Geology topics

Christine J. Kranenburg

Publications and source records attributed to Christine J. Kranenburg.

At least 19 recordsLinked to original sources

USGS2025

Automated generation of an urban synthetic elevation checkpoint network across the North Carolina coastline, USA

Lidar and structure from motion-derived digital elevation and surface models have widespread application. Consideration of a topographic model's vertical root mean squared error (RMSEz) and systematic directional bias is important for many of these applications, particularly landscape change detection and measurement. Due to logistic, resource, and time constraints, wide area remotely sensed topographic surveys are not always accompanied by an in situ checkpoint network for validating and characterizing survey error. Here we describe and test a method for automatically generating synthetic elevation checkpoints in bulk across hundreds of kilometers using a publicly available lidar-derived DEM time-series, road vector network, and landcover classification map. Our method produced 6000–10,000 synthetic checkpoints across the developed barrier island coastline of North Carolina. These checkpoints characterized vertical error metrics in a statistically similar way as in situ checkpoints when assessing the vertical accuracy of a contemporary lidar-derived DEM and produced RMSEz metrics an average of 0.018 m from the RMSEz of historical lidar DEMs published with tested accuracy metrics. This new method has the potential to A) lower the cost and time required to validate new remotely sensed topographic surveys by reducing or eliminating the field work associated with in situ checkpoint surveys, B) provide a means of retroactively assessing the absolute vertical accuracy and systematic bias of historical topographic datasets that were not published with tested accuracy metrics, and C) generate reference networks to assess and correct spatially variable patterns of vertical bias in topographic datasets.

North Carolina↗

USGS2023

Accurate maps of reef-scale bathymetry with synchronized underwater cameras and GNSS

We investigate the utility of towed underwater camera systems with tightly coupled Global Navigation Satellite System (GNSS) positions to provide reef-scale bathymetric models with millimeter to centimeter resolutions and accuracies with Structure-from-Motion (SfM) photogrammetry. Successful development of these techniques would allow for detailed assessments of benthic conditions, including the accretion and erosion of reefs and adjacent sediment deposits, without the need for ground control points. We use a multi-camera system towed by a small vessel to map over 70,000 m 2 of complex shallow (2–8 m water depth) bedrock reef, boulder fields, and fine (sand and gravel) sediments of Lake Tahoe, California. We find that multiple synchronized cameras increase overall mapping coverage and allow for wider survey line spacing. The accuracy of the techniques was sub-millimeter for local length measurements less than a meter, and the bathymetric reproducibility was found to scale with the accuracy of GNSS (3–5 cm), although this could be improved to sub-centimeter with the inclusion of one or more co-registered, but unsurveyed, control points. For future applications, we provide guidance on conducting field operations, correcting underwater image color, and optimizing the SfM workflows. We conclude that a GNSS-coupled underwater camera array is a promising technique to map shallow reefs at high accuracy and resolution without ground control.

California↗

USGS2023

Sound-side inundation and seaward erosion of a barrier island during hurricane landfall

Barrier islands are especially vulnerable to hurricanes and other large storms, owing to their mobile composition, low elevations, and detachment from the mainland. Conceptual models of barrier-island evolution emphasize ocean-side processes that drive landward migration through overwash, inlet migration, and aeolian transport. In contrast, we found that the impact of Hurricane Dorian (2019) on North Core Banks, a 36-km barrier island on the Outer Banks of North Carolina, was primarily driven by inundation of the island from Pamlico Sound, as evidenced by storm-surge model results and observations of high-water marks and wrack lines. Analysis of photogrammetry products from aerial imagery collected before and after the storm indicate the loss of about 18% of the subaerial volume of the island through the formation of over 80 erosional washout channels extending from the marsh and washover platform, through gaps in the foredunes, to the shoreline. The washout channels were largely co-located with washover fans deposited by earlier events. Net seaward export of sediment resulted in the formation of deltaic bars offshore of the channels, which became part of the post-storm berm recovery by onshore bar migration and partial filling of the washouts with washover deposits within 2 months. This event represents a volumetric setback in the overwash/rollover behavior required for barrier transgression, but the new ponds and lowland habitats may provide beneficial habit for endangered species and will likely persist for years.

North Carolina↗

USGS2022

Human-in-the-Loop segmentation of earth surface imagery

Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. Manual methods are often prohibitively time-consuming, especially those images consisting of small objects and/or significant spatial heterogeneity of colors or textures. Labeling complicated regions of transition that in Earth surface imagery are represented by collections of mixed-pixels, -textures, and -spectral signatures, can be especially error-prone because it is difficult to reliably unmix, identify and delineate consistently. However, the success of supervised machine learning (ML) approaches is entirely dependent on good label data. We describe a fast, semi-automated, method for interactive segmentation of N-dimensional (x, y, N) images into two-dimensional (x, y) label images. It uses human-in-the-loop ML to achieve consensus between the labeler and a model in an iterative workflow. The technique is reproducible; the sequence of decisions made by human labeler and ML algorithms can be encoded to file, so the entire process can be played back and new outputs generated with alternative decisions and/or algorithms. We illustrate the scientific potential of segmentation of imagery of diverse settings and image types using six case studies from river, estuarine, and open coast environments. These photographic and non-photographic imagery consist of 1- and 3-bands on regular and irregular grids ranging from centimeters to tens of meters. We demonstrate high levels of agreement in label images generated by several labelers on the same imagery, and make suggestions to achieve consensus and measure uncertainty, ideal for widespread application in training supervised ML for image segmentation.

Earth and Space Science↗

USGS2021

Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation

Introduction Structure from motion (SFM) has become an integral technique in coastal change assessment; the U.S. Geological Survey (USGS) used Agisoft Metashape Professional Edition photogrammetry software to develop a workflow that processes coastline aerial imagery collected in response to storms since Hurricane Florence in 2018. This report details step-by-step instructions to create three-dimensional (3D) spatial products from both singular and repeated collections of shoreline aerial imagery. The products can be used for real-time hazard guidance and future forecasting and recovery endeavors.

Open-File Report↗

USGS2021

Developing bare-earth digital elevation models from structure-from-motion data on barrier islands

Unoccupied aerial systems can collect aerial imagery that can be used to develop structure-from-motion products with a temporal resolution well-suited to monitoring dynamic barrier island environments. However, topographic data created using photogrammetric techniques such as structure-from-motion represent the surface elevation including the vegetation canopy . Additional processing is required for estimating bare-earth elevation, which is critical for understanding the underlying geomorphology of these islands. In this study, we used a vegetation and elevation survey to produce bare-earth digital elevation models from structure-from-motion-derived elevation products for two sites on Dauphin Island, Alabama (USA). One site was exposed to high wave energy and included a mix of beach, dune, and barrier flat habitats that were dominated by supratidal/upland herbaceous vegetation. The second site was exposed to low wave energy and was dominated by intertidal marsh. Aerial imagery was collected in late fall of 2018 and spring of 2019. We tested several machine learning algorithms for predicting and removing elevation bias for vegetated areas using predictors that included spectral indices from unoccupied aerial systems-based multispectral imagery and landscape position information (e.g., relative topography and distance from shore). Models were developed for each site and season. We also explored how well the model from one season generalized to data from a different season for the same site. For developing initial digital surface models, we found that utilizing a minimum bin algorithm, as opposed to interpolation, led to lower elevation bias. For bias removal, Gaussian process regression performed the best and led to a root mean square error for the bare-earth digital elevation models of around 0.10 m for the high energy site and 0.15 m for the low energy site. Compared to the digital surface models, the root mean square error for the bare-earth digital elevation models was reduced by at least 29 percent for the high energy site and 69 percent for the low energy site. For all models, common predictors included surface elevation, vegetation greenness, and distance from the shoreline. The models produced comparable results when trained using data from a different season. The error estimates for all analyses were within published elevation standards for lidar data for vegetated areas. With calibration, this approach could be portable to other areas or data, such as aerial lidar (conventional or unoccupied), to provide an efficient and repeatable framework for monitoring geomorphology or provide baseline elevations for predicting changes to these environments under future conditions.

Alabama↗

USGS2020

Accurate bathymetric maps from underwater digital imagery without ground control

Structure-from-Motion (SfM) photogrammetry can be used with digital underwater photographs to generate high-resolution bathymetry and orthomosaics with millimeter-to-centimeter scale resolution at relatively low cost. Although these products are useful for assessing species diversity and health, they have additional utility for quantifying benthic community structure, such as coral growth and fine-scale elevation change over time, if accurate length scales and georeferencing are included. This georeferencing is commonly provided with “ground control,” such as pre-installed seafloor benchmarks or identifiable “static” features, which can be difficult and time consuming to install, survey, and maintain. To address these challenges, we developed the SfM Quantitative Underwater Imaging Device with Five Cameras (SQUID-5), a towed surface vehicle with an onboard survey-grade Global Navigation Satellite System (GNSS) and five rigidly mounted downward-looking cameras with overlapping views of the seafloor. The cameras are tightly synchronized with both the GNSS and each other to collect quintet photo sets and record the precise location of every collection event. The system was field tested in July 2019 in the U.S. Florida Keys, in water depths ranging from 3 to 9 m over a variety of bottom types. Surveying accuracy was assessed using pre-installed stations with known coordinates, machined scale bars, and two independent surveys of a site to evaluate repeatability. Under a range of sea conditions, ambient lighting, and water clarity, we were able to map living and senile coral reef habitats and sand waves at mm-scale resolution. Data were processed using best practice SfM techniques without ground control and local measurement errors of horizontal and vertical scales were consistently sub-millimeter, equivalent to 0.013% RMSE relative to water depth. Survey-to-survey repeatability RMSE was on the order of 3 cm without georeferencing but could be improved to several millimeters with the incorporation of one or more non-surveyed marker points. We demonstrate that the SQUID-5 platform can map complex coral reef and other seafloor habitats and measure mm-to-cm scale changes in the morphology and location of seafloor features over time without pre-existing ground control.

Florida↗

USGS2016

Depth calibration of the Experimental Advanced Airborne Research Lidar, EAARL-B

Introduction The original National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL) was extensively modified to increase the spatial sampling density and to improve performance in water ranging from 3 to 44 meters (m). The new (EAARL-B) sensor features a higher spatial density that was achieved by optically splitting each laser pulse into three pulses spatially separated by 1.6 m along the flight track and 2.0 m across the flight track, on the water surface when flown at a nominal altitude of 300 m (984 feet). The sample spacing can be optionally increased to 1.0 m across the flight track. Improved depth capability was achieved by increasing the total peak laser power by a factor of 10 and by designing a new “deep-water” receiver, which is optimized to exclusively receive refracted and scattered light from deeper water (15–44 m). Two different clear-water flight missions were conducted over the U.S. Navy's South Florida Testing Facility (SFTF) to determine the EAARL-B calibration coefficients. The SFTF is an established lidar calibration range located in the coastal waters southeast of Fort Lauderdale, Florida. We used 23 selected polygons at 23 distinct depths to compare a reference dataset from this site to determine EAARL-B calibration constants over the depth range of 6.5 to 34 m. We also conducted a near-simultaneous single-beam jet-ski-based sonar survey of selected transects ranging from 1 to 33 m depth in the same area. The near-concurrent jet ski data were used to evaluate the EAARL-B performance over the depth range from 0.9 to 10 m. The more timely jet ski data were necessary because the primary reference dataset was 9 years old, and areas shallower than 6.5 m are dominated by shifting sand. We determined the jet ski data were not useful as a calibration reference in water deeper than 10 m due to large uncertainty in the vertical measurement introduced by the lack of any sensor orientation data, that is, for pitch, roll, and heading to correct the measured slant range to a vertical measurement. The resulting calibrated EAARL-B data were then analyzed and compared with the original reference dataset, the jet-ski-based dataset from the same Fort Lauderdale site, as well as the depth-accuracy requirements of the International Hydrographic Organization (IHO). We do not claim to meet all of the IHO requirements and standards. The IHO minimum depth-accuracy requirements were used as a reference only and we do not address the other IHO requirements such as “ Full Seafloor Search”. Our results show good agreement between the calibrated EAARL-B data and all reference datasets, with results that are within the 95 percent depth accuracy of the IHO Order 1 (a and b) depth-accuracy requirements.

Open-File Report↗

USGS2016

Depth calibration and validation of the Experimental Advanced Airborne Research Lidar, EAARL-B

The original National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), was extensively modified to increase the spatial sampling density and improve performance in water ranging from 3–44 m. The new (EAARL-B) sensor features a 300% increase in spatial density, which was achieved by optically splitting each laser pulse into 3 pulses spatially separated by 1.6 m along the flight track and 2.0 m across-track on the water surface when flown at a nominal altitude of 300 m. Improved depth capability was achieved by increasing the total peak laser power by a factor of 10, and incorporating a new “deep-water” receiver, optimized to exclusively receive refracted and scattered light from deeper water (15–44 m). Two clear-water missions were conducted to determine the EAARL-B depth calibration coefficients. The calibration mission was conducted over the U.S. Navy’s South Florida Testing Facility (SFTF), an established lidar calibration range located in the coastal waters southeast of Fort Lauderdale, Florida. A second mission was conducted over Lang Bank, St. Croix, U.S. Virgin Islands. The EAARL-B survey was spatially and temporally coincident with multibeam sonar surveys conducted by the National Oceanic and Atmospheric Administration (NOAA) ship Nancy Foster. The NOAA depth data range from 10–100 m, whereas the EAARL-B captured data from 0–41 m. Coefficients derived from the SFTF calibration mission were used to correct the EAARL-B data from both missions. The resulting calibrated EAARL-B data were then compared with the original reference dataset, a jet-ski-based single beam sonar dataset from the SFTF site, and the deeper NOAA data from St. Croix. Additionally, EAARL-B depth accuracy was evaluated by comparing the depth results to International Hydrographic Organization (IHO) standards. Results show good agreement between the calibrated EAARL-B data and all three reference datasets, with 95% confidence levels well within the maximum allowable total vertical uncertainty for IHO Order 1 surveys.

Journal of Coastal Research↗

USGS2015

Archive of bathymetry data collected at Cape Canaveral, Florida, 2014

Remotely sensed, geographically referenced elevation measurements of the sea floor, acquired by boat- and aircraft-based survey systems, were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida, for the area at Cape Canaveral. The work was conducted as part of a study to describe an updated bathymetric dataset collected in 2014 and compare it to previous data sets. The updated data focus on the bathymetric features and sediment transport pathways that connect the offshore regions to the shoreline and, therefore, are related to the protection of other portions of the coastal environment, such as dunes, that support infrastructure and ecosystems. Cape Canaveral Coastal System (CCCS) is a prominent feature along the Southeast U.S. coastline and is the only large cape south of Cape Fear, North Carolina. Most of the CCCS lies within the Merritt Island National Wildlife Refuge and included within its boundaries are the Cape Canaveral Air Force Station (CCAFS), NASA’s Kennedy Space Center (KSC), and a large portion of Canaveral National Seashore. The actual promontory of the modern cape falls within the jurisdictional boundaries of the CCAFS. Hydrographic survey data were collected August 18-20, 2014 ( USGS Field Activity Number 2014-324-FA ). The study covered a 20 kilometer (km) section of shoreline extending from Port Canaveral, Fla., to the northern end of the KSC property, and from the shoreline to about 2.5 km offshore. Data were acquired using both sound navigation and ranging (sonar) and light detection and ranging (lidar) systems. Two jet skis and a 17-foot (ft) outboard motor boat equipped with the USGS SANDS (System for Accurate Nearshore Depth Surveying) hydrographic system collected precision sonar data. The USGS airborne EAARL-B mapping system flown in a twin engine airplane was used to collect lidar data. The missions were synchronized so that there was temporal and spatial overlap between the sonar and lidar operations. Additional data were collected to evaluate water clarity to verify the ability of lidar to receive bathymetric returns. Both systems used differential Global Positioning System GPS and utilized the National Oceanic and Atmospheric Administration/National Geodetic Survey (NOAA/NGS) Continuously Operating Reference Station (CORS) station located at CCAFS was used as the reference station. This data series serves as an archive of processed single-beam sonar and lidar bathymetry data. Graphical Information System (GIS) data products include XYZ point bathymetry data files, a color coded bathymetry map, and interpolated bathymetry grid surface. Additional information includes an error analysis and formal Federal Geographic Data Committee (FGDC) metadata. For more information about similar projects, please visit the Barrier Island Evolution Web site.

Florida↗

USGS2015

EAARL-B coastal topography: Chandeleur Islands, Louisiana, 2012: seamless (bare earth and submerged)

These remotely sensed, geographically referenced elevation measurements of light detection and ranging ( lidar )-derived seamless (bare-earth and submerged) topography datasets were produced by the U.S. Geological Survey ( USGS ), St . Petersburg Coastal and Marine Science Center, St . Petersburg, Florida. This project provides datasets acquired on August 7 and 8, 2012, for a portion of the Chandeleur Islands, Louisiana. An innovative airborne lidar system, known as the second-generation Experimental Advanced Airborne Research Lidar ( EAARL -B), was used during data acquisition. The EAARL -B system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometers) lidar designed to map nearshore bathymetry, topography, and vegetation structure simultaneously. For more information about similar projects, please visit the Lidar for Science and Resource Management Web site.

Louisiana↗

USGS2014

EAARL-B coastal topography: eastern New Jersey, Hurricane Sandy, 2012: first surface

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida. This project provides highly detailed and accurate datasets for a portion of the New Jersey coastline beachface, acquired pre-Hurricane Sandy on October 26, and post-Hurricane Sandy on November 1 and November 5, 2012. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar system, known as the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), was used during data acquisition. The EAARL-B system is a raster-scanning, waveform-resolving, green-wavelength (532-nm) lidar designed to map nearshore bathymetry, topography, and vegetation structure simultaneously. The EAARL-B sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, down-looking red-green-blue (RGB) and infrared (IR) digital cameras, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL-B platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL-B system. The resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the "bare earth" under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Lidar for Science and Resource Management Web site.

New Jersey↗

USGS2014

EAARL-B submerged topography: Barnegat Bay, New Jersey, pre-Hurricane Sandy, 2012

These remotely sensed, geographically referenced elevation measurements of lidar-derived submerged topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida. This project provides highly detailed and accurate datasets for part of Barnegat Bay, New Jersey, acquired pre-Hurricane Sandy on October 18, 22, 23, and 26, 2012. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar, known as the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), was used during data acquisition. The EAARL-B system is a raster-scanning, waveform-resolving, green-wavelength (532-nm) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL-B sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, down-looking red-green-blue (RGB) and infrared (IR) digital cameras, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL-B platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL-B system. The resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed originally in a NASA-USGS collaboration. The exploration and processing of lidar data in an interactive or batch mode is supported using ALPS. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. The Airborne Lidar Processing System (ALPS) is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the "bare earth" under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Lidar for Science and Resource Management Web site.

New Jersey↗

USGS2014

EAARL-B submerged topography: Barnegat Bay, New Jersey, post-Hurricane Sandy, 2012-2013

These remotely sensed, geographically referenced elevation measurements of lidar-derived submerged topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida. This project provides highly detailed and accurate datasets for part of Barnegat Bay, New Jersey, acquired post-Hurricane Sandy on November 1, 5, 16, 20, and 30, 2012; December 5, 6, and 21, 2012; and January 10, 2013. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar system, known as the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), was used during data acquisition. The EAARL-B system is a raster-scanning, waveform-resolving, green-wavelength (532-nm) lidar designed to map nearshore bathymetry, topography, and vegetation structure simultaneously. The EAARL-B sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, down-looking red-green-blue (RGB) and infrared (IR) digital cameras, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL-B platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL-B system. The resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed originally in a NASA-USGS collaboration. The exploration and processing of lidar data in an interactive or batch mode is supported using ALPS. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. The Airborne Lidar Processing System (ALPS) is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the "bare earth" under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Lidar for Science and Resource Management Web site .

New Jersey↗

USGS2014

EAARL-B coastal topography: Fire Island, New York, pre-Hurricane Sandy, 2012: seamless (bare earth and submerged)

These remotely sensed, geographically referenced elevation measurements of lidar-derived seamless (bare-earth and submerged) topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida. This project provides highly detailed and accurate datasets for part of Fire Island, New York, acquired pre-Hurricane Sandy on October 27, 2012. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar, known as the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), was used during data acquisition. The EAARL-B system is a raster-scanning, waveform-resolving, green-wavelength (532-nm) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL-B sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, down-looking red-green-blue (RGB) and infrared (IR) digital cameras, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL-B platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL-B system. The resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed originally in a NASA-USGS collaboration. The exploration and processing of lidar data in an interactive or batch mode is supported using ALPS. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. The Airborne Lidar Processing System (ALPS) is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the "bare earth" under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Lidar for Science and Resource Management Web site.

New York↗

USGS2013

Land loss due to recent hurricanes in coastal Louisiana, U.S.A.

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 Gustav's impact in the absence of new storm events.

Louisiana↗

USGS2012

Recent wetland land loss due to hurricanes: Improved estimates based upon multiple source images

The objective of this study was to provide a moderate resolution 30-m fractional water map of the Chenier Plain for 2003, 2006 and 2009 by using information contained in high-resolution satellite imagery of a subset of the study area. Indices and transforms pertaining to vegetation and water were created using the high-resolution imagery, and a threshold was applied to obtain a categorical land/water map. The high-resolution data was used to train a decision-tree classifier to estimate percent water in a lower resolution (Landsat) image. Two new water indices based on the tasseled cap transformation were proposed for IKONOS imagery in wetland environments and more than 700 input parameter combinations were considered for each Landsat image classified. Final selection and thresholding of the resulting percent water maps involved over 5,000 unambiguous classified random points using corresponding 1-m resolution aerial photographs, and a statistical optimization procedure to determine the threshold at which the maximum Kappa coefficient occurs. Each selected dataset has a Kappa coefficient, percent correctly classified (PCC) water, land and total greater than 90%. An accuracy assessment using 1,000 independent random points was performed. Using the validation points, the PCC values decreased to around 90%. The time series change analysis indicated that due to Hurricane Rita, the study area lost 6.5% of marsh area, and transient changes were less than 3% for either land or water. Hurricane Ike resulted in an additional 8% land loss, although not enough time has passed to discriminate between persistent and transient changes.

Louisana↗

USGS2012

Unsupervised classification of lidar-based vegetation structure metrics at Jean Lafitte National Historical Park and Preserve

Traditional vegetation maps capture the horizontal distribution of various vegetation properties, for example, type, species and age/senescence, across a landscape. Ecologists have long known, however, that many important forest properties, for example, interior microclimate, carbon capacity, biomass and habitat suitability, are also dependent on the vertical arrangement of branches and leaves within tree canopies. The objective of this study was to use a digital elevation model (DEM) along with tree canopy-structure metrics derived from a lidar survey conducted using the Experimental Advanced Airborne Research Lidar (EAARL) to capture a three-dimensional view of vegetation communities in the Barataria Preserve unit of Jean Lafitte National Historical Park and Preserve, Louisiana. The EAARL instrument is a raster-scanning, full waveform-resolving, small-footprint, green-wavelength (532-nanometer) lidar system designed to map coastal bathymetry, topography and vegetation structure simultaneously. An unsupervised clustering procedure was then applied to the 3-dimensional-based metrics and DEM to produce a vegetation map based on the vertical structure of the park's vegetation, which includes a flotant marsh, scrub-shrub wetland, bottomland hardwood forest, and baldcypress-tupelo swamp forest. This study was completed in collaboration with the National Park Service Inventory and Monitoring Program's Gulf Coast Network. The methods presented herein are intended to be used as part of a cost-effective monitoring tool to capture change in park resources.

Louisiana↗