Sea floor classification from airborne lidar data

Sea floor classification from airborne lidar data H. Michael Tulldahl*a, Claes Vahlberga, Andreas Axelssonb, Henrik Karlssonb, Peter Jonssonc a Swedish Defence Research Agency, FOI, Box 1165, 581 11 Linköping, Sweden; b Airborne Hydrography AB, Klubbhusgatan 15, 553 03 Jönköping, Sweden; c Lund University, Engineering Geology, Box 118, 221 00 Lund, Sweden ABSTRACT Airborne depth sounding lidar has proven to be a valuable sensor for rapid and accurate sounding of shallow areas. The received lidar pulse echo contains information of the sea floor depth, but also other data can be extracted. We currently perform work on bottom classification and water turbidity estimation based on lidar data. In this paper we present the theoretical background and experimental results on bottom classification. The algorithms are developed from simulations and then tested on experimental data from the operational airborne lidar system Hawk Eye II. We compare the results to field data taken from underwater video recordings. Our results indicate that bottom classification from airborne lidar data can be made with high accuracy. Keywords: HawkEye II, remote sensing, underwater optics, multiple scattering, lidar, eelgrass

1. INTRODUCTION Airborne depth sounding lidar has many applications. Operational systems have been used for production of depth charts for several years. In spring 2006, eight systems (LADS, LADS Mk II, EAARL, three SHOALS-1000s, CHARTS, and Hawk Eye II) were in active use around the world.1 In addition to the production of depth charts, other applications of this technique are coastal zone management, search and rescue, production of terrain/hydrographic models and object detection such as detection of underwater vehicles and mines. Since the introduction of lidar bathymetry in the mid 1980:s it has been known that water volume and sea floor features can be extracted by inverse transformation of lidar responses. Water volume turbidity estimation was studied and developed e.g. by Billard et al.,2 Hoge et al.,3 and Vasilkov et al.4 Recent advancement in physical modeling and computer simulation by e.g. Zege et al.5,6 has given powerful tools to retrieve the inherent optical characteristics from lidar waveforms. The performance for detection of small targets and sea bottom features was studied e.g. by Guenther et al.,7 West and Lillycrop,8 and Steinvall et al.9,10 Recent work by Tuell et al.11,12 have demonstrated the potential to produce estimates of green laser reflectance and optical properties of the water column by analyzing lidar waveforms, which provides for the fusion of lidar and hyperspectral data for classification of e.g. sea floor vegetation. In this context, the sea floor reflectance data obtained from lidar are used to constrain the inversion of radiative transfer equations for passive hyperspectral data. In this paper we examine the ability to perform sea floor classification and its vegetation directly from lidar waveform data from the HawkEye II system. The method relies on accurate modeling of the pulse characteristics of the pulse echo reflected from the sea floor or vegetation. The classification principles utilize the bottom reflectance and the bottom vegetation elevation estimated respectively from the peak power and width of the bottom pulse in the lidar waveforms. The extraction of more information than the bottom depth and reflectance from lidar data will provide robust data for fusion with multi- and hyperspectral data. Additionally, the lidar produces data to a larger maximum depth range than passive airborne optical sensors, and will thus benefit from increased information extracted directly from lidar data. The main purpose of the work is to examine the feasibility to classify bottom types with characteristic properties in vegetation height and reflectance. Thus, a test site close to the Swedish south coast was chosen which mainly contains low reflectance algae, high reflectance sand, and eelgrass having high reflectance in green and significant height above the sea floor. The experimental lidar data was taken at bottom depths between 1 and 7 m. The water optical attenuation coefficient at the site was between 0.9 and 1.1 m-1. The classification from lidar data supported by theoretical modeling is evaluated against underwater video measurements and compared to limited data from a manual inventory of eelgrass. *[email protected]; phone +46 13 378516; fax +46 13 378066; www.foi.se

Lidar Technologies, Techniques, and Measurements for Atmospheric Remote Sensing III, edited by Upendra N. Singh, Gelsomina Pappalardo, Proc. of SPIE Vol. 6750, 675003, (2007) 0277-786X/07/$18 · doi: 10.1117/12.737922 Proc. of SPIE Vol. 6750 675003-1

The underwater video data are also used for calibration of the classification algorithm. A limitation in our work is that the times elapsed between the lidar survey, the underwater video measurements and the manual inventory are about 12 months respectively. Our theoretical modeling is accomplished with a waveform simulation model that contains both numerical and analytical components. The simulation model involve a two-scale model of the sea surface which is similar to that proposed by Mobley,13 and an analytical beam spread function for in-water propagation developed by McLean et al.14 The influence due to the presence of whitecaps on the water surface is not included in the model and simulations are performed for unpolarized light.

2. HAWKEYE II LIDAR SYSTEM The experimental results presented in this paper are based on a survey with the HawkEye II system. The HawkEye II – Airborne coastal survey system, by Airborne Hydrography AB (AHAB), Sweden, consists of the latest advances within airborne bathymetric and topographic lidar technologies. The system surveys both land and sea floor simultaneously. Hence, a HawkEye II survey delivers a seamless transition between land and sea-floor from one single survey. The system is operated by one single operator when airborne. Beside the operator, the crew consists of the pilot and a co-pilot who also acts as a spotter supporting the survey. The ground support consists of survey planning, data post processing and quality control. All data and the mission planning are saved on portable devices. The Hawk Eye II system has a total system weight of less than 180 kg (including wiring) and has a power consumption of less than 1.4 kW. The system is designed for easy installation into most small to medium sized rotary or fixed wing aircraft. The time required for installation in the aircraft is less than an hour. The interfaces necessary from the aircraft are a 28 V power supply, a GPS antenna connection, a mechanical attachment plate, and a photogrammetric window. The system uses a pulsed, infrared (1064 nm) and green (532 nm) laser for the bathymetric sounding. The infrared pulses are reflected at the water surface whereas the green pulses proceed into the water volume. The laser light backscattered from the sea surface, the water volume and the sea floor is collected in multiple, high-sensitivity receivers. The received waveforms are digitized for further processing and the depth is determined by the time lapse between surface and the bottom echoes. The integrated differential GPS enables positioning of surface, sea floor, and objects in the water column in 3 dimensions in WGS 84 coordinates. The Hawk Eye II system is equipped with a two axis, servo-controlled, scanner mirror to space the sounding spots evenly on the surface. This feature also compensates for flight deviations in yaw, pitch, roll, side slip, speed and altitude. The nominal system optical axis incidence angle from the vertical is 15°-20°. The Hawk Eye II system simultaneously collects 4 kHz bathymetric, 64 kHz topographic Lidar soundings and one digital image per second. The sounding spot density for bathymetric data is programmable from 0.5 m x 0.5 m to 3 m x 3 m depending on survey requirements. The flight altitude is between 200 m and 450 m. The swath width of the system is typically 0.2-0.6 times the altitude and the aircraft speed typically 120-180 knots. The area coverage per hour is dependent of parameters such as the turn around time between different flight lines, geometrical shape of the survey area, and requirements of survey accuracy. The Hawk Eye II high laser sounding frequency and the programmable spot density allows high area coverage combined with accurate survey data collection. The system compensations for flight deviations allow for minimized overlap between adjacent flight lines. Since the system simultaneously collects data both above and below the surface only a single mission is required also in complex shallow coastal areas. More information about the system can be found in Ref. 15. The Hawk Eye II system Operator Console software is used both for survey planning and for airborne operation. The software is installed in a portable laptop which is easily connected to the system when airborne. All system parameters can be modified and supervised during the survey via the Operator Console. The software includes an extensive range of tools such as: coverage plots, real time 3-D views of the surveyed area, flight line plan, background map, system parameters, laser pulse responses, and depth ranges. Many attributes are stored for each sounding as a basis for further analysis, post processing, and quality control. The data is stored on hard disks transported from the aircraft for further post processing. In brief, the post processing consists of: −

Refinement of position data with use of navigation post processing software and data from reference stations.

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Analysis of Hydrographic and Topographic elevation data by the AHAB Coastal Survey Studio (CSS) software. The CSS tool includes several advanced algorithms for sub-surface objects detection and identification, quality checks of data, estimates of water and sea floor parameters and decision support to the data cleaning process and interfaces to third party software.

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The accuracy of the lidar system is very good both for topographic and bathymetric measurements lidar. To reach the best possible absolute accuracy, the dataset is calibrated by data from reference stations and by accurate absolute measurement on specific spots in the survey area. By usage of such reference positions, absolute errors can be reduced from the lidar dataset such that only statistical errors remain. Such procedures result in a very high absolute accuracy of the lidar dataset. In a validation measurement of the statistical accuracy of the Hawk Eye II system a repeatability survey study was performed on the Norwegian coast. A several square kilometers area was surveyed. The survey area included some small islands and a seabed with depths down to 30 meters. The survey was performed by Admiralty Coastal Surveys AB (ACSAB). The area was surveyed twice on two different days in January 2006. The two datasets were then compared in order to conclude the accuracy of the HawkEye II bathymetric lidar data. The average depth differences between the datasets in these tests were below 5 cm for all depths down to 30 meters.

3. SIMULATIONS AND METHODS Our theoretical results are generated with a waveform simulation model, which is described in detail in Ref. 10. Here, we give a brief model description. The model for transfer of the optical beam through the sea surface is a two-scale model similar to the models proposed by Mobley13 and McLean.16 The large-scale part of the model includes a representation of the gravity waves generated on a triangular grid that resolve the gravity wave components. The wave spectrum used for the gravity waves is the spectrum deduced in the Stereo Wave Observation Project (SWOP).13 The light incident on a gravity wave facet is transferred and reflected according to a capillary wave model governed by Snell's law and Fresnel's formula. The transfer model for the capillary waves (function of wind speed and incidence angle) is implemented with Monte Carlo simulations as described by Mobley.13 The two-scale water surface model uses the wind speed as the input parameter. The influence due to the presence of whitecaps on the water surface is not modeled and simulations are performed for unpolarized light. To calculate the lidar pulse responses for two-way beam propagation from the lidar transmitter to the bottom and back to the receiver we make use of the reciprocity principle. This principle has been employed for various ocean optic studies. 16-19 Reciprocity is a declaration of symmetry which, when applied to airborne lidar depth sounding, implies that the ensemble of possible paths in the water is the same for downwelling and for upwelling radiation, because the exiting photons must leave the medium in the direction opposite from that at which they entered in order to arrive at the distant receiver collocated with the laser source. The sea floor and water volume pulse responses are formed as weighted sums of individual pulse responses for each transmitted and received beam pairs through all pairs of gravity wave surface facets. Each individual path is given a weight corresponding to the power transmitted over that path. The weighting factor for an individual water volume pulse response for a gravity wave facet pair is the product of the transmitted power through the two facets. The individual volume responses are calculated with an analytic technique developed by Katsev et al.,17 which allows that the source and receiver are spaced apart and their axes oriented in different directions. With this terminology we thus represent one gravity wave facet as the source and the other as the receiver. We use an in-water beam propagation model developed by McLean et al.14 which accounts for both unscattered and scattered propagation. In our model computations for two-way propagation we make use of the downwelling diffuse attenuation coefficient. The downwelling diffuse attenuation coefficient Kd(z0) is defined in terms of the exponential decrease with vertical depth z0 of the horizontal downwelling irradiance. The average of Kd(z0) over the depth interval from depth 0 to depth z0 can be denoted K d ( z0 ) .13 The downwelling diffuse attenuation coefficient is an apparent optical parameter, which means that its value varies with the radiance distribution. By using the models for transfer through the sea surface and for in-water propagation, we calculate estimates for the downwelling diffuse attenuation coefficient. In Ref. 10, we have evaluated the qualitative behavior of our calculated K d ( z0 ) -values and found that they are consistent with the results from Monte Carlo simulations by Kirk.20 A two-step energy calibration scheme is included in our model. In the first step, the sea floor and volume returns are evaluated for a lidar system with large field-of-view FOV. The returns are then radiometrically calibrated to K d ( z0 ) , which is the apparent optical attenuation coefficient for the lidar with a large FOV for the current sensing situation and with respect to the lidar system, the depth, and the environmental optical parameters. In the second step, the calibration factors are fixed to values for the large-FOV


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receiver after which the pulse responses are evaluated for the actual FOV. The method conveys that radiometrically accurate power values are obtained for comparison between the volume backscatter and the sea floor return, and also that the reduction of backscattered power due to a limited FOV can be examined. In Fig. 1 we show examples of simulated and measured laser beam distribution after one way propagation through a wavy water surface and a water volume with small depth. The measurement of the beam profile is created by a sub-sea video capture of a laser beam in a test setup in a harbor environment.21 In the simulation the incident laser beam on the water surface is divided into individual beams, transferred through the water surface and propagated through the water. From this data, the downwelling irradiance distribution is obtained by summing the individual contributions on a sea bottom grid. The figure illustrates the influence of wave focusing of the beam irradiance distribution. Note that the Hawk Eye II beam diameter at the sea surface is larger than shown in Fig. 1. The sea surface and bottom grid side used for generation of Fig. 1 is 2 cm. A small grid side is needed for simulation of imaging systems. Effects of wave focusing in imaging systems and has been studied previously, some examples are given in Refs. 22-24. The required simulation grid side length depends on the parameters to be investigated. For generation of the data in the following figures we have used the grid side 0.5 m. The received temporal waveform of the lidar system consists of the sea floor return, the backscatter from the water volume, the return from the water surface, the background level associated with sun and sky radiation reflected from the water, and the noise equivalent power NEP of the detector. The noise model that we use is a shot noise model with Gaussian distribution with zero mean and standard a deviation that varies with the signal level at each instant in time within the waveform. Examples of a simulated and a measured waveform from the HawkEye II system are shown in Fig. 2. Sea floor classification algorithms should use characteristics from the received lidar waveform. From earlier experiments9 and simulations25,26 we have experienced that small objects on the sea bottom can be detected by an increase in the reflected bottom pulse width. The bottom pulse width can also be used for indication of elevated vegetation on the sea floor. We have examined the pulse width variations due to lidar and environmental aspects for three different model sea bottom types or vegetations: brown algae, eelgrass, and sand. The simulation results are shown in Fig. 3 - Fig. 6. Brown algae were modeled as a flat bottom surface with reflectance 3 %. In measurements by Kutser et al.,27 the reflectance for four different specimens of the brown macroalga Fucus vesiculosus was between 1 % and 4 % at 530 nm wavelength, measured through a water column of 5 cm. Eelgrass were modeled as every other grid patch with 15 % reflectance, elevated 0.5 m above bottom, and every other patch at bottom level with 12 % reflectance. Sand was modeled as a flat bottom surface with 12 % reflectance. In the measurements by Kutser et al.,27 the reflectance of sand through 50 cm of water was 10 % and the reflectance of white wet sand reached above 30 %, both at 530 nm. In Fig. 3 Fig. 6, both the simulated pulse width and the pulse peak power (dB-scale) of the sea floor echo are shown. Simulations were made with 20 lidar soundings for each bottom type and figure. The variations in the results are explained by the random model components: sea surface waves, receiver noise, and beam position on the sea floor. If other values are not stated, the flight altitude is 200 m, the water optical attenuation coefficient c = 0.39 m-1, the wind speed 3 m/s, and the bottom depth 3 m. Other lidar parameters are set to typical HawkEye II parameters. The width and peak value of the bottom pulses are measured on the difference wave, which is calculated as the difference between recorded waveform and an interpolated volume backscatter wave under the bottom echo according to Refs. 10 and 25. The width is measured at 30 % of the height of the bottom echo.


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In Fig. 3 we compare model results from wind speed 3 m/s and 6 m/s. Generally, the higher wind speed results in a larger random spread of the data. A slight increase in pulse width from the brown algae can also be seen. As one would expect the pulse echoes from elevated eelgrass are wider than from sand and brown algae (without elevation according to our algae model). In Fig. 4, the data from 3 m and 4 m bottom depth are compared. The increase in depth by 1 m results in a peak power reduction by ≈ 2 dB and a pulse widening by 0.5 ns - 1 ns. In Fig. 5, results for two different water attenuation coefficients, c = 0.39 m-1 and c = 0.7 m-1, are compared. The more turbid water results in a peak power reduction by 2 dB - 3 dB and a pulse widening by 0.5 ns - 1.5 ns. Additionally, the turbid water slightly reduces the random spread in the data. In Fig. 6, results for two different flight altitudes H, 200 m and 250 m, are compared. The higher flight altitude results in a peak power reduction by ≈ 2 dB and a pulse widening by ≈ 1 ns. The bottom pulse peak power is approximately proportional to 1/H2 according to basic lidar equations.10,19,28 Additionally, an increased flight altitude while preserving the lidar FOV-angle, can cause a widening of the bottom pulse due to an increased geometrical pulse path length.25 The water volume and water surface between the sensor and the sea floor require that accurate corrections are applied for correct sea floor classifications. Using data similar to those shown in Fig. 4 and Fig. 6, a pulse model for the pulse width and peak power was created. The pulse model is used in the next section for evaluation of sea floor classification. The pulse model includes effects from bottom depth and flight altitude on the pulse width and peak power. The purpose of the model is to remove effects from bottom depth and flight altitude on lidar data to obtain pulse data which mainly are influenced by the bottom type or vegetation. In this paper we have not included effects from the water turbidity in the pulse model, but this will be the next step in our work. We have an ongoing work where the water turbidity will be estimated from the lidar waveform. Thus, a correction for the water turbidity in each sounding position can be accounted for in the sea floor classification.


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4. FIELD DATA AND RESULTS The results presented below are based on a lidar survey made in October 2005 in the Baltic Sea near Ystad in the south of Sweden. The HawkEye II survey was made for the municipal office of Ystad and the Swedish Geotechnical Institute with the primary purpose of evaluation of bathymetric lidar for monitoring of coastline erosion. The area is exposed to strong coastal erosion which has been monitored by the municipal office since the 1950:s by transect surveys. The site is a suitable test area for evaluation of eelgrass classification with lidar. The occurrence of eelgrass at this site was documented in manual transect inventories of eelgrass at the Swedish south coast in September 2004.29 Eelgrass is a flowering plant which produces much of the food for species on the low end of the food chain. It is an important indicator of water quality. It forms a canopy to provide shelter and protection for many of the animals that live in estuaries. The HawkEye II was operated from an Aerocommander 690 B aircraft. The flight altitude was about 200 m, the survey speed 90 m/s, and the swath width 100 m. The data presented below are taken with a sounding distance of 3 m between


adjacent sounding positions. The wind speed varied between 4 m/s and 7 m/s. The lidar data were post-processed by using AHAB:s software Coastal Survey Studio, which produces a point cloud form of data, i.e. latitude, longitude, depth. The effect of tide was removed from the depths. Sample measurements of the water turbidity on the site were made with a c-Beta instrument from HobiLabs one day after the lidar survey. The attenuation coefficient at 532 nm was between 0.9 m-1 and 1.1 m-1. A geo-referenced sub-surface video survey of parts of the test site was made in August 2006. The video data are thus taken about 10 months after the lidar survey. Consequently, a complete evaluation of the classification accuracy is not achievable due to possible changes in the sea floor properties and vegetation between the measurements. The video data is however valuable for calibration of the pulse model developed from simulations (Section 3) and as an indicator of the performance of classification from lidar data. The pulse model developed from simulations was calibrated using data from 375 selected lidar shots on positions classified as algae (125 shots), sand (125 shots), and eelgrass (125 shots) from underwater video. The classification from video was made by manual inspection of the video sequences and selection of geographic positions where each bottom type/vegetation (algae, eelgrass, sand) had more than 90% coverage rate respectively. Lidar shots from these sounding positions were then extracted from the lidar data and used for calibration of the pulse model. The calibration is necessary to include specific characteristics for the lidar system (e.g. receiver gain and linearity) and because the pulse model does not contain water turbidity as an input parameter. In Fig. 7 the bottom pulse width and pulse peak power residuals are shown for the selected lidar shots on positions classified as algae, sand, and eelgrass from underwater video data. The residuals are calculated as the difference between the measured lidar data and the corresponding data from the calibrated pulse model. The residuals in bottom pulse width and pulse peak power shows a qualitative correspondence with simulations in that sand bottom data have a larger pulse peak power than algae and eelgrass data. Furthermore, the eelgrass data indicates a slightly larger pulse width than the algae data. Naturally, the spread in measured data within each group (algae, sand, eelgrass) is larger than in simulated data. The time lapse between the lidar and video data is one explanation for the larger spread. Also, it should be noted that the simulations used rather simplified models for vegetation. Natural eelgrass is represented by several elevation heights and variations in reflectance. The sand, algae in the underwater video frames also show different representations in reflectance and some algae are also elevated from bottom and thus not tightly placed on the sea floor as in the simulations. Other causes for spread in the data in Fig. 7 are local variations in water turbidity and that sloping bottoms affect the characteristics of the reflected lidar pulse. The vegetation also moves with the waves, changing geometry, and thus possibly the reflection properties. In Fig. 8 a comparison is made between classification from lidar and video data. In this figure, data from 375 other lidar shots (125 shots for each bottom type) were used than those used for calibration of the pulse model. The overall correspondence between video and lidar data is 65 %, i.e. 65 % of the sounding positions were classified similarly by both video and lidar data. The classification was made by fitting three (algae, eelgrass, sand) two-dimensional (pulse width and peak power residuals) Gaussian distributions to the data shown in Fig. 7. The classification of each sounding is then made by comparing its pulse characteristics with the Gaussian distributions for each bottom type and assigning it to the bottom type with the highest probability. Soundings with probabilities lower than 1 % are denoted “No classification”. A demonstration example of sea floor classification from two lidar flight lines and corresponding bottom depths are shown in Fig. 9. Similarly to the lidar classification in Fig. 8, the classification of each sounding is made by comparing its pulse characteristics with the Gaussian distributions for each bottom type and assigning it to the bottom type with the highest probability. Soundings with probabilities lower than 1 % are excluded in Fig. 9. The data in Fig. 9 is based on approximately 18 000 lidar soundings. About 4 % of the soundings did not fall within the 1 % probability limits for any of the three bottom types. We have compared the lidar classification to aerial photos of the site and found very good correspondence for the sand bottom and parts of the bottom covered with vegetation. With simple inspection, the aerial photos did not exhibit any differences between algae and eelgrass. It should be noted that from the underwater video data, we know that except for algae and eelgrass some parts of the bottom also was covered with bladder wrack. One of the field inventory lines reported in Ref. 29 is situated within the area for the lidar survey data. In Fig. 10 the bottom depth and eelgrass coverage estimation from lidar is compared to the manual inventory made in September 2004. The manual inventory reported in Ref. 29 was made as a transect inventory along a straight line which is perpendicular to the lidar flight lines. A transect inventory is a method used to evaluate e.g. types of vegetation by recording observations at regular intervals along a straight line. The eelgrass coverage estimation from lidar classification is calculated as the mean coverage for the closest lidar soundings within a radius of 10 m along points along the transect. For example, four soundings within a radius of 10 m, of which three soundings are classified as eelgrass and one as not eelgrass gives a coverage estimate of 75 %. There is a good correspondence between the transect method and the lidar method, when taking into account the time difference between the two methods.


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5. CONCLUSIONS Classification of the sea floor and its vegetation is of great interest for mapping, monitoring and protecting coastal areas and shallow banks. This work extends the available methods to include airborne lidar for classification between brown algae, eelgrass and bare sand bottom. The method relies on theoretical models and lidar return pulse analysis. We foresee a suitable measurement routine where lidar data and sample underwater video are simultaneously collected and where the video data are used for model calibration and for quality control of the lidar classification. Only small parts of the lidar survey area are needed for video data collection. Classification with airborne lidar is a powerful complement to manual inventory methods.

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The main goal of the work was to examine the feasibility to classify bottom types with characteristic properties in vegetation height and reflectance. Thus, experimental data was examined from a site with low reflectance algae, high reflectance sand and eelgrass having high reflectance in green and significant height above the sea floor. A comparative study shows reasonable agreement between lidar classification, underwater video and manual inventory. Some of the differences between the lidar classification and other methods may be caused by the time difference between the data collections.

ACKNOWLEDGMENTS This work is partially supported by the Swedish Governmental Agency for Innovation Systems (VINNOVA), the Swedish Armed Forces, and the Swedish National Space Board (SNSB). The authors would also like to thank Admiralty Coastal Surveys AB providing survey data from the Hawk Eye II system, the Swedish Geotechnical Institute, Ystad Municipal Office, the Swedish Environmental Protection Agency, and the County Administrative Boards of Skåne and Östergötland.

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