Isaac Scientific Publishing
Geosciences Research
GR > Volume 2, Number 3, August 2017

Shorelines Extraction and Generalization Using Spatiotemporal Datasets

Download PDF  (460.1 KB)PP. 161-171,  Pub. Date:June 20, 2017
DOI: 10.22606/gr.2017.23002

Ahmed F. Elaksher
Civil Engineering Department, Cal Poly Pomona University, Pomona, CA 91768, USA
This paper discusses integrating multiple remotely sensed measurements, spatiotemporal databases, coastal hydrological modeling, and geospatial information analysis to study the impact of water level changes on the shoreline geometry in order to support coastal geospatial information systems and decision making. Different shorelines are generated using a CTM and a WSM. The CTM is generated using multiple remotely sensed datasets and spatiotemporal databases, while the WSM is provided using a coastal hydrological model. Two shoreline generalization schemes are implemented to simplify the shoreline geometry. Experiments for estimating the shoreline geometry at different water levels were performed using each generalization scheme. The relationship between the shoreline geometry and the water levels was studied. High correlation between water level changes and the changes in the shoreline geometry was observed. The proposed algorithm can be used in creating or updating coastal geospatial databases, managing the dynamic natural of shorelines, and making scientific decisions in coastal environments.
Bathymetry, DEM, extraction, generalization, statistics.
  • [1]  Lockwood, M., 1997. NSDI shoreline briefing to the FGDC coordination group, NOAA/NOS.
  • [2]  Thieler, E.R. and Danforth, W.W., 1994. Historical shoreline mapping (I): improving techniques and reducing positioning error. Coastal Research. 10(3), pp. 549-563.
  • [3]  Smith, G.L. and Zarillo, G.A., 1990. Calculating long-term shoreline recession rates using aerial photographic and beach profiling techniques. Coastal Research, 6(1), pp. 111-120.
  • [4]  Pradjoko, E., & Tanaka, H. (2011). Aerial photograph of Sendai Coast for shoreline behavior analysis. Coastal Engineering Proceedings, 1(32), 92.
  • [5]  Sesli, F. A. (2010). Mapping and monitoring temporal changes for coastline and coastal area by using aerial data images and digital photogrammetry: A case study from Samsun, Turkey. International Journal of Physical Sciences, 5(10), 1567-1575.
  • [6]  García-Rubio, G., Huntley, D., & Russell, P. (2015). Evaluating shoreline identification using optical satellite images. Marine Geology, 359, 96-105.
  • [7]  Chen, W. W., & Chang, H. K. (2009). Estimation of shoreline position and change from satellite images considering tidal variation. Estuarine, Coastal and Shelf Science, 84(1), 54-60.
  • [8]  Tarmizi, N. M., Samad, A. M., & Yusop, M. S. M. (2014, March). Shoreline data extraction from QuickBird satellite image using semi-automatic technique. In Signal Processing & its Applications (CSPA), 2014 IEEE 10th International Colloquium on (pp. 157-162). IEEE.
  • [9]  Sesli, F. A., Karsli, F., Colkesen, I., & Akyol, N. (2009). Monitoring the changing position of coastlines using aerial and satellite image data: an example from the eastern coast of Trabzon, Turkey. Environmental Monitoring and Assessment, 153(1), 391-403.
  • [10]  White, S. A., Parrish, C. E., Calder, B. R., Pe'eri, S., & Rzhanov, Y. (2011). Lidar-derived national shoreline: empirical and stochastic uncertainty analyses. Journal of Coastal Research, 62-74.
  • [11]  Brock, J. C., & Purkis, S. J. (2009). The emerging role of lidar remote sensing in coastal research and resource management. Journal of Coastal Research, 1-5.
  • [12]  Bruno, M. F., Molfetta, M. G., Mossa, M., Nutricato, R., Morea, A., & Chiaradia, M. T. (2016). Coastal observation through COSMO-SkyMed high-resolution SAR images. Journal of Coastal Research, 75(sp1), 795-799.
  • [13]  Elaksher, A. F. (2008). Fusion of hyperspectral images and lidar-based dems for coastal mapping. Optics and Lasers in Engineering, 46(7), 493-498.
  • [14]  Kerfoot, W. C., Hobmeier, M. M., Yousef, F., Green, S. A., Regis, R., Brooks, C. N., & Reif, M. (2014). Light detection and ranging (LiDAR) and multispectral scanner (MSS) studies examine coastal environments influenced by mining. ISPRS International Journal of Geo-Information, 3(1), 66-95.
  • [15]  Bilskie, M. V., Hagen, S. C., Medeiros, S. C., & Passeri, D. L. (2014). Dynamics of sea level rise and coastal flooding on a changing landscape. Geophysical Research Letters, 41(3), 927-934.
  • [16]  Hajek, E., Paola, C., Petter, A., Alabbad, A., & Kim, W. (2014). Amplification of shoreline response to sea-level change by back-tilted subsidence. Journal of Sedimentary Research, 84(6), 470-474.
  • [17]  Jones, D.M. (2009). Lake Erie Water Levels, online:, last accessed: March 5th, 2017.
  • [18]  Wilcox, D. A., Thompson, T. A., Booth, R. K., & Nicholas, J. R. (2007). Lake-level variability and water availability in the Great Lakes. US Geological Survey Circular 1311.
  • [19]  Highman, T. A. (1997). A Study of Soil Joints in Relation to Bluff Erosion Along Lake Erie Shoreline, Northeast Ohio. Kent, OH: Kent State University.
  • [20]  Holcombe, T. L., Taylor, L. A., Reid, D. F., Warren, J. S., Vincent, P. A., & Herdendorf, C. E. (2003). Revised Lake Erie postglacial lake level history based on new detailed bathymetry. Journal of Great Lakes Research, 29(4), 681-704.
  • [21]  Cressie, N. A., 1993: Statistics for Spatial Data, Revised Edition, A Wiley-Interscience Publication, New York, 887 pp.
  • [22]  Heiskanen, W. A., and Moritz, H. (1967).Physical Geodesy. W. H. Freeman and Co., San Francisco, Calif
  • [23]  Li, R., Di, K., and Ma, R., 2002. Digital tide-coordinated shoreline. Marine Geodesy, (25), pp. 27-36.
Copyright © 2017 Isaac Scientific Publishing Co. All rights reserved.