The FUDOTERAM is a national Canadian light detection and ranging (LIDAR) project founded by the Canadian Network of Excellence GEOmatics for Informed DEcision (GEOIDE) that investigates data fusion from airborne, marine, and terrestrial mapping sensors. In March 2009, the second Fusion des Données TERrestres, Aériennes et Marines (FUDOTERAM) workshop was held in Quebec City, Quebec, Canada. The focus of the workshop was on international collaboration: Workshops can provide an international platform for sharing ideas and study results among academy, industry, mapping and charting organizations, and service providers. LIDAR work and research included data collected from seven different coastal areas in four nations. This special issue contains selected studies from the second FUDOTERAM workshop on LIDAR technology applied in coastal studies and management. Current studies in this special issue explore LIDAR processing in charting and mapping organizations, shoreline mapping, data integration, coastal processes and coastal management, and seafloor characterization.

Starting in 2005, the French Naval Hydrographic and Oceanographic Office (Service Hydrographique et Océanographique de la Marine [SHOM]) and the French National Geographic Institute (Institut Géographique National [IGN]) began conducting a series of coastal surveys using airborne light detection and ranging (LIDAR) bathymetry (ALB) and topographic LIDAR technologies. This paper describes SHOM's experience using ALB in very shallow coastal waters and under challenging hydrographic survey conditions. The performance of ALB in comparison to multibeam echosounder (MBES) and topographic LIDAR surveys is discussed. Further, a procedure is described for integrating ALB data sets from SHOM with topographic data sets from IGN. Recommendations on conducting future survey operations are provided in this paper based on the experience gained and lessons learned. Based on these experiences, SHOM and IGN have begun a national survey project on mapping the coastal areas (sea and land) of France.

Light detection and ranging (LIDAR) data are used for a wide array of purposes in the coastal zone. This can result in LIDAR data being collected multiple times in order to meet the specific needs of different agencies. This paper assesses the potential for airborne LIDAR bathymetry (ALB) and topographic LIDAR to be integrated for use in coastal research. Two topographic LIDAR data sets and an ALB data set are examined in three coastal test areas. Consideration of the potential for data integration focuses upon external validation of each data set using global positioning system (GPS) points, comparison of subareas and onshore-offshore cross-sections, horizontal feature matching onshore, and data set datum conversion. Data accuracy and datum integration potential confirm that all three data sets can be integrated onshore to facilitate extended LIDAR coverage and possibly also to minimise survey duplication in the coastal zone. Integration potential offshore is assessed by comparing the littoral component of an onshore topographic LIDAR digital surface model (DSM) data set with ALB data. Water-surface returns in the topographic LIDAR data collected during times of high water are found to constitute a barrier to data integration offshore, but topographic LIDAR data captured at low tide in one of the three coastal test areas suggest an opportunity to minimise duplicate surveying in the coastal zone.

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

One of the most useful survey methods in nearshore studies is airborne light detection and ranging (LIDAR), which is able to densely sample topographic and shallow bathymetric elevation data over large geographic regions. Airborne LIDAR bathymetry systems are dependent on water clarity, but in the surf zone sediment and air bubbles entrained in the water column by wave breaking attenuate the laser pulse and compromise the LIDAR's ability to retrieve accurate bottom elevations. Data assimilation techniques can improve the ability of LIDAR systems to estimate bathymetry inside the surf zone. The assimilation methods are based on comparing pixel intensity patterns (scaled by offshore wave energy flux) extracted from time-averaged airborne imagery with dissipation profiles produced by a simple wave-energy transformation model. The subaerial topography and the offshore bathymetry are assumed known and an initial featureless bathymetry is assumed in the surf zone (where the data are missing). Differences between modeled dissipation and observed image pixel intensity patterns can be minimized by incrementally modifying the bathymetry. Final assimilated bathymetry estimates are compared with surveyed bathymetric data collected at the U.S. Army Corps of Engineers Field Research Facility in Duck, NC using traditional surveying methods. Analysis of data from three aerial overflights produced average root mean square differences between assimilated and surveyed bathymetry of 25–35 cm, similar to results from land-based systems. This methodology can be used to improve LIDAR-derived profiles where large gaps exist because of surf that attenuates the laser pulses, and allow for more complete evaluation of large-scale coastal behavior that includes profile evolution within the surf zone.

This paper examines the short-term evolution of a cuspate foreland with diminished sediment supply in the western Gulf of St. Lawrence. Topographic light detection and ranging and airborne light detection and ranging bathymetry are used to provide an overall analysis of the foreland system at Paspébiac, Quebec, including high-resolution digital morphology of combined subaerial and subaqueous components, between 2003 and 2006. Results indicate large differences in coastal stability around the foreland. The western barrier exhibits a stable shoreline (net change  =  0.3 m·y⁻¹), a moderate beach slope (0.12–0.18), and no subtidal bar system. The morphodynamic response in this sector is influenced by jetties and alongshore variability and is related to beach planform readjustments to varying wave conditions. The eastern barrier has higher wave exposure, high erosion rates (<6.7 m·y⁻¹), wave washover, and an intermediate barred-beach profile, with higher beach slopes (β  =  0.16–0.24). The alongshore variability is controlled, at length scales of about 500 to 700 m, by differences in relaxation time between distal and proximal sections of the foreland. At shorter length scales (∼100–500 m), alongshore variation is related to inner bar morphology, higher erosion rates being observed near rip channels or in the absence of an inner bar. Sand transport patterns reflect wave energy and approach and include a reversal in transport direction along the eastern barrier under storm waves from the SW. We show that, under declining sediment supply, sediment is being lost to a shoal and deep water off the tip of the foreland and erosion on the eastern barrier is not compensated by the slow accretion on the western side of the point.

The National Oceanic and Atmospheric Administration's (NOAA) National Geodetic Survey (NGS) is mandated to map the national shoreline, which is depicted on NOAA nautical charts, serves as an important source in determining territorial limits, and is widely used in various coastal science and management applications. The National Geodetic Survey's primary method of mapping the national shoreline is through stereo compilation from tide-coordinated aerial photography. However, over the past decade, NGS has conducted several phases of research to develop, test, and refine light detection and ranging (LIDAR)–based shoreline mapping procedures. Although important, reliable estimates of uncertainty of these products have, unfortunately, lagged behind in development. We attempt here to outline possible solutions to this lack. Specifically, this study presents and compares two new methods of assessing the uncertainty of NGS' LIDAR-derived shoreline: an empirical (ground-based) approach and a stochastic (Monte Carlo) approach. We observe uncertainties in the horizontal position of the shorelines on the order of 1 to 6 m (95%) depending on location and, especially, beach slope. We show that appropriate adjustment for biases can reduce these to about 1 m (95%) and that the two methods of assessing the uncertainty show good agreement in our test cases.

Airborne light detection and ranging (LIDAR) bathymetry (ALB) is an efficient remote-sensing technique for shallow-water mapping using green-channel waveforms. In addition to the green-channel waveforms, the SHOALS-3000 ALB system collects laser measurements in the red and infrared channels. The goal of this study was to evaluate the capacity of the ALB system to discriminate between land and water using all available channels in the system. This paper provides a review of all currently available ALB-based land-water interface algorithms (green-, red-, and infrared-channel waveforms) and a quantitative evaluation of the algorithms' performance on the basis of both individual laser measurement products and the resulting shoreline vector feature. Data for this study were collected with the use of a SHOALS-3000 system over five study sites along the New Hampshire and Maine coastlines that contain rocks, vegetation, and man-made features. All ALB shoreline algorithms show successful results in discriminating between land and water, but the best-performing algorithm only contains information from the infrared (IR)-channel waveforms (IR saturation algorithm) and the worst-performing algorithms only contain information from the red-channel waveforms (red standard deviation algorithm). The main environmental parameters that affect the performances of the algorithms are the vegetation in the rocky shoreline and the presence of surf along the sandy shoreline.

The scope of this research is to assess benthoscape discrimination by airborne light detection and ranging (LIDAR) bathymetry (ALB) on the basis of statistical parameters derived from the LIDAR waveforms, textural information, and local spatial statistics. Analysis of the underwater camera stations allowed clustering of the stations into groups on the basis of their habitat composition (β-diversity). Twelve descriptive statistics describing the shape of the bottom part of the waveform, also called 12 benthic parameters, were used for discriminating four benthic habitats. A K-means classification and a supervised method based on the Support Vector Machine (SVM) were applied to this dataset, and overall accuracies of 67.7% and 89.9% were obtained, respectively. Geostatistical analyses, using 11 textural measures, defined by the gray-level occurrence matrix (GLOM) and the gray-level co-occurrence matrix (GLCM), and three local spatial statistics were then applied to the 12 benthic parameters to enhance the SVM classification performance. The assessment of the contribution of geostatistics into benthic class segmentation was achieved by computation of separability distance. Mean (from the GLOM), mean (from the GLCM) and the local Getis-Ord statistic yielded the best rates of discrimination. These added metrics, integrated with bands related to the 12 benthic parameters, showed that the rate of correct (supervised) classification was thereby improved and increased by 5.3%. Finally, the first four principal components (PCs) (i.e., 90.41% of the 12 parameter variances, boosted by the three best geostatistics) brought out an overall accuracy of 93.3%, showing evidence for optimizing the classification processing.