Knowledge of coastal elevation is an essential requirement for resource management and scientific research. Recognizing the vast potential of lidar remote sensing in coastal studies, this Special Issue includes a collection of articles intended to represent the state-of-the-art for lidar investigations of nearshore submerged and emergent ecosystems, coastal morphodynamics, and hazards due to sea-level rise and severe storms. Some current applications for lidar remote sensing described in this Special Issue include bluegreen wavelength lidar used for submarine coastal benthic environments such as coral reef ecosystems, airborne lidar used for shoreline mapping and coastal change detection, and temporal waveform-resolving lidar used for vegetation mapping.

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

Producing thematic coral reef benthic habitat maps from single-beam acoustic backscatter has been hindered by uncertainties in interpreting the acoustic energy parameters E1 (tail of 1st echo) and E2 (complete 2nd echo), typically limiting such maps to sediment classification schemes. In this study, acoustic interpretation was guided by high-resolution lidar (LIght Detection And Ranging) bathymetry. Each acoustic record, acquired from a BioSonics DT-X echosounder and multiplexed 38 and 418 kHz transducers, was paired with a spatially-coincident value of a lidar-derived proxy for topographic complexity, reef-volume (RV), and its membership to one of eight benthic habitat classes, delineated from lidar imagery, ground-truthing, and characterization of epibenthic biota. The discriminatory capabilities of the 38 and 418 kHz signals were generally similar. Individually, the E1 and E2 of both frequencies differentiated between levels of RV and most habitat classes, but could not unambiguously delineate habitats. Plotted in E1:E2 Cartesian space, both frequencies formed two main groupings: uncolonized sand habitats and colonized reefal habitats. E1 and E2 were significantly correlated at both frequencies: positively over sand habitats and negatively over reefal habitats, where the scattering influence of epibenthic biota strengthened the E1:E2 interdependence. However, sufficient independence existed between E1 and E2 to clearly delineate habitats using the multi-echo E1:E2 bottom ratio method. The point-by-point calibration provided by the lidar data was essential for resolving the uncertainties surrounding the factors informing the acoustic parameters in a large, survey-scale dataset. The findings of this study indicate that properly interpreted single-beam acoustic data can be used to thematically categorize coral reef benthic habitats.

Coral reef ecosystems are topographically complex environments and this structural heterogeneity influences the distribution, abundance and behavior of marine organisms. Airborne hydrographic lidar (Light Detection and Ranging) provides high resolution digital bathymetry from which topographic complexity can be quantified at multiple spatial scales. To assess the utility of lidar data as a predictor of fish and coral diversity and abundance, seven different morphometrics were applied to a 4 m resolution bathymetry grid and then quantified at multiple spatial scales (i.e., 15, 25, 50, 100, 200 and 300 m radii) using a circular moving window analysis. Predictive models for nineteen fish metrics and two coral metrics were developed using the new statistical learning technique of stochastic gradient boosting applied to regression trees. Predictive models explained 72% of the variance in herbivore biomass, 68% of parrotfish biomass, 65% of coral species richness and 64% of fish species richness. Slope of the slope (a measure of the magnitude of slope change) at relatively local spatial scales (15–100 m radii) emerged as the single best predictor. Herbivorous fish responded to topographic complexity at spatial scales of 15 and 25 m radii, whereas broader spatial scales of between 25 and 300 m radii were relevant for piscivorous fish. This study demonstrates great utility for lidar-derived bathymetry in the future development of benthic habitat maps and faunal distribution maps to support ecosystem-based management and marine spatial planning.

Reef fish assemblage relationships with in situ and lidar topographic measurements across the seascape were analyzed to evaluate the possibility of using lidar metrics as a proxy for prediction models. In situ topographic complexity (i.e., linear rugosity) was measured from 346 point-count fish surveys spanning the reef seascape. Lidar topographic measurements (i.e., surface rugosity, elevation, and volume) were obtained from a high-resolution lidar bathymetric dataset of each survey's footprint. The survey sites were characterized by an independently derived benthic habitat map. Reef fish abundance and species richness appeared to increase with increasing topographic complexity. Although significant, the relationship was weak. Habitat characterization showed that these relationships changed across the seascape. The relationship between topographic complexity and species richness was more pronounced in shallow habitats, whereas, topographic complexity related more closely to abundance in offshore habitats. In situ rugosity measurement yielded the best explanation of fish assemblage structure parameters, but the weaker lidar metric correlations followed similar trends. Accordingly, lidar-measured topographic complexity may be a useful metric for reef fish distributional models. Such predictive models could have many scientific and management applications including: estimating fish stocks, viewing data trends across the seascape, and designing marine protected areas. However, better understanding of the appropriate spatial scale, measurement scale, and fish operational scale is needed, as well as more research on the dynamics of how reef fishes relate to topographic complexity and other ecological factors influencing distributions across the seascape.

The importance of sea-level rise in shaping coastal landscapes is well recognized within the earth science community, but as with many natural hazards, communicating the risks associated with sea-level rise remains a challenge. Topography is a key parameter that influences many of the processes involved in coastal change, and thus, up-to-date, high-resolution, high-accuracy elevation data are required to model the coastal environment. Maps of areas subject to potential inundation have great utility to planners and managers concerned with the effects of sea-level rise. However, most of the maps produced to date are simplistic representations derived from older, coarse elevation data. In the last several years, vast amounts of high quality elevation data derived from lidar have become available. Because of their high vertical accuracy and spatial resolution, these lidar data are an excellent source of up-to-date information from which to improve identification and delineation of vulnerable lands. Four elevation datasets of varying resolution and accuracy were processed to demonstrate that the improved quality of lidar data leads to more precise delineation of coastal lands vulnerable to inundation. A key component of the comparison was to calculate and account for the vertical uncertainty of the elevation datasets. This comparison shows that lidar allows for a much more detailed delineation of the potential inundation zone when compared to other types of elevation models. It also shows how the certainty of the delineation of lands vulnerable to a given sea-level rise scenario is much improved when derived from higher resolution lidar data.

The morphology of coastal sand dunes plays an important role in determining how a beach will respond to a hurricane. Accurate measurements of dune height and position are essential for assessing the vulnerability of beaches to extreme coastal change during future landfalls. Lidar topographic surveys provide rapid, accurate, high-resolution datasets for identifying the location, position, and morphology of coastal sand dunes over large stretches of coast. An algorithm has been developed for identification of the crest of the most seaward sand dune that defines the landward limit of the beach system. Based on changes in beach slope along cross-shore transects of lidar data, dune elevation and location can automatically be extracted every few meters along the coastline. Dune elevations in conjunction with storm-induced water levels can be used to predict the type of coastal response (e.g., beach erosion, dune erosion, overwash, or inundation) that may be expected during hurricane landfall. The vulnerability of the beach system at Fire Island National Seashore in New York to the most extreme of these changes, inundation, is assessed by comparing lidar-derived dune elevations to modeled wave setup and storm surge height. The vulnerability of the beach system to inundation during landfall of a Category 3 hurricane is shown to be spatially variable because of longshore variations in dune height (mean elevation = 5.44 m, standard deviation = 1.32 m). Hurricane-induced mean water levels exceed dune elevations along 70% of the coastal park, making these locations more vulnerable to inundation during a Category 3 storm.

Hurricane Katrina was one of the largest natural disasters in U.S. history. Due to the sheer size of the affected areas, an unprecedented regional analysis at very high resolution and accuracy was needed to properly quantify and understand the effects of the hurricane and the storm tide. Many disparate sources of lidar data were acquired and processed for varying environmental reasons by pre- and post-Katrina projects. The datasets were in several formats and projections and were processed to varying phases of completion, and as a result the task of producing a seamless digital elevation dataset required a high level of coordination, research, and revision. To create a seamless digital elevation dataset, many technical issues had to be resolved before producing the desired 1/9-arc-second (3meter) grid needed as the map base for projecting the Katrina peak storm tide throughout the affected coastal region. This report presents the methodology that was developed to construct seamless digital elevation datasets from multipurpose, multi-use, and disparate lidar datasets, and describes an easily accessible Web application for viewing the maximum storm tide caused by Hurricane Katrina in southeastern Louisiana, Mississippi, and Alabama.

Driven by the successful applications of lidar in forestry and the availability of lidar technology, new research is being carried out in other ecosystems. While lidar data have often been used to study tall forest ecosystems, this study explores the utility of lidar in the lower-canopy ecosystems of the Belgian coastal dune belt. This area is largely covered by marram dune, moss dune, grassland, scrubs and some woodland. Small diameter (0.4 m) footprint lidar was applied to derive the canopy height by analyzing the first and last pulse returns simultaneously. The investigation focused on whether the height of low-canopy ecosystems could be mapped with adequate accuracy. An error analysis was performed first on flat terrain (i.e., tennis court and parking lot) and then on vegetation canopy. The mapping of coastal dune vegetation is necessary to establish the strength of the dune belt. Dune vegetation fixes the sand dunes, protecting them from erosion and from possible breakthroughs threatening the historically reclaimed land (polders) situated inland from the dunes. Next, multispectral data was acquired from a digital camera with visual and near infrared channels. The digital camera overflight was not conducted on the same platform as the lidar. After ortho-rectification of the multispectral image, the data of both sources were fused. The limited spectral information delivered by the digital camera was not able to provide a sufficiently detailed and accurate vegetation map. The fusion with lidar data provided the extra information needed to obtain the desired vegetation and dune strength maps. A total of fourteen classes were defined, of which twelve cover vegetation. It was shown that overall classification accuracy improved 16%, from 55% to 71% after data fusion.

This study evaluates the capabilities of the Experimental Advanced Airborne Research Lidar (EAARL) in delineating vegetation assemblages in Jean Lafitte National Park, Louisiana. Five-meter-resolution grids of bare earth, canopy height, canopy-reflection ratio, and height of median energy were derived from EAARL data acquired in September 2006. Ground-truth data were collected along transects to assess species composition, canopy cover, and ground cover. To decide which model is more accurate, comparisons of general linear models and generalized additive models were conducted using conventional evaluation methods (i.e., sensitivity, specificity, Kappa statistics, and area under the curve) and two new indexes, net reclassification improvement and integrated discrimination improvement. Generalized additive models were superior to general linear models in modeling presence/absence in training vegetation categories, but no statistically significant differences between the two models were achieved in determining the classification accuracy at validation locations using conventional evaluation methods, although statistically significant improvements in net reclassifications were observed.