Mapping Seafloor Relative Reflectance and Assessing Coral Reef Morphology with EAARL-B Topobathymetric Lidar Waveforms
Topobathymetric lidar is becoming an increasingly valuable tool for benthic habitat mapping, enabling safe, efficient data acquisition over coral reefs and other fragile ecosystems. In 2014, a novel topobathymetric lidar system, the Experimental Advanced Airborne Research Lidar-B (EAARL-B), was used to acquire data in priority habitat areas in the U.S. Virgin Islands (USVI), spanning the 0–44-m depth range. In this study, new algorithms and procedures were developed for generating seafloor relative reflectance, along with a suite of shape-based waveform features from EAARL-B. Waveform features were then correlated with percent cover of coral morphologies, domed and branched, and total cover of hard and soft corals. Results show that the EAARL-B can be used to produce useful seafloor relative reflectance mosaics and also that the additional waveform shape-based features contain additional information that may benefit habitat classification—specifically, to aid in distinguishing among hard corals and their coral morphologies, domed and branched. Knowing the spatial extent of changes in coral communities is important to the understanding of resiliency of coral reefs under stress from human impacts.
KeywordsLidar Seafloor Reflectance EAARL-B Habitat mapping
Coral reef ecosystems provide essential ecosystem services to millions of people around the world (Hughes et al. 2017). The habitat and food provided by these reefs are essential for many coastal communities, including the US Caribbean. In the U.S. Virgin Islands (USVI), these ecosystems are estimated to provide economic benefit over $187 million annually to the local economy, by supporting tourism, providing coastal protection from storms, and providing habitat for commercially important fisheries (Van Beukering et al. 2011). Over the last several decades, coral reefs have undergone an unprecedented rate of decline in the USVI and worldwide (Gardner et al. 2003; Pandolfi et al. 2003; Bellwood et al. 2004). It is likely this decline will endure as these ecosystems continue to be impacted by anthropogenic stressors from ocean warming and acidification (Hughes et al. 2017). In the face of these threats, the ability to map and characterize coral reef ecosystems is a critical tool for coastal managers to monitor these rapid changes on a broad scale (Mumby and Harborne 1999; Brown et al. 2011; Monaco et al. 2012). Such maps would facilitate policies designed to improve the resiliency of these important ecosystems and to sustain the value services these ecosystems provide for coastal communities.
Benthic habitat maps that depict the spatial extent and distribution of coral reefs and other seafloor habitats are valuable to coastal management and policy makers in managing coastal ecosystems and assessing change over time. Mapping of these habitats using divers is infeasible, due to inability of divers to access dangerous or challenging locations and to the time it would take to create maps of sufficient spatial extent. While acoustic techniques are most effective in temperate ecosystems or in deeper waters, airborne bathymetric lidar has increasingly gained recognition as a viable technology for benthic habitat mapping and characterization (Collin et al. 2008; Narayanan et al. 2009; Costa et al. 2009). Past habitat mapping efforts have used lidar for consistent classification of broad functional groups (seagrass, coral, etc.). However, the development of topo-bathymetric lidar systems that record waveform metrics presents an opportunity to explore their use for finer classification of coral reef communities. Linking lidar waveforms metrics to biological characteristics of coral reef habitats and the seafloor may provide new or unique information that will help to capture fundamental changes in these habitats. This may provide another tool to better determine optimal sites for species restoration projects or to focus limited resources on areas that may be of national or conservation value.
This study included developing and testing procedures for generating relative reflectance mosaics and additional waveform features, including area under the curve, skewness, and standard deviation, from the Experimental Advanced Airborne Research Lidar-B (EAARL-B) to benefit mapping of coral reef habitats. The EAARL-B is well suited to acquire spatially dense data in the depth ranges of interest for benthic habitat mapping (Wright et al. 2016). However, the EAARL-B system and its processing software, ALPS (Airborne Lidar Processing System) (Nagle and Wright 2016), did not previously (prior to this study) provide functionality for generating seafloor reflectance products or bottom return waveform shape-based features. Reflectance mosaics may greatly enhance the value of the system to benefit benthic habitat mapping, while the addition of other shape-based waveform features may further facilitate assessment of coral communities and benthic composition (Collin et al. 2011; Rogers et al. 2015). The procedures were implemented and investigated using EAARL-B data collected over two priority locations in the USVI in 2014 and assessed using in situ seafloor reflectance spectra collected from a small boat. Correlations between waveform metrics and coral reef communities were performed using 100-m2 photo mosaics of sites surrounding Flat Cay.
EAARL-B data collection parameters
Flight altitude (AGL)
55 m/s (110 kts)
5° forward, 22° across-track
Laser footprint (at water surface)
To process the lidar data, we adopted the definitions of lidar radiometric processing levels given in Kashani et al. (2015), wherein level 0 = raw intensity; level 1 = intensity correction (i.e., correction for range, angle of incidence); level 2 = intensity normalization (i.e., histogram normalization to match adjacent flight strips or data collected across different days, sites, following the level 1 processing); and level 3 = full, rigorous radiometric correction and calibration to obtain “true” surface reflectance (generally unattainable, due to lack of manufacturer-proprietary system information and full environmental characterization). With reference to these processing levels, seafloor relative reflectance, as defined in this study, is a level 2 product, whereas true reflectance corresponds to level 3.
The ground truth for the reef cover characterization consisted of generating 100-m2 photo mosaics of coral reef communities around Flat Cay. Underwater video footage of 9 sites, ranging in depth from ~ 2 to ~ 17 m, was collected by swimming in a lawnmower pattern along transects placed on the seafloor between September 4 and 9, 2016. Overlapping still frames were extracted from the video and stitched together into a single composite image using texture based video mosaic (Rzhanov et al. 2006; Gu and Rzhanov 2006). To create a species map for each dive site, each mosaic was georeferenced and viewed on a high-resolution computer screen. Corals and macroalgae were identified and manually segmented down to the lowest possible taxonomic level, typically genus or species. Adobe Photoshop’s Magic Wand tool was used to isolate each coral head and patch of macroalgae and mask them with a species-specific color. These masks were used to calculate percent cover. To account for difficulties in identification to species level (especially among octocoral genera) and the effects of less-abundant species, we created five functional groups. Hard corals were divided into “domed” and “branched” groups based on colony growth form in order to represent the varied habitat types they provide and expected differences in response to lidar signals. The domed coral group includes boulder, brain, hill, pillar, and star corals, while the branched coral group includes finger, fire, and staghorn corals. The remaining three groups comprise the octocorals, sponges, and macroalgae turf (mostly genus Dictyota).
The input to the relative reflectance mapping process depicted in Fig. 4 consisted of georeferenced EAARL-B lidar point clouds created with the USGS ALPS software. (Readers interested in the details of this step and the algorithms implemented in ALPS are referred to Nagle and Wright et al. (2016).) Importantly for this work, each bottom return lidar point in each point cloud had an intensity value, I, which was taken to be the peak amplitude of the detected bottom return and, in turn, proportional to the received optical power. The first step in our procedure was to perform pre-processing or cleaning of the lidar data set, which entailed removing areas of land, as well as obvious noise points. The next step was to apply intensity corrections, corresponding to level 1 processing, as defined in Kashani et al. (2015). Corrections were applied for the following: (1) depth (or, perhaps more precisely stated, for the range-dependent attenuation of radiant flux in the water column) and (2) angle of incidence.
The next step in the process was the incidence angle correction. This correction is extremely important, since, unlike other bathymetric lidar systems, such as the Optech CZMIL (Feygels et al. 2013) and SHOALS (Collin et al. 2008), the EAARL-B does not attempt to maintain a constant off-nadir transmit pulse angle but instead scans back and forth across the field of view, passing nearly through nadir. This created a pronounced reduction in intensity towards the outer edges of the swath.
An underlying assumption in the correction procedures described above is that the points used to derive the correction parameters had the same “true” reflectance; hence, it was important that the subsets of points used as input were collected from a homogeneous bottom type. When and where possible, homogeneous regions were identified with the aid of imagery and/or existing habitat maps. For the EAARL-B deep receiver channel, an initial approximation of correction parameters was made using all of the points calculated for given day. Then, the resulting point cloud was used to assist in delineation of uniform bottom type, typically sand. The points of uniform bottom were then used to determine final correction parameters.
Continuing with the workflow depicted in Fig. 4, a normalization step was next performed, corresponding to level 2 processing, as defined in Kashani et al. (2015). This step consisted of first matching points from overlapping point clouds within 1 m of each other. The distributions of the corrected intensities of the matched points were analyzed, and a linear transformation (shifting and scaling of the intensities) was performed on the second point cloud, such that its mean and standard deviation were made to equal that of the first point cloud.
Next, the level 2 intensities were interpolated to a regular grid. Although any of a number of interpolation algorithms could have been used in this step, based on experimentation, we used inverse distance weighting (IDW), which was found to reduce seamline artifacts between adjacent flight lines and to generally create a more uniform representation of regions in which the angle of incidence correction has been either over- or under-applied, while also keeping processing times within practical limits.
Next, a second histogram normalization was performed, such that the level 2 intensity rasters could be combined for multiple flight lines and days, while minimizing seamlines. This was achieved using custom software developed as part of this research. This software applies semi-automated histogram scaling and shifting to adjust the contrast and brightness across adjacent rasters using a graphical user interface (GUI). Overlapping gridded data were adjusted by the user until overlapping regions visually matched. The output relative reflectance mosaic was generated in Esri ASCII raster format, with values linearly scaled to the 0–255 range (i.e., 8-bit rasters), for compatibility with other coastal GIS data layers. The resulting relative reflectance mosaics were then assessed visually and through quantitative comparison with the reference spectra acquired in the Buck Island site.
Using the underwater image mosaics generated from the in situ (diver) data, we performed linear regressions to assess the associates between the percent cover of each of these groups (along with a combined hard coral group) and the lidar waveform metrics. Three of the nine sites were below a depth of 10 meters and were excluded from this analysis, for two key reasons. First, the manner by which the waveform metrics were interpolated to into GIS-compatible formats meant that the increasing footprint size of the lidar waveform with increasing depth was not represented, so the waveform metrics include higher amounts of error at greater depths. Second, the deeper sites were dominated by soft corals and experienced greater amounts of surge—the constant motion of the soft corals within the video meant that the percent cover metrics calculated from those photo mosaics were less accurate, as the same moving soft corals may appear in multiple locations or be excluded entirely.
Visual analysis and in situ seafloor reflectance comparisons showed that the lidar-derived relative reflectance data are substantially free of artifacts, such as seamlines, falloff at swath edges, and falloff with depth, which are salient in the raw intensity data. Profiles from adjacent swaths and different acquisition dates exhibit much greater agreement after our correction and normalization procedures and show substantially higher agreement with ground truth seafloor reflectance, with R2 values improving from 0.46 to 0.73. Importantly, the procedures were designed to be efficient, such that they could be applied over large spatial extents, and, in the future, potentially even larger areas. The achieved processing performance for generating the seafloor relative reflectance mosaics was approximately one day of processing time per day of lidar data acquisition and could be further reduced by optimizing the data read/write functions within the software.
This study developed and tested new algorithms and techniques for producing relative reflectance mosaics and waveform metrics from the novel EAARL-B topobathymetric lidar. These new data sets where then investigated as an aid in distinguishing among hard corals and their morphologies. Results of the analysis performed using the waveform metrics suggest that these metrics may help to detect changes in the morphological composition of coral reef communities. Morphological types of branched and domed corals were positively correlated with standard deviation of the area under the curve and mean dispersion, respectively. This may be highly beneficial for groups who are engaged in creating benthic habitat maps for this region. Previous studies have shown that radiometric and geometric artifacts in imagery (whether optical or acoustic) can degrade the quality and accuracy of the resulting benthic habitat maps (Mumby et al. 1998, Costa and Battista 2013, Kumpumäki et al. 2015, Lecours et al. 2017). Thus, having artifact free, normalized seafloor reflectance will be critical for developing a quality habitat map for use by marine managers of the Buck Island Reef National Monument, Virgin Islands National Park, St. Thomas East End Reserve, and the Cas Cay-Mangrove Lagoon Marine Reserve and Wildlife Sanctuary. Furthermore, these methods could be extended to a wide range of coastal areas with clear, shallow (≤ 40m) water, to support a variety of management and regulatory agencies (state and federal), as well as other coastal stakeholder groups. Regarding the extensibility to other areas, it is important to note that the methods are in no way specific to coral reefs but can be applied to oyster reefs, seagrass beds, and any number of substrates and bedforms of ecological or management interest.
This study has also led to the identification of avenues for ongoing and future work. First, while the current procedures are reasonably efficient, it is anticipated that further efficiency gains—particularly, reduction of human time in the process—can be obtained by automating the determination of the coefficients in the depth and incidence angle corrections. The correction parameters (a, b) from the depth correction are functions of water clarity, while the parameters (α and β) from the incidence angle correction are functions of the seafloor reflectance properties. Through analysis of a large number of sites, it may be possible to both obtain reasonable starting estimates of the parameters for new sites and also to predict when, where, and how frequently new parameters need to be determined. Second, the encouraging results from analyzing the lidar waveform metrics suggest that, combined with the relative reflectance mosaics, these waveform features may further benefit ecological assessments performed using lidar. Prior studies suggest that reefs are degrading and shifting from hard to soft coral or macroalgae (Fabricius et al. 2011; Inoue et al. 2013). Methods described in this study provide several metrics from which it may be possible to observe this degradation over 100s to 1000s of meters. Waveform metrics of standard deviation of the skewness and standard deviation of the area under the curve can provide qualitative assessment of changes in reef communities that can help to prioritize in situ, detailed monitoring of selected sites. Lastly, the interesting effect of specular solar reflections on lidar intensity and derived reflectance values merits further investigation. By incorporating an image processing step which identifies areas of sun glint into the procedure, it will likely be possible to better anticipate and correct for this effect.
This work stems from and extends the first author’s master’s thesis research at Oregon State University. The authors gratefully acknowledge the support of the following people: Rodolfo Troche (NOAA), David Nagle (USGS), Christine Kranenburg (USGS), Stephen White (NOAA), and Juliet Kinney (NOAA Joint Hydrographic Center, University of New Hampshire).
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