Shoreline Mapping Using Airborne LiDAR: History
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Since the shorelines are important geographical boundaries, monitoring shoreline change plays an important role in integrated coastal management. With the evolution of remote sensing technology, many studies have used optical images to measure and to extract shoreline. However, some factors limit the use of optical imaging on shoreline mapping. Considering that airborne LiDAR data can provide more accurate topographical information, they are used to map shorelines. There are two major types of airborne LiDAR systems that are commonly used in shoreline area surveys: the airborne laser topographic scanning system (ALT) and the airborne laser bathymetric scanning system (ALB).

  • airborne LiDAR
  • laser scanning system
  • shoreline mapping
  • shoreline extraction

1. Introduction

Shorelines are the physical interface that separates the land from the ocean. This interface is surrounded by a wealth of ocean resources. The desired economic development forces most coastal countries around the world to exploit these ocean resources [1]. Approximately 50% of the population around the world lives within 100 km of a shoreline, and aims to tap into these ocean resources along the shoreline for economic and social benefits [2][3]. However, natural factors and excessive human intervention have caused varying degrees of damage to coastal zones, making it somewhat vulnerable, especially the evidence that has been demonstrated in shoreline changes.
Due to the dynamic changes of shorelines depending on the temporal and spatial scale, the shoreline is not an invariable line. Thus, coastal scientists undertake studies to analyze variability and erosion-accretion trends for shorelines. Traditional measurement techniques such as ground surveys or aerial photogrammetry can be used for measuring the coastal beach erosion to track fluctuations in shoreline position [4]. However, there are some limitations of the traditional shoreline measurement, including limited spatial resolution, large expense, time-consuming, and the requirement of a large amount of well-trained manpower [4][5][6][7]. With the progress of remote sensing technology, such as optical, microwave, and Light Detection and Ranging (LiDAR) sensors, high precision coastal maps can be obtained accurately and efficiently [4][8][9], which have been applied in practical scenarios including the generation of accurate navigation charts, the determination of marine boundaries, the monitoring of shoreline erosion and accumulation, and delineating the intertidal zones, wetlands, and other coastal ecosystems [10][11].
In terms of shoreline measurement, optical satellite remote sensing and airborne LiDAR have been widely used in modern surveying [12][13][14][15][16][17][18][19]. A comparison between optical satellite remote sensing and airborne LiDAR is shown in Table 1. Optical satellite as passive sensor technology requires daylight and optimal weather conditions for shoreline measurement. Otherwise, the collection of optical images would be affected by adverse weather conditions, such as haze, clouds, and poor light conditions [20]. Meanwhile, it may be difficult to acquire suitable images for the study area due to the revisit of time, depending on the operational cycle of the satellite [21]. In addition, most common optical images lack vertical information [22], which cannot deal with the various tide-level results. Thus, the shoreline extracted is instantaneous. When studying the trend of shoreline change, we need to correct the instantaneous shoreline using the tidal level data. In addition, different coastal types also can affect the accuracy of optical satellite imagery-derived shorelines [23].
Table 1. Comparison of airborne LiDAR and optical satellite remote sensing.
In contrast, airborne LiDAR, as an active remote sensing technology, has the advantage of high speed and high precision of measurement, and it is not affected by lighting conditions [24]. More importantly, the real-time three-dimensional spatial information can be highly automated and captured to support rapid deployment in the coastal zone, due to the ability of LiDAR allowing for the observation of targets without terrain limitation [12][25]. For example, the U.S. Army Corps of Engineers has developed the SHOALS project to map coastal areas in a large-scale region [26], and has developed the Coastal Zone Mapping and Imaging LiDAR (CZMIL) system to improve survey efficiency and system reliability in coastal areas [27]. Canada implemented a national LiDAR project called FUDOTERAM to assess topographic and bathymetric elevation characteristics in 2006 [28]. The latest news collected is that National Oceanic and Atmospheric Administration (NOAA) has provided over 800 new coastal LiDAR datasets covering 1.4 million km2 in 2022 (https://www.opentopography.org/news/noaa-coastal-lidar-data-now-available-academic-users-through-opentopography (accessed on 8 December 2022)). Moreover, LiDAR technology has another advantage that can be used as a standalone measurement device or that can combine with with other remotely sensed devices. However, it is a pity that the temporal resolution of airborne LiDAR is closely related to the project requirements, where the operation time depends on the project period (Table 1). It is lower than that of optical satellite remote sensing, which operates all day long and visits regularly, such as SPOT [14].
The investigation of shoreline changes is one of the main applications using remote sensed devices in coastal mapping. However, shorelines changes over time with the tidal and complex coastal environment increase the difficulty of mapping in coastal areas. The shoreline extracted from the airborne LiDAR data provides an efficient, highly accurate, useful, and fast solution [29][30]. This is because it provides not only more accurate geometrics information for the topography, but it also provides a richer radiometric information such as the intensities from different channels, which help to better distinguish between the water and land [24][31]. Although airborne LiDAR demonstrates the good performance of shoreline measuring according to some studies [32][33][34], its main drawback is that it cannot directly obtain the positions of shorelines due to it storing and organizing the data as unordered discrete points which are also called point clouds [12]. Thus, how to appropriately adopt and accurately use the airborne LiDAR data for shoreline extraction is an important topic for monitoring shoreline changes.

2. Airborne LiDAR Systems Development and Datasets Availability for Shoreline Mapping

Airborne LiDAR consists of a series of components, of which there are three core pieces of equipment, namely, laser scanning system, differential GPS/GNSS, and Inertial Measurement Unit (IMU). This system allows for the instant collection of a three-dimensionality (3D) point cloud by capturing the reflectance energy emitted by the sensor [35][36][37].
As the most important equipment of airborne LiDAR, the laser ranging system makes it easy to be operated both day and night, which undertakes the role of sending and receiving laser signals. It can detect and record laser energy in different ways, including discrete-return, full-waveform, and photon-counting. Although the theory of laser was first introduced by Townes and Schawlow in 1958 [38]; it was not widely used until the 1990s. This development allows airborne LiDAR as an ideal choice for measuring shoreline environmental parameters, especially for the topography, ground objects, and vegetation classification in both surface and submerged areas [39]. More stable data of shoreline measurement (e.g., datum-derived contours) from airborne LiDAR are obtained than in High Water Line (HWL) measurements from aerial photographs, which benefit by being not subject to the effects of short-term fluctuations in wave energy and water level [40].

2.1. Airborne Laser Topographic and Bathymetric Scanning System for Shoreline Measurement

Currently, there are two major types of airborne LiDAR systems that are commonly used in shoreline area surveys, including the airborne laser topographic scanning system (ALT) and the airborne laser bathymetric scanning system (ALB). Table 2 and Table 3 summarized the development of airborne laser scanning systems, including ALT and ALB, which were used in shoreline mapping and monitoring applications.
Table 2. A summary of the airborne laser topographic scanning system (ALT) used in shoreline-related studies.
Table 3. A summary of airborne laser bathymetric scanning system (ALB) used in shoreline related studies.
With the development of the ALT, the accuracy of the data has been improved (Table 2). Many studies have confirmed that shoreline extracted from the ALT can actually deliver a satisfied and accurate shoreline position, and also be suitable for larger scale shoreline extraction projects [41][42][43][44]. Recently, the Optech Pegasus has updated a sensor with horizontal and vertical accuracy, respectively, at 16 cm and 5–20 cm, at 1200 m Above Ground Level (AGL), providing better detection results between the ground and the water surface [59][60].
Compared to traditional measurement techniques, shoreline positions generated using ALT are sufficiently accurate and support further analysis [53][54][55][74][75]. They have the ability to collect the cross-environmental profiles of coastal topography [12][38][76][77]. White et al. [55] introduced that there are less requirements for the tide window to conduct shoreline surveys using the ALT. Moreover, practices have proven that the ALT can easily approach areas that are difficult to access [51]. This capacity has made it possible to conduct annual high-resolution shoreline surveys [46]. However, there are some cases that show that ALT may not be able to make some detections in specific areas, such as immersed lands, jetties, and very shallow water [53].
The ALT provides very detailed terrain information, but its lasers cannot penetrate the water surface itself. Considering the features of coastal areas, and with the development of airborne LiDAR technology, it was able to integrate more advanced sensors for collecting more accurate and informative point clouds from different wavelengths, such as Optech CZMIL and Riegl VQ-880G for measuring shoreline characteristics [24][61][71]. The ALB commonly employs two laser rangefinders with different wavelengths, near-infrared (NIR) and green [78], as shown in Table 3. The measuring principle of the ALB is the NIR beam, which can measure the land and water surface [78], and the green beam can penetrate the water [79] and is reflected back from the seabed or lakebed, as shown in Figure 1. The measuring depth can vary over a range of 25–70 m, depending on the different systems [61]. ALB is becoming a fundamental tool for coastal scientists within coastal studies, due to its bathymetric ability to distinguish topographic (high density) and bathymetric (low density) LiDAR points, providing the elevation data that are critical to producing datum-based shoreline, and that are suitable for measuring the features in coastal areas [13][25].
Figure 1. Diagram of waveform reflected from ALB.
With the requirements of both the academic and industrial fields, ALB is being constantly updated and upgraded. The upgraded ALB makes a great contribution to improving the accuracy of shoreline measurement. Since 2000, many researchers have proposed various methods and sensors to perform shoreline measurements by using ALB (Table 3). For instance, NASA has built a new ALB called “EAARL” in 2001, which uses a 532 nm green laser with full waveform and an across-track scan pattern, and an RGB digital camera and a color infrared multispectral camera installed [80]. Some researchers have used EAARL-generated datasets to extract a shoreline without tide coordination [49][65][66], and the result was demonstrated to have better performances than ATM [49].
Furthermore, commercial companies (e.g., Optech, Riegl, and Leica) keep designing and upgrading their products (Table 3). Experts have successfully conducted shoreline extraction and monitoring studies using datasets collected by CZMIL [15][27][70]. More advanced ALB have been engineered by more companies, such as Leica Chiroptera II (Leica Geosystems AG, Switzerland) and Riegl VQ-880G (RIEGL Laser Measurement Systems GmbH, Austria). Webster [71] indicated that the Leica Chiroptera II has the ability to monitor subtle changes in coastal areas. Madore et al. [73] have tested that the Reigl VQ-880G can successfully aid with large-scale coastal surveys, providing more seamless datasets for transitional areas between topography and bathymetry.

2.2. Datasets Availability in Coastal Areas

Under the environment of more and more governments, organizations, companies willing to share, and open data, more and more countries provide the airborne LiDAR data online. After broad searching,  some available datasets are summarized in Table 4. The main resources of this table are from OpenTopography (https://www.opentopography.org/ (accessed on 8 December 2022)), and the government websites. These datasets are usually initiated by national or regional projects, and are conducted by the lower-level municipal governments or organizations such as provinces or states. For example, the province New Brunswick is in the eastern coastal area of Canada, and Airborne Lidar data were collected from 2015 to 2018 within the province, which fully covered the coastal area of the whole province and is open access. (https://geonb.snb.ca/li/ (accessed on 8 December 2022)).
Table 4. A summary of available airborne LiDAR datasets in coastal area.
Another example is the Scotland LiDAR project, which was conducted for five phases (still ongoing). Phase I was initialed by the Scottish Government, Scottish Environmental Protection Agency (SEPA), and Scottish Water, collaboratively. Phase 3 was initially captured by Fugro for the Scottish Power Energy Network (www.spatialdata.gov.scot (accessed on 8 December 2022)). The collaboration between the cross-functional organizations to collect the dataset may occur because the cost of collecting airborne LiDAR data is relatively high. Similarly, well-developed countries such as the USA, where LiDAR data have fully covered the coastal area nationwide, have more open airborne LiDAR-based data of the coastal area. Therefore, even though airborne LiDAR has demonstrated the ability and advantage in shoreline mapping tasks, data availability is still a challenge.

This entry is adapted from the peer-reviewed paper 10.3390/rs15010253

References

  1. Wu, T.; Hou, X. Review of research on coastaline changes. Acta Ecol. Sin. 2016, 36, 1170–1182.
  2. Toure, S.; Diop, O.; Kpalma, K.; Maiga, A. Shoreline detection using optical remote sensing: A review. ISPRS Int. J.-Geo-Inf. 2019, 8, 75.
  3. Crossland, C.J.; Baird, D.; Ducrotoy, J.P.; Lindeboom, H. The Coastal Zone—A Domain of Global Interactions. In Coastal Fluxes in the Anthropocene: The Land-Ocean Interactions in the Coastal Zone Project of the International Geosphere-Biosphere Programme; Global Change—The IGBP Series; Springer: Berlin/Heidelberg, Germany, 2005; book section Chapter 1; pp. 1–37.
  4. Boak, E.H.; Turner, I.L. Shoreline definition and detection: A review. J. Coast. Res. 2005, 21, 688–703.
  5. May, S.K.; Dolan, R.; Hayden, B.P. Erosion of the US shorelines. Eos Trans. Am. Geophys. Union 1983, 64, 521–523.
  6. Ghosh, M.K.; Kumar, L.; Roy, C. Monitoring the coastline change of Hatiya Island in Bangladesh using remote sensing techniques. ISPRS J. Photogramm. Remote Sens. 2015, 101, 137–144.
  7. Lee, I.C. Instantaneous shoreline mapping from worldview-2 satellite images by using shadow analysis and spectrum matching techniques. J. Mar. Sci. Technol. 2016, 24, 1204–1216.
  8. Alesheikh, A.A.; Ghorbanali, A.; Nouri, N. Coastline change detection using remote sensing. Int. J. Environ. Sci. Technol. 2007, 4, 61–66.
  9. Graham, D.; Sault, M.; Bailey, C.J. National ocean service shoreline—Past, present, and future. J. Coast. Res. 2003, 38, 14–32. Available online: http://www.jstor.org/stable/25736597 (accessed on 26 December 2022).
  10. Liu, X.; Jia, R.; Liu, Q.; Zhao, C.; Sun, H. Coastline extraction method based on convolutional neural networks—A case study of Jiaozhou Bay in Qingdao, China. IEEE Access 2019, 7, 180281–180291.
  11. Moore, L.J. Shoreline mapping techniques. J. Coast. Res. 2000, 16, 111–124.
  12. Ackermann, F. Airborne laser scanning: Present status and future expectations. ISPRS J. Photogramm. Remote Sens. 1999, 54, 64–67.
  13. Brock, J.C.; Wright, C.W.; Sallenger, A.H.; Krabill, W.B.; Swift, R.N. Basis and methods of NASA airborne topographic mapper lidar surveys for coastal studies. J. Coast. Res. 2002, 18, 1–13.
  14. García-Rubio, G.; Huntley, D.; Russell, P. Evaluating shoreline identification using optical satellite images. Mar. Geol. 2015, 359, 96–105.
  15. Kim, H.; Lee, S.B.; Min, K.S. Shoreline change analysis using airborne LiDAR bathymetry for coastal monitoring. J. Coast. Res. 2017, 79, 269–273.
  16. Liu, Y.; Wang, X.; Ling, F.; Xu, S.; Wang, C. Analysis of Coastline Extraction from Landsat-8 OLI Imagery. Water 2017, 9, 816.
  17. Mitri, G.; Nader, M.; Abou Dagher, M.; Gebrael, K. Investigating the performance of sentinel-2A and Landsat 8 imagery in mapping shoreline changes. J. Coast. Conserv. 2020, 24, 40.
  18. Morsy, S.; Shaker, A.; El-Rabbany, A.; Larocque, P.E. Airborne multispectral lidar data for land-cover classification and land/water mapping using different spectral indexes. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, III-3, 217–224.
  19. Obu, J.; Lantuit, H.; Grosse, G.; Günther, F.; Sachs, T.; Helm, V.; Fritz, M. Coastal erosion and mass wasting along the Canadian Beaufort Sea based on annual airborne LiDAR elevation data. Geomorphology 2017, 293, 331–346.
  20. Molander, C. Photogrammetry. In Digital Elevation Model Technologies and Applications: The DEM Users Manual; Maune, D.F., Bethesda, E., Eds.; ASPRS: Bethesda, MD, USA, 2001; pp. 121–142.
  21. Canaz, S.; Karsli, F.; Guneroglu, A.; Dihkan, M. Automatic boundary extraction of inland water bodies using LiDAR data. Ocean Coast. Manag. 2015, 118, 158–166.
  22. Lin, Y.C.; Cheng, Y.T.; Zhou, T.; Ravi, R.; Hasheminasab, S.; Flatt, J.; Troy, C.; Habib, A. Evaluation of UAV LiDAR for Mapping Coastal Environments. Remote Sens. 2019, 11, 2893.
  23. Shen, J.; Zhai, J.; Guo, H. Study on coastline extraction technology. Hydrogr. Surv. Charting 2009, 29, 74–77.
  24. Flood, M.; Gutelius, B. Commercial implications of topographic terrain mapping using scanning airborne laser radar. Photogramm. Eng. Remote Sens. 1997, 63, 327–366.
  25. Carter, J.; Schmid, K.; Waters, K.; Betzhold, L.; Hadley, B.; Mataosky, R.; Halleran, J. LiDAR 101: An introduction to Lidar Technology, Data, and Applications; Revised; NOAA Coastal Services Center: Charleston, SC, USA, 2012.
  26. Gens, R. Remote sensing of coastlines: Detection, extraction and monitoring. Int. J. Remote Sens. 2010, 31, 1819–1836.
  27. Sylvester, C.; Macon, C. Coastal remote sensing through sensor and data fusion with CZMIL. In Proceedings of the Oceans 2011 MTS/IEEE, Kona, HI, USA, 19–22 September 2011; pp. 1–5.
  28. Pe’eri, S.; Long, B. LIDAR Technology Applied in Coastal Studies and Management. J. Coast. Res. 2011, 2011, 1–5.
  29. Sesli, F.A.; Caniberk, M. Estimation of the coastline changes using LIDAR. Acta Montan. Slovaca 2015, 20, 225–233.
  30. Xu, S.; Ye, N.; Xu, S. A new method for shoreline extraction from airborne LiDAR point clouds. Remote Sens. Lett. 2019, 10, 496–505.
  31. Morsy, S.; Shaker, A.; El-Rabbany, A. Using multispectral airborne LiDAR data for land/water discrimination: A case study at Lake Ontario, Canada. Appl. Sci. 2018, 8, 349.
  32. Starek, M.J.; Vemula, R.K.; Slatton, K.C.; Shrestha, R.L.; Carter, W.E. Shoreline based feature extraction and optimal feature selection for segmenting airborne LiDAR intensity images. In Proceedings of the 2007 IEEE International Conference on Image Processing, San Antonio, TX, USA, 16 September–19 October 2007; IEEE: Piscataway, NJ, USA, 2007; Volume 4, pp. IV-369–IV-372.
  33. Shaker, A.; Yan, W.Y.; LaRocque, P.E. Automatic land-water classification using multispectral airborne LiDAR data for near-shore and river environments. ISPRS J. Photogramm. Remote Sens. 2019, 152, 94–108.
  34. Chen, X.; Lai, Z.; Li, W.; Cheng, X. Research on Some Key Technologies of Features Extraction from LIDAR Data in Coastal Zone. In Proceedings of the 2009 International Conference on Information Engineering and Computer Science, Wuhan, China, 19–20 December 2009; IEEE: Piscataway, NJ, USA, 2009; pp. 1–3.
  35. Yang, X. Remote Sensing and Geospatial Technologies for Coastal Ecosystem Assessment and Management; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2009.
  36. Wehr, A.; Lohr, U. Airborne laser scanning—An introduction and overview. ISPRS J. Photogramm. Remote Rensing 1999, 54, 68–82.
  37. Adams, M.D. LiDAR design, use, and calibration concepts for correct environmental detection. IEEE Trans. Robot. Autom. 2000, 16, 753–761.
  38. Flood, M. LiDAR activities and research priorities in the commercial sector. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2001, 34, 3–7.
  39. Gisuser. Optech Announces Titan, the World’s First Multispectral Airborne Lidar Sensor. 2014. Available online: https://gisuser.com/2014/12/optech-announces-titan-the-worlds-first-multispectral-airborne-lidar-sensor/ (accessed on 23 December 2020).
  40. Robertson, W.; Whitman, D.; Zhang, K.; Leatherman, S.P. Mapping shoreline position using airborne laser altimetry. J. Coast. Res. 2004, 20, 884–892.
  41. Elaksher, A.F. Fusion of hyperspectral images and lidar-based dems for coastal mapping. Opt. Lasers Eng. 2008, 46, 493–498.
  42. Sallenger, A.H.; Krabill, W.B.; Swift, R.N.; Brock, J.; List, J.; Hansen, M.; Holman, R.A.; Manizade, S.; Sontag, J.; Meredith, A.; et al. Evaluation of airborne topographic lidar for quantifying beach changes. J. Coast. Res. 2003, 19, 125–133.
  43. Liu, H.; Sherman, D.; Gu, S. Automated extraction of shorelines from airborne light detection and ranging data and accuracy assessment based on Monte Carlo simulation. J. Coast. Res. 2007, 23, 1359–1369.
  44. Stockdon, H.F.; Sallenger, A.H.; List, J.H.; Holman, R.A. Estimation of shoreline position and change using airborne topographic lidar data. J. Coast. Res. 2002, 18, 502–513.
  45. Reineman, B.D.; Lenain, L.; Castel, D.; Melville, W.K. A portable airborne scanning lidar system for ocean and coastal applications. J. Atmos. Ocean. Technol. 2009, 26, 2626–2641.
  46. Paine, J.G.; Caudle, T.L.; Andrews, J.R. Shoreline and sand storage dynamics from annual airborne LIDAR surveys, Texas Gulf Coast. J. Coast. Res. 2017, 33, 487–506.
  47. Gibeaut, J.C.; White, W.A.; Hepner, T.; Gutiérrez, R.; Tremblay, T.A.; Smyth, R.; Andrews, J. Texas Shoreline Change Project: Gulf of Mexico Shoreline Change from the Brazos River to Pass Cavallo; Report; Texas Coastal Coordination Council: Austin, TX, USA, 2000.
  48. Starek, M.J.; Vemula, R.; Slatton, K.C. Probabilistic detection of morphologic indicators for beach segmentation with multitemporal LiDAR measurements. IEEE Trans. Geosci. Remote Sens. 2012, 50, 4759–4770.
  49. White, S. Utilization of LIDAR and NOAA’s vertical datum transformation tool (VDatum) for shoreline delineation. In Proceedings of the Oceans 2007, Aberdeen, UK, 19–22 June 2017; pp. 1–6.
  50. Woolard, J.W.; Aslaksen, M.; Longenecker, J.; Ryerson, A. Shoreline mapping from airborne lidar in Shilshole Bay, Washington. In Proceedings of the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS), US Hydrographic Conference, Washington, DC, USA, 3 February 2003.
  51. Lee, I.S.; Park, H.J.; Lee, J.O.; Kim, Y.S. Delineating the natural features of a cadastral shoreline in South Korea using airborne laser scanning. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2011, 4, 905–910.
  52. Song, D.S.; Kim, I.H.; Lee, H.S. Preliminary 3D Assessment of Coastal Erosion by Data Integration between Airborne LiDAR and DGPS Field Observations. J. Coast. Res. 2013, 1445–1450.
  53. Smeeckaert, J.; Mallet, C.; David, N.; Chehata, N.; Ferraz, A. Large-scale water classification of coastal areas using airborne topographic lidar data. In Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium—IGARSS, Melbourne, VIC, Australia, 21–26 July 2013; pp. 61–64.
  54. Limber, P.W.; List, J.H.; Warren, J.D.; Farris, A.S.; Weber, K.M. Using Topographic LIDAR Data to Delineate the North Carolina Shoreline. In Proceedings of the Coastal Sediments ’07, the Sixth International Symposium on Coastal Engineering and Science of Coastal Sediment Processes, New Orleans, LA, USA, 13–17 May 2007; pp. 1837–1850.
  55. White, S.A.; Parrish, C.E.; Calder, B.R.; Pe’eri, S.; Rzhanov, Y. LIDAR-derived national shoreline: Empirical and stochastic uncertainty analyses. J. Coast. Res. 2011, 62, 62–74.
  56. Yousef, A.; Iftekharuddin, K.M.; Karim, M.A. Shoreline extraction from light detection and ranging digital elevation model data and aerial images. Opt. Eng. 2014, 53, 011006.
  57. Yousef, A.H.; Iftekharuddin, K.; Karim, M. A new morphology algorithm for shoreline extraction from DEM data. In Proceedings of the Optical Pattern Recognition XXIV, Baltimore, MD, USA, 29–30 April 2013.
  58. Dudzińska-Nowak, J.; Wężyk, P. Volumetric changes of a soft cliff coast 2008–2012 based on DTM from airborne laser scanning (Wolin Island, southern Baltic Sea). J. Coast. Res. 2014, 70, 59–64.
  59. Incekara, A.H.; Seker, D.Z.; Bayram, B. Qualifying the LIDAR-derived intensity image as an infrared band in NDWI-based shoreline extraction. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2018, 11, 5053–5062.
  60. Demir, N.; Bayram, B.; Şeker, D.Z.; Oy, S.; İnce, A.; Bozkurt, S. Advanced lake shoreline extraction approach by integration of SAR image and LIDAR data. Mar. Geod. 2019, 42, 166–185.
  61. Shan, J.; Toth, C.K. Topographic Laser Ranging and Scanning: Principles and Processing, 2nd ed.; CRC Press: Boca Raton, FL, USA; New York, NY, USA, 2018.
  62. Bakuła, K. Multispectral airborne laser scanning—A new trend in the development of LiDAR technology. Arch. Fotogram. 2015, 27, 25–44.
  63. García-Quijano, M.J.; Jensen, J.R.; Hodgson, M.E.; Hadley, B.C.; Gladden, J.B.; Lapine, L.A. Significance of altitude and posting density on Lidar-derived elevation accuracy on hazardous waste sites. Photogramm. Eng. Remote Sens. 2008, 74, 1137–1146.
  64. Pfennigbauer, M.; Ullrich, A. Multi-wavelength airborne laser scanning. In Proceedings of the International Lidar Mapping Forum, ILMF, New Orleans, LA, USA, 7–9 February 2011; pp. 1–10.
  65. White, S.A.; Wright, C.W.; Sellars, J.D.; Woolard, J.; Dunbar, A.; Le, B.; Aslaksen, M. Shoreline Delineation Using NASA’s Experimental Advanced Airborne Research Lidar (EAARL) and NOAA’s Vertical Datum Transformation Tool (VDatum); American Geophysical Union: Washington, DC, USA, 2006; Volume 2006, p. G53E–02.
  66. Houser, C.; Hapke, C.; Hamilton, S. Controls on coastal dune morphology, shoreline erosion and barrier island response to extreme storms. Geomorphology 2008, 100, 223–240.
  67. Wilson, J.C. Using airborne hydrographic LiDAR To support mapping of california’s waters. In Proceedings of the OCEANS 2008—MTS/IEEE Kobe Techno-Ocean, Kobe, Japan, 8–11 April 2008; pp. 1–8.
  68. Macon, C.L. USACE National Coastal Mapping Program and the next generation of data products. In Proceedings of the OCEANS 2009, Biloxi, MS, USA, 26–29 October 2009; pp. 1–7.
  69. Pe’eri, S.; Morgan, L.V.; Philpot, W.D.; Armstrong, A.A. Land-Water interface resolved from airborne LIDAR bathymetry (ALB) waveforms. J. Coast. Res. 2011, 62, 75–85.
  70. Wozencraft, J. Requirements for the Coastal Zone Mapping and Imaging Lidar (CZMIL). In SPIE Defense, Security, and Sensing; SPIE: Bellingham, WA, USA, 2010; Volume 7695.
  71. Webster, T. Results from 3 seasons of surveys in maritime Canada using the Leica Chiroptera II shallow water topo-bathymetric lidar sensor. In Proceedings of the Oceans 2017, Aberdeen, UK, 19–22 June 2017; pp. 1–4.
  72. Caudle, T.L.; Paine, J.G.; Andrews, J.R.; Saylam, K. Beach, Dune, and Nearshore analysis of southern Texas Gulf Coast Using xhiroptera LIDAR and imaging system. J. Coast. Res. 2019, 35, 251–268.
  73. Madore, B.; Imahori, G.; Kum, J.; White, S.; Worthem, A. NOAA’s use of remote sensing technology and the coastal mapping program. In Proceedings of the Oceans 2018 MTS/IEEE, Charleston, SC, USA, 22–25 October 2018; pp. 1–7.
  74. Lee, I.c.; Wu, B.; Li, R. Shoreline extraction from the integration of LiDAR point cloud data and aerial orthophotos using Mean Shift Segmentation. In Proceedings of the ASPRS 2009 Annual Conference, Baltimore, MD, USA, 9–13 March 2009; pp. 3033–3040.
  75. Zhang, L.; Ma, H.; Wu, J. Utilization of LiDAR and tidal gauge data for automatic extracting high and low tide lines. J. Remote Sens. 2012, 16, 405–416.
  76. Krabill, W.; Collins, J.; Link, L.; Swift, R.; Butler, M. Airborne laser topographic mapping results. Photogramm. Eng. Remote Sens. 1984, 50, 685–694.
  77. Stoker, J.; Parrish, J.; Gisclair, D.; Harding, D.; Haugerud, R.; Flood, M.; Andersen, H.E.; Schuckman, K.; Maune, D.; Rooney, P.; et al. Report of the First National Lidar Initiative Meeting; Report; US Department of the Interior, US Geological Survey: Reston, VA, USA, 2007.
  78. Guenther, G.C.; Cunningham, G.; LaRocque, P.E.; Reid, D.J. Meeting the accuracy challenge in airborne bathymetry. In Proceedings of the EARSeL-SIG-Workshop LiDAR, Dresden, Germany, 16–17 June 2000.
  79. Jerlov, N.G. Marine Optics; Elsevier Scientific Publishing Compan: Amsterdam, The Netherlands, 1976; Volume XIII, p. 231.
  80. Bonisteel, J.; Nayegandhi, A.; Wright, C.; Brock, J.C.; Nagle, D. Experimental Advanced Airborne Research Lidar (EAARL) Data Processing Manual; Report; U.S. Department of the Interior: Reston, VA, USA, 2009.
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