Development in Tide-coordinated Shoreline Mappingccom.unh.edu/sites/default/files/publications/E07...

OSU Mapping and GIS Lab

Developments in Tide-coordinated Shoreline Mapping

Rongxing (Ron) Li 1

Christopher Parrish 2

Anuchit Sukcharoenpong 1

1: Mapping and GIS Laboratory, Dept. of Civil & Environmental Engineering & Geodetic Science, College of Engineering, The Ohio State University

2: NGS Remote Sensing Division, NOAA/NOS, National Geodetic Survey


Importance of Shorelines

• Legal boundaries: shorelines divide ownership between public (states) and private.

• Used in determining national maritime zones and boundaries (e.g., EEZ).

• Coastal science applications– Shoreline erosion, sea-level rise, inundation,

habitat change, wetlands studies

• Coastal zone management– Development, protection, restoration, etc.

OSU Mapping and GIS LabInstantaneous Shorelines

• A line where land and water touch at one instant in time (Boak and Turner, 2005).

• These shorelines generally are from intersections at the instant of data acquisition.

• Shorelines extracted from satellite images are effective and inexpensive to obtain.

• In general, instantaneous shorelines are not very useful for charting or for shoreline change applications (poor repeatability, difficult to quantify uncertainty, difficult to relate to other shorelines, chart datum, etc.).


Tide-coordinated Shorelines (TCS)

• By definition, TCS is a shoreline based on a desired tidal datum (Li et al., 2002; Lipakis, 2008).

• Examples of tidal-datum-based shoreline: mean lower-low water (MLLW) and mean high water (MHW)

• TCS may be categorized into two types depending on method of shoreline derivation (Li et al., 2002)• Physical tide-coordinated shorelines

• Digital tide-coordinated shorelines


Tidal Datums


Mapping of TCS

• Conventional approach to TCS: delineate shorelines from tide-coordinated aerial photography

• It is operationally challenging to acquire imagery at the exact instant when the instantaneous water level coincides with the tidal datum surface => tide windows

• Recent advances in mapping of TCS involve advanced tools and technologies including LiDAR, satellite imagery, and automatic-feature extraction from imagery

OSU Mapping and GIS LabNOAA’s National Shoreline

• LiDAR data is now beginning to be used for mapping MHW shoreline in NOAA’s Coastal Mapping Program

• Advantages of using LiDAR over aerial photogrammetry for shoreline mapping include increased flexibility in data acquisition and a more automated shoreline extraction process

• Tide windows for LiDAR acquisition are broader than tide windows in aerial photogrammetry

• VDatum (vertical datum transformation tool) is an important element in LiDAR shoreline mapping


VDatum

• NOAA developed VDatum for vertical datum transformation

• In NOAA National Shoreline mapping, VDatum transforms LiDAR data from ellipsoidal datum (NAD83: CORS96) from GPS to tidal datum (MHW), to extract TCS (White et al., 2010)


NOAA’s National Shoreline Workflow


VDatum MHW LiDAR-derived Shoreline

Result of LiDAR-derived MHW shoreline at Duck Field Research Facility Site(Parrish, 2009)


OSU Digital Tide-coordinated Shoreline

• Li et al. (2002) studied a method using CTM and WSM to generate TCS

• A CTM can be obtained by merging topographic data (aerial/satellite stereo imagery, LiDAR) with bathymetric data

• A WSM, representing the surface of the water body, can be produced using hydrological modeling system incorporating meteorological information and physical environmental data as boundary conditions

• An error propagation model is developed forthe digital shorelines obtained this way.


Workflow of TCS from CTM and WSM

Digital tide-coordinated shoreline from CTM and WSM (Li et al., 2002)


Digital Shoreline from CTM and WSM

DEM

Bathymetry CTM GenerationCTM

CTM

Water Surface

Digital Shoreline Generation

Digital Shoreline


Experimental Results of the Shoreline Model

Experiment 1 (WSM constraint)• Simulated Shorelines

• straight line segments• Simulated WSM

• flat plane• Different Initial Positions

Experiment 2 (WSM constraint)• Simulated Shorelines

• straight line segments• Real WSM• Different Initial Positions

Historical lines Initial line Results line

Case 1

Case 2

Case 1

Case 2


Experimental Results of the Shoreline Model

Experiment 3 (WSM constraint)• Real Shorelines

• historical Lake Erie shoreline• Real WSM• Different Initial Positions

Water

Land


Other Shoreline Mapping: Ground Survey

• Kinematic GPS survey to monitor beach changes (Morton et al., 1993)

Illustration of kinematic surveying with stop-and-go and GPS receiver mounted on a vehicle (Morton et al., 1993)


Tide-coordinated Shoreline from Ground Survey

• USGS SWASH (Surveying Wide-Area Shorelines)

Illustration of SWASH operations (USGS, 2010b)


Tide-coordinated Shoreline from Airborne Sensors

• The Defense Research Establishment Ottawa (DREO) of Canada employed polarimetric SAR images acquired from an airborne platform to extract the Mean High Water line (Yeremy et al., 2001)

• A shore slope is extracted from one polarimetric SAR image. The method to determine shoreline slopes was estimated by the tilt of the reflected surface in a direction perpendicular to the SAR’s line-of-sight (LOS) by measuring the offsets from a model polarization response.

• A shore slope and a water-level referenced instantaneous shoreline were used to determine the position of the tide-coordinated shoreline

• 6.3 to 7.8 meters in mean error

OSU Mapping and GIS LabTCS from Satellite Imagery

1. As an alternative to LiDAR approaches, DEM of the beach can be generated from a set of instantaneous shorelines (Mason et al., 1995; Hoja et al., 2000)– Extracts instantaneous shorelines from satellite images

– Links the extracted shorelines to water levels at the time of image acquisition

– Treats the shorelines as contour lines, then a beach elevation model can be interpolated

2. Both topographic data (DEM) and bathymetry also can be derived from satellite imagery, then TCS can be extracted (Muslim and Foody, 2008)– Derive DEM from satellite stereo images

– Depth information of 90 training points and 25 accuracy check points were used to determine parameters of a model for generating the bathymetry


DEM and Bathymetry from Satellite Imagery

Flow chart of tide-coordinated shoreline creation at MSL (Muslim and Foody, 2008)


Modeling of TCS from Instantaneous Shorelines

• An instantaneous shoreline can be represented by a set of polynomials as (Li et al., 2002):

• t is the time variable; s is the length parameter that starts at the beginning of the shoreline; a, b and c are temporal polynomial coefficients that can be expressed as polynomials function at water level h


Snake Model to Derive Tide-coordinated Shoreline

Implementation of snake model to derive tide-coordinated shoreline (Li et al., 2005).


Conclusions

• The method to derive laser/datum-based TCS has progressed to the point that the technique can be applied in federal projects

• Several methods are capable of deriving accurate TCS with respect to standards of hydrographic surveys for shoreline positioning and other navigation aids (IHO 2008)

• Methods to derive TCS from instantaneous shorelines show potentials and are still in research

• Advancements in remote sensing methods, especially improvements in imaging sensors and laser altimetry, greatly benefit developments to approaches to the derivation of tide-coordinated shorelines


Thank You


Diurnal Inequality

• Variations in the heights of the tides resulting from changes in the declination angle of the moon (NOAA, 2011)

• Three type of tides– Semidiurnal

– Mixed

– DiurnalPrincipal types of tides (NOAA, 2011)


Recent Approaches to TCS

• Focus on deriving elevation models of shore

• Many methods are developed to avoid tide-image coordination (time and budget consuming)

• Accuracy of the resulting shoreline mainly is based on technique and quality of input data

• Approaches may be categorized into:

– Elevation model derived directly from observations

– Utilization of instantaneous shorelines to derive elevation model


Accurate Linking of Water Levels between Vertical Datums

• Cheng et al. (2008) developed a method to accurately link water levels between datums

• A floating GPS buoy was implemented near two gauge stations in Lake Erie to simultaneously collect GPS data and water-gauge readings

• As the use of VDatum is limited to the U.S. and some countries in North America, an alternate approach linking water level from gauge stations with multiple data sources could prove to be useful for applications in other coastal regions around the world


Accurate Linking of Water Levels between Vertical Datums (2)

• By obtaining both orthometic (H) and ellipsoidal (h) heights, geoid height (N) can be determined and used to link water levels from gauge readings with satellite altimetry into a unified reference system

GPS buoy deployment near water gauge station (Cheng et al., 2008)


Back-up Slides

1. Summary of methods that derive TCS and potentially can be further developed to derive TCS (23)

2. Mapping of instantaneous shorelines from satellite images - studies from OSU (24)

3. VDatum in LiDAR shoreline mapping (28)

4. Other methods to derive TCS (30)

5. Bathymetry from satellite imagery (33)

6. References (37)

OSU Mapping and GIS LabSummary

Technique implemented Citation Accuracy

Gro

und

surv

ey Stop-and-go KGPS and vehicle mounted GPS receiver Morton et al., 1993 N/A

SWASH USGS, 2010± 1.6 meters at a 95% confidence

interval

Air

born

e se

nsor

Airborne polarimetric SAR Yeremy et al., 2001 6.3 to7.8 meters for mean difference

contouring method from LiDAR DEM Stockdon et al., 20022.9 m. for RMSE and 2.12 m. for

average difference.

contouring method from LiDAR DEM Robertson et al., 2004Less than 6 m. for mean difference of

HWL

Shoreline from LiDAR DEM with image processing techniques Liu et al., 2007± 4.5 meters at a 95% confidence

interval

Tide-coordinated shoreline from LiDAR and VDATUM White et al., 2007, 2010approx. 0.5 m. for RMSE and 0.2 m.

fro std. deviation (bias removed)

Tide-coordinated shoreline from CTM (LiDAR DEM + Bathymetry) and Water gauge observation

Li et al., 20103.55 m. in average error and 2.49 m.

in standard deviation

Tide-coordinated shoreline from CTM and WSM Li et al., 20022-13 meters for standard deviation

(estimated)

Mean shift segmentation with LiDAR DEM, satellite images, and orthoimages to extract shoreline

Lee et al., 2009, 20101.55 m. in RMSE and 8.24 m.

maximum error for overall study area (Lee et al. 2010)

Satt

ellit

e-bo

rne

sens

or Intertidal DEM interpolated from water level referenced instantaneous shoreline

Hoja et al., 2000 N/A

Intertidal DEM from water-line method from ESR-1 SAR imageMason et al., 1995, 1997,

1998, 2001N/A

DEM generated from IKONOS imagery Muslim and Foody, 2008RMSE 1.80 m. and ± 4.5 meters at a

90% confidence interval

Snake-based tide-coordinated shoreline Li et al., 2005, 2006 N/A


Mapping of Instantaneous Shorelines

• Shoreline mapping from high-accuracy satellite images has been examined in many studies

• Methods to increase the accuracy of IKONOS images includes improving the Rational Function (RF) of the sensor model (Di et al., 2001, 2003c; Li et al., 2003; Wang et al., 2005)

• 3D instantaneous shorelines can be derived almost automatically, utilizing image processing methods (Di et al., 2003c)

• Accuracy of the derived 3D shoreline was estimated to be 2-3 meters for 1-m Panchromatic IKONOS images


Rational Function Refinement of Satellite Sensor Model

• The studies examined vendor provided Rational Functions (RFs) for IKONOS satellite images, instead of rigorous sensor models.

• two methods to improve the accuracy of ground coordinates using RFs Li et al. (2002b).

– refinement of vendor provided RF (about 4.5 m RMSE)

– refinement of ground coordinates from vendor provided RF (less than 2 RMSE)


Shoreline Extraction from Satellite Images

• An image-matching technique was applied to assist in conjugate point matching (Li et al., 2002b).

– Manually select points on the first image

– Area-based matching to determine conjugate points on the second image

• Shoreline extraction using image processing techniques (Di et al., 2003c).

– Performed on 1 m resolution panchromatic and 4-m resolution multispectral IKONOS stereo images.

– Mean shift segmentation was applied on satellite images to segment an image into homogeneous regions.

– Accuracy of 3D shoreline was estimated to be 2-3 m for Pan images and about 8.5 m for multispectral images.


Shoreline Extraction using Image Processing Techniques (Di et al., 2003c)

Candidate polygons (left column) and refined shoreline (right column) (Di et al., 2003c)


VDatum

• VDatum is an important element in deriving TCS from digital models as it facilitates integration of vertical data from several sources.

• VDatum utilizes geoid and hydrodynamic models to forming relationship between vertical datums (Graham, Sault, and Bailey 2003)

• VDatum currently supports transformation for the many seaports around the United States and is to be expanded to cover most coasts around the U.S., Puerto Rico, and Hawaii by 2013 (NOAA, 2010f).


Shoreline Mapping from LiDAR using VDatum

Workflow for extracting lidar shorelines with VDatum (White et al., 2007)


Bathymetry from Satellite Imagery

• IKONOS image diginal numbers (DN) was converted to planetary reflectance values using


Bathymetry from Satellite Imagery (2)

• The spectral radiance, Lλ , was calculated using published calibration coefficient and image bandwidth as (Fleming, 2001)

• The depth information of 90 training points and 25 accuracy check points along the shoreline, seaward, were used to determine parameters of the model for generating the bathymetry.



• Water depth at point x can be estimated as (Muslim and Foody, 2008):



• L0 and Ld were estimated from a histogram of the reflectance of the water area in the image. The reflectance value from the lower part of the histogram was used as Ld and the upper part was used as L0.

• Parameter E’ was calculated from the water and air reflexive indices and the solar elevation angle (E).

• Constant was determined as a slope of a regression line from a plot of the logarithm of depth against the logarithm of reflectance obtained from 90 training points.


References (1)

Boak, E.H. and I.L. Turner, 2005. Shoreline definition and detection: a review, Journal of Coastal Research, 21: 688–703.

Graham, D., M. Sault, and J. Bailey, 2003. National Ocean Service shoreline – past, present, and future, Journal of Coastal Research, SI(38): 14-32.

Hess, K.W., 2004. Tidal datums and tide coordination, Journal of Coastal Research, SI(38): 33-43.

Hoja, D., S. Lehner, A. Niedermeier, and E. Romaneessen, 2000. DEM Generation from ERS SAR Shorelines Compared to Airborne Crosstrack InSAR DEMs in the German Bight. In Proceedings of the 20th International Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, July 24–28, 5: 1889–1891.

Lee, I.-C., B. Wu, and R. Li, 2009. Shoreline Extraction from the Integration of LiDAR Point Cloud Data and Aerial Orthophotos using Mean Shift Segmentation, American Society for Photogrammetry and Remote Sensing, p. 489.

Lee, I-C., L. Cheng, and R. Li, 2010. Optimal Parameter Determination for Mean-Shift Segmentation-Based Shoreline Extraction using LiDAR Data, Aerial Orthophotos, and Satellite Imagery. Proceedings of the ASPRS 2010 Annual Conference, 26-30 April, San Diego, CA, 8 p.

Li, R., R. Ma, and K. Di, 2002. Digital tide-coordinated shorelines, Journal of Marine Geodesy, 25(1–2): 27–36.

Li, R., K.W. Bedford, X. Niu, V. Velissariou, F. Zhou and S. Deshpande, 2005. Seamless Integration of Geospatial Data from Water to Land, Annual Project Report (1st year), Submitted to NGA, December 2005, 123p.


References (2)

Li, R., K.W. Bedford, X. Niu, V. Velissariou, F. Zhou and S. Deshpande, 2006. Seamless Integration of Geospatial Data from Water to Land, Annual Project Report (2nd year), Submitted to NGA, December 2006, 43p.

Li, D., S. Deshpande, R. Li, D. Chiu, and G. Agrawal, 2010. Quantifying Uncertainty in Shoreline Extraction, Submitted to ACM SIGSPATIAL GIS 2010.

Lipakis, M., N. Chrysoulakis, and Y. Kamarianakis, 2008. Shoreline extraction using satellite imagery. In: Pranzini, E. and Wetzel, E. (eds): Beach Erosion Monitoring. Results from BEACHMED/e-OpTIMAL Project (Optimization des Techniques Integrées de MonitorageAppliquées aux Lottoraux) INTERREG IIIC South, Nuova Grafica Fiorentina, Florence, Italy, pp. 81 – 95.

Liu, H., D. Sherman, and S. Gu, 2007. Automated extraction of shorelines from airborne light detection and ranging data and accuracy assessment based on Monte Carlo simulation, Journal of Coastal Research, 23: 1359-1369.

Mason, D. C., I. J. Davenport, G. J. Robinson, R. A. Flather, and B. S. McCartney, 1995. Construction of an inter-tidal digital elevation model by the ‘Water-Line’ Method, Geophysical Research Letters, 22(23): 3187-3190.

Mason, D.C., I. J. Davenport, and R. A. Flather, 1997. Interpolation of an intertidal digital elevation model from heighted shorelines: a case study in the western Wash. Estuarine, Coastal and Shelf Science, 45: 599-612.


References (3)

Mason, D. C., I. J. Davenport, R. A. Flather, and C. Gurney, 1998. A digital elevation model of the intertidal areas of the Wash produced by the waterline method, International Journal of Remote Sensing, 19: 1455-1460.

Mason, D.C., I. J. Davenport, R. A. Flather, C. Gurney, G. J. Robinson, and J. A. Smith, 2001. A sensitivity analysis of the waterline method of constructing a digital elevation model for intertidal areas in ERS SAR scene of Eastern England, Estuarine and CoastalShelf Science, 53: 759-778.

Morton, R. A., M. P. Leach, J. G. Paine, and M. A. Cardoza, 1993. Monitoring beach changes using GPS surveying techniques, Journal of Coastal Research, 9(3): 702-720.

Muslim, A. M., and G. M. Foody, 2008. DEM and bathymetry estimation for mapping a tide-coordinated shoreline from fine spatial resolution satellite sensor imagery. International Journal of Remote Sensing, 29: 4515-4536.

NOAA, 2000. Tidal Datums and Their Applications, NOAA Special Publication NOS CO-OPS 1, 112 p.

NOAA, 2011. Our restless tide, URL: http://co-ops.nos.noaa.gov/restles4.html (last accessed 01/21/2011)

Parrish, C. E., 2009. Lidar Shoreline Mapping in NOAA’s National Geodetic Survey and Results of Uncertainty Analysis, OSU Seminar, November 30, 2009.

Robertson, W. V., D. Whitman, K. Q. Zhang, and S. P. Leatherman, 2004. Mapping shoreline position using airborne laser altimetry, Journal of Coastal Research, 20: 884-892.


References (4)

Smith, J. T., 1981. A history of flying and photography in the Photogrammetry Division of the National Ocean Survey, 1919-79, U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Ocean Survey, 486p.

Stockdon, H. F., A. H. Sallengera, J. H. List, and R. A. Holman, 2002. Estimation of shoreline position and change using airborne topographic lidar data, Journal of Coastal Research, 18(3): 502-513.

USGS, 2010. SWASH: a New Method for Quantifying Coastal Change, URL: http://woodshole. er.usgs.gov/operations/swash/ (last accessed: 03/29/10).

White, S., 2007. Utilization of LiDAR and NOAA’s Vertical Datum Transformation Tool (VDatum) for Shoreline Delineation, Proceedings of the Marine Technology Society / IEEE OCEANS Conference, Vancouver, BC.

White, S.A., C.E. Parrish, B.R. Calder, S. Pe'eri, and Y. Rzhanov, 2010. Lidar-Derived National Shoreline: Empirical and Stochastic Uncertainty Analysis. Journal of Coastal Research.

Yeremy, M., A. Beaudoin, J. D. Beaudoin, and G. M. Walter, 2001. Global shoreline mapping from an airborne polarimetric SAR: assessment for RADARSAT 2 polarimetric mode, DefenceResearch Establishment, Ottawa, Canada, 67 p.

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