vegetation structural complexity to detect changes in vegetation at the landscape-scale over time.

Hurricane Sandy Impact Region

Hurricane Sandy severely impacted the northeastern United States, particularly the New Jersey and New York coasts, altering the topography and ecosystems of this heavily populated area. Up-to-date elevation and water depth data reflecting topographic changes induced by Hurricane Sandy are needed to support the development of hazard mitigation policies and emergency preparedness, coastal redevelopment planning, inundation analyses, and studies of storm-related coastal change. In response to the storm, the USGS is developing 3D topobathymetric models for the coastal zone from Cape Cod, Massachusetts, to the Chesapeake Bay. Airborne lidar surveys were conducted immediately before and after Hurricane Sandy using the EAARL-B experimental lidar instrument (Wright et al., 2014a; 2014b). These high-resolution EAARL-B topobathymetric lidar surveys are key source data for building a comprehensive integrated TBDEM for the New Jersey–Delaware subregion, because the EAARL-B mapped the submerged topography of the Barnegat Bay estuary at an unprecedented level of detail (Figure 6). These data are being incorporated into storm surge inundation simulations that reflect how post–Hurricane Sandy geomorphic changes have affected the region's vulnerability to future storms.

Figure 6.

EAARL-B bathymetry acquired in the vicinity of Barnegat Bay Inlet, New Jersey, in late 2012, compiled from data collected both immediately before and after the landfall of Hurricane Sandy to reduce the number of data gaps. The bathymetric DEM is referenced to the North American Vertical Datum of 1988 (NAVD88).

i1551-5036-76-sp1-64-f06.tif

Alaska North Slope

The topography of Alaska is poorly mapped compared to the existing maps available for the conterminous United States. For example, modern shoreline data (1960 or newer) are available for less than 10% of Alaska (NGS, 2012b), and there is very limited coastal coverage of elevation data that meet minimum spatial resolution and vertical accuracy requirements for inundation mapping. Coastal regions in Alaska are vulnerable to landscape change associated with melting sea-ice and rising temperatures, tsunamis, and storm surge. In addition, Alaska is exhibiting very high rates of coastal erosion in some areas, with shoreline retreat rates as high as 20 m per year (Mars and Houseknecht, 2007), among the highest rates of coastal erosion worldwide.

The USGS Alaska Mapping Initiative (USGS, 2014) and a consortium of state organizations known as the Alaska Statewide Digital Mapping Initiative ( http://www.alaskamapped.org/) are collaborating on a multi-year mapping effort to improve foundational digital map layers, including elevation, to benefit safety, navigation, planning, and resource management. As part of this initiative, Interferometric Synthetic Aperture Radar (IfSAR) is being used to acquire elevation data for the state. Although the IfSAR-generated DEMs, with a 5-m post spacing and 3-m vertical accuracy, are a tremendous improvement over existing statewide DEMs derived from circa-1950s aerial photography (30×60-m post spacing and < 18-m vertical accuracy), the resolution is insufficient for detailed coastal hazard assessments and modeling efforts. Few high-resolution coastal elevation datasets exist for Alaska. The most extensive to date (2015) was acquired between 2009 and 2012 along the northern coast of Alaska (Figure 7). This dataset is one of the first and certainly the most extensive to be collected over a permafrost landscape, and many periglacial features unique to this environment are represented in exceptional detail (Figure 8).

Figure 7.

Coastal airborne lidar survey footprints for northern Alaska from 2009–2012.

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Figure 8.

Polygonal tundra features at Brownlow Point, along the North Slope of Alaska, are visible in a digital elevation model derived from high-resolution airborne lidar. Shoreline positions between 1947 and 2009, digitized from historical maps and satellite imagery, are also shown on the map. Adapted from Gibbs and Richmond (2015).

i1551-5036-76-sp1-64-f08.tif

The northern coast of Alaska lidar dataset will be used to document the modern shoreline position from which to measure past and future shoreline change (Gibbs and Richmond, 2015), as a foundational dataset for the development of coastal inundation maps and hazard assessments based on projected increases in water levels associated with climate change, and to characterize landscape features at a high level of detail, such as the morphology of coastal bluffs, beach width, the morphology of polygonal tundra features, and hydrologic connectivity (Gibbs et al., 2013).

SUMMARY

The central aim of CoNED is to construct topobathymetric elevation models keyed to science applications in coastal regions of high societal or ecological significance. Up-to-date topobathymetric models covering the United States coastal zone are required by decision makers establishing policies for hazard mitigation and emergency preparedness, for coastal redevelopment planning and prediction of inundation due to sealevel rise, and for use in studies of storm-related coastal change. The project began in 2010 as an extension and enhancement of the NED to enable the widespread creation of flood, hurricane, and sea-level rise inundation hazard maps.

USGS coastal mapping efforts are focused on selected sites of high scientific interest and hazard vulnerability. The TBDEMs are being developed for coastal scientific applications within an expanding set of geographic regions, currently including the Alaska North Slope, the San Francisco Bay region, the northern Gulf of Mexico coast, and the Hurricane Sandy impact region. These integrated elevation products incorporate topographic, topobathymetric, and bathymetric lidar collections that provide the extremely high level of regional topographic detail required for accurately mapping elevation in low-lying coastal regions.

ACKNOWLEDGMENTS

We thank Sasha Pryborowski of NOAA and two anonymous reviewers for their constructive comments. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the United States Government.

LITERATURE CITED

1.

Bergen, K.; Goetz, R.; Dubayah, R.; Henebry, G.; Hunsaker, C.; Imhoff, M.; Nelson, R.; Parker, G., and Radeloff, V., 2009. Remote sensing of 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions. Journal of Geophysical Research, 114, https://doi.org/10.1029/2008jg000883.  Google Scholar

2.

Buxton, H.T.; Andersen, M.E.; Focazio, M.J.; Haines, J.W.; Hainly, R.A.; Hippe, D.J., and Sugarbaker, L.J., 2013. Meeting the science needs of the Nation in the wake of Hurricane Sandy—A U.S. Geological Survey science plan for support of restoration and recovery. U.S. Geological Survey Circular 1390, 26 p.  http://pubs.usgs.gov/circ/1390/.  Google Scholar

3.

Couvillion, B.R.; Barras, J.A.; Steyer, G.D.; Sleavin, W.; Fischer, M.; Beck, H.; Trahan, N.; Griffin, B., and Heckman, D., 2011. Land Area Change in Coastal Louisiana from 1932 to 2010. U.S. Geological Survey Scientific Investigations Map 3164, 12 p.  Google Scholar

4.

Danielson, J.J.; Poppenga, S.K.; Brock, J.C.; Evans, G.A.; Tyler, D.J.; Gesch, D.B.; Thatcher, C.A.; and Barras, J.A.; 2016. Topobathymetric elevation model development using a new methodology—Coastal National Elevation Database. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 75– 89.  Google Scholar

5.

Danielson, J.J.; Brock, J.C.; Howard, D.M.; Gesch, D.B.; Bonisteel-Cormier, J.M., and Travers, L.J., 2013. Topobathymetric model of Mobile Bay, Alabama. U.S. Geological Survey Data Series 769, 6 map sheets, scale 1:55,000.  http://pubs.er.usgs.gov/publication/ds769.  Google Scholar

6.

Eakins, B.W. and Grothe, P.R., 2014. Challenges in building coastal digital elevation models. Journal of Coastal Research, 30 (5), 942– 953.  Google Scholar

7.

Environmental Protection Agency, 2007. National Estuary Program Coastal Condition Report,  http://water.epa.gov/type/oceb/nep/upload/2007_05_09_oceans_nepccr_pdf_nepccr_nepccr_gom_parte.pdf. Environmental Protection Agency, 2007. National Estuary Program Coastal Condition Report,  http://water.epa.gov/type/oceb/nep/upload/2007_05_09_oceans_nepccr_pdf_nepccr_nepccr_gom_parte.pdf.

8.

Gesch, D. and Wilson, R., 2001. Development of a seamless multisource topographic/bathymetric elevation model of Tampa Bay. Marine Technology Society Journal, 35 (4), 58– 64.  Google Scholar

9.

Gesch, D.B., 2007. The National Elevation Dataset. In: Maune, D. (ed.), Digital Elevation Model Technologies and Applications: The DEM Users Manual, 2nd Edition. Bethesda, Maryland: American Society for Photogrammetry and Remote Sensing, pp. 99– 118.  Google Scholar

10.

Gesch, Dean B., 2013. Consideration of vertical uncertainty in elevation-based sea-level rise assessments–Mobile Bay, Alabama case study. In: Brock, J.C.; Barras, J.A., and Williams, S.J. (eds.), Understanding and Predicting Change in the Coastal Ecosystems of the Norther Gulf of Mexico. Journal of Coastal Research, Special Issue No. 63, pp. 197– 210.  Google Scholar

11.

Gesch, D.B.; Oimoen, M.J., and Evans, G.A., 2014. Accuracy assessment of the U.S. Geological Survey National Elevation Dataset, and comparison with other large-area elevation datasets—SRTM and ASTER. U.S. Geological Survey Open-File Report 2014–1008, 10 p.  http://dx.doi.org/10.3133/ofr20141008.  Google Scholar

12.

Gibbs, A.E.; Richmond, B.M.; Palaseanu, M.; Erikson, L.H.; Jones, B.M., and Brock, J.C., 2013. Remote sensing of the Arctic coast of Alaska using airborne lidar data. Abstract, American Geophysical Union Fall Meeting ( San Francisco, CA) abstract number B51H-0406,  http://abstractsearch.agu.org/meetings/2013/FM/sections/B/sessions/B51H/abstracts/B51H-0406.html. Gibbs, A.E.; Richmond, B.M.; Palaseanu, M.; Erikson, L.H.; Jones, B.M., and Brock, J.C., 2013. Remote sensing of the Arctic coast of Alaska using airborne lidar data. Abstract, American Geophysical Union Fall Meeting ( San Francisco, CA) abstract number B51H-0406,  http://abstractsearch.agu.org/meetings/2013/FM/sections/B/sessions/B51H/abstracts/B51H-0406.html.

13.

Gibbs, A.E. and Richmond, B.M., 2015. National Assessment of Shoreline Change: Historical shoreline changes along the north coast of Alaska – U.S. Canadian Border to Icy Cape. U.S. Geological Survey Open-File Report 2015-1048, 96 p.  http://dx.doi.org/10.3133/ofr20151048.  Google Scholar

14.

Kelmelis, J.A.; DeMulder, M.L.; Ogrosky, C.E.; Van Driel, N.J., and Ryan, B.J., 2003. The National Map—From geography to mapping and back again. Photogrammetric Engineering & Remote Sensing, 69 (10), 1109– 1118.  Google Scholar

15.

Mars, J.C. and Houseknecht, D.W., 2007. Quantitative remote sensing study indicates doubling of coastal erosion rate in past 50 years along a segment of the Arctic coast of Alaska. Geology, 35 (7), 583– 586.  Google Scholar

16.

Maune, D.F.; Maitra, J.B., and McKay, E.J., 2007. Accuracy standards & guidelines. In: Maune, D. (ed.), Digital Elevation Model Technologies and Applications: The DEM Users Manual, 2nd Edition. Bethesda, Maryland: American Society for Photogrammetry and Remote Sensing, pp. 65– 97.  Google Scholar

17.

Monfort, C.L. and Lippmann, T.C., 2011. Assimilation of Airborne Imagery with a Wave Model for Bathymetric Estimation. In: Pe'eri, S. and Long, B. (eds.), Applied LIDAR Techniques. Journal of Coastal Research, Special Issue No. 62, pp. 40– 49.  Google Scholar

18.

Morton, R.A. and Barras, J.A., 2011. Hurricane impacts on coastal wetlands: A half-century record of storm-generated features from southern Louisiana. Journal of Coastal Research, 27 (1), 27– 43.  Google Scholar

19.

National Geodetic Survey, 2012a. GPS on Bench Marks GEOID12A.  http://www.ngs.noaa.gov/GEOID/GEOID12AGPSonBM12A.shtm.  Google Scholar

20.

National Geodetic Survey, 2012b. Alaska Coastal/Shoreline Mapping Program.  http://www.iho.int/mtg_docs/rhc/ArHC/ArHC3/ARHC3-3.2.8_NGS_Operations_Alaska.pdf.  Google Scholar

21.

NOAA, 2014a. Coastal and Marine Spatial Planning.  http://www.msp.noaa.gov/.  Google Scholar

23.

National Ocean Council, 2013. National Ocean Policy Implementation Plan. 32 p.,  http://www.whitehouse.gov//sites/default/files/national_ocean_policy_implementation_plan.pdf.  Google Scholar

24.

National Research Council, 2004. A geospatial framework for the coastal zone. Washington, DC: The National Academies Press, 149 p.  Google Scholar

25.

National Science and Technology Council, 2016. National Coastal Mapping Strategy 1.0: Coastal Lidar Elevation for a 3D Nation. Office of Science and Technology Policy, in press, 58 p.  Google Scholar

26.

Palaseanu-Lovejoy; M., Danielson, J.; Thatcher, C.; Foxgrover, A.; Barnard, P.; Brock, J.; and Young, A.; 2016. Automatic delineation of sea-cliff limits using lidar-derived high-resolution DEMs in southern California. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 162– 173.  Google Scholar

27.

Palaseanu-Lovejoy, M.; Thatcher, C.A., and Barras, J.A., 2014. Levee crest elevation profiles derived from airborne lidar-based high resolution digital elevation models in South Louisiana. ISPRS Journal of Photogrammetry and Remote Sensing, 91, 114– 126.  Google Scholar

28.

Poppenga, S.K.; Worstell, B.B.; Stoker, J.M., and Greenlee, S.K., 2012. Using selective drainage methods to hydrologically-condition and hydrologically-enforce lidar-derived surface flow. International Association of Hydrological Sciences Proceedings and Reports, Publication 352, p. 91.  http://iahs.info/uploads/dms/16255.352%20Abstracts%2085.pdf. Poppenga, S.K.; Worstell, B.B.; Stoker, J.M., and Greenlee, S.K., 2012. Using selective drainage methods to hydrologically-condition and hydrologically-enforce lidar-derived surface flow. International Association of Hydrological Sciences Proceedings and Reports, Publication 352, p. 91.  http://iahs.info/uploads/dms/16255.352%20Abstracts%2085.pdf.

29.

Poppenga, S.K.; Worstell, B.B.; Danielson, J.J.; Brock, J.C.; Evans, G.A., and Heidemann, H.K., 2014. Hydrologic enforcement of lidar DEMs. U.S. Geological Survey Fact Sheet 2014–3051, 4 p.  http://dx.doi.org/10.3133/fs20143051. Poppenga, S.K.; Worstell, B.B.; Danielson, J.J.; Brock, J.C.; Evans, G.A., and Heidemann, H.K., 2014. Hydrologic enforcement of lidar DEMs. U.S. Geological Survey Fact Sheet 2014–3051, 4 p.  http://dx.doi.org/10.3133/fs20143051.

30.

Poppenga, S.K. and Worstell, B.B., 2016. Hydrologic connectivity: a quantitative assessment of hydrologic-enforced drainage structures in an elevation model. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 90– 106.  Google Scholar

31.

Snyder, G.I., 2013. The benefits of improved national elevation data. Photogrammetric Engineering & Remote Sensing, 79 (2), 105– 110.  Google Scholar

32.

State of Louisiana, 2012. Louisiana's comprehensive master plan for a sustainable coast: Louisiana Coastal Protection and Restoration Authority, 188 p.  http://coastal.la.gov/a-common-vision/2012-coastal-master-plan/.  Google Scholar

33.

Sugarbaker, L.J.; Constance, E.W.; Heidemann, H.K.; Jason, A.L.; Lukas, V.; Saghy, D.L., and Stoker, J.M., 2014. The 3D Elevation Program initiative—A call for action. U.S. Geological Survey Circular 1399, 35 p.  http://dx.doi.org/10.3133/cir1399.  Google Scholar

34.

Tuell, G.; Barbor, K., and Wozencraft, J., 2010. Overview of the coastal zone mapping and imaging lidar (CZMIL): A new multisensor airborne mapping system for the U.S. Army Corps of Engineers. Proceedings, SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, https://doi.org/10.1117/12.851905. Tuell, G.; Barbor, K., and Wozencraft, J., 2010. Overview of the coastal zone mapping and imaging lidar (CZMIL): A new multisensor airborne mapping system for the U.S. Army Corps of Engineers. Proceedings, SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, https://doi.org/10.1117/12.851905.

35.

Tyler, D.; Zawada, D.G.; Nayegandhi, A.; Brock, J.C.; Crane, M.P.; Yates, K.K., and Smith, K.L., 2007. Topobathymetric data for Tampa Bay, Florida. U.S. Geological Survey Open-File Report 2007-1051.  http://pubs.usgs.gov/of/2007/1051/.  Google Scholar

36.

USGS, 2013. Hurricane Sandy Science Plan–Coastal Topographic and Bathymetric Data to Support Hurricane Impact Assessment and Response. U.S. Geological Survey Fact Sheet 2013-3099.  http://pubs.usgs.gov/fs/2013/3099/.  Google Scholar

37.

USGS, 2014. USGS Alaska Mapping Initiative.  http://nationalmap.gov/alaska/index.html. USGS, 2014. USGS Alaska Mapping Initiative.  http://nationalmap.gov/alaska/index.html.

38.

Wong, P.P.; Losada, I.J.; Gattuso, J.P.; Hinkel, J.; Khattabi, A.; McInnes, K.L.; Saito, Y., and Sallenger, A., 2014. Coastal systems and low-lying areas. In: Field, C.B., Barros, V.R.; Dokken, D.J.; Mach, K.J.; Mastrandrea, M.D.; Bilir, T.E.; Chatterjee, M.; Ebi, K.L.; Estrada, Y.O.; Genova, R.C.; Girma, B.; Kissel, E.S.; Levy, A.N.; MacCracken, S.; Mastrandrea, P.R., and White, L.L. (eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom, and New York: Cambridge University Press, pp. 361– 409.  Google Scholar

39.

Wright, C.W.; Fredericks, A.; Troche, R.J.; Klipp, E.S.; Kranenburg, C.J., and Nagle, D.B., 2014a. EAARL-B coastal topography-Eastern New Jersey, Hurricane Sandy, 2012: First Surface. U.S. Geological Survey Data Series 767.  http://pubs.usgs.gov/ds/767/.  Google Scholar

40.

Wright, C.W.; Troche, R.J.; Kranenburg, C.J.; Klipp, E.S.; Fredericks, A., and Nagle, D.B., 2014b. EAARL-B submerged topography: Barnegat Bay, New Jersey, post-Hurricane Sandy, 2012-2013. U.S. Geological Survey Data Series 887.  http://pubs.er.usgs.gov/publication/ds887.  Google Scholar
©Coastal Education and Research Foundation, Inc. 2016
Cindy A. Thatcher, John C. Brock, Jeffrey J. Danielson, Sandra K. Poppenga, Dean B. Gesch, Monica E. Palaseanu-Lovejoy, John A. Barras, Gayla A. Evans, and Ann E. Gibbs "Creating a Coastal National Elevation Database (CoNED) for Science and Conservation Applications," Journal of Coastal Research 76(sp1), 64-74, (1 December 2016). https://doi.org/10.2112/SI76-007
Received: 26 May 2015; Accepted: 15 August 2015; Published: 1 December 2016
KEYWORDS
3DEP
coastal hazards
LIDAR
sea-level rise
Storm surge
Topobathymetric elevation models
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