Scholarly record
DATA SOURCE EVALUATION FOR SHORELINE DELINIATION APPLICATIONS
Abstract
This paper proposes an evaluation of data acquired with various sensors and used in coastal water segmentation applications. Correct monitoring of coastal changes in dynamic coastal environments strongly depends on accurate and frequent detection of shoreline position. Automatic shoreline delineation methods are preferable, especially in terms of time, cost, labor intensiveness and difficulties of in-situ measurements. Two main issues have been encountered within this application field, the quality of data and the segmentation algorithms. In this work, potential benefits of various data sources including optical and active sensors for extraction of shorelines have been investigated. The goal with shoreline detection from digital data sources is to obtain information as efficiently as possible and as reliably as necessary. Starting with that observation the paper discusses the effectiveness of coastal information extraction provided different data sources. This question is especially important to address since we observe a fast development of high spatial resolution data acquisition. There are many of segmentation algorithms described in the field of image processing and yet there is currently no single theory or method, no universal segmentation framework, that can be applied on all images to precisely and robustly extract shorelines. Nether there is a uniform standard for the assessment of segmentation results, and this process still largely relies on visual analysis and personal judgment. Out of myriads of image segmentation algorithms, we chose the most frequently and successfully applied within the application field and considering the data sources. In optical sensor data cases, the most frequently used methods are NDWI (Normalized Difference Water Index) and thresholding techniques. We do not aim to create yet another method to segment out the particular objects from remotely sensed data and then tailor it to work efficiently on that data set. Instead, we evaluate the data quality regarding the given application field. The case study is carried out on a 10 km coastal stretch facing the Baltic Sea (Sweden) and belonging to the Municipality of GпїЅvle. In citu measurements were acquired to evaluate the extracted coastal lines and comparisons with reference were performed based on the average mean distance. A conclusion is done regarding the most reliable data source for this particular application of shoreline delineation.
Publication Impact Profile
Publication details
References15
Brinkmann, W. A. Causes of variability in monthly Great Lakes water supplies and lake levels. Climate Research, vol. 15(2), 2000, pp.151-160.
R. M. Goncalves and J. L. Awange. (2017) Three Most Widely Used GNSS-Based Shoreline Monitoring Methods to Support Integrated Coastal Zone Management Policies. Journal of Surveying Engineering, 05017003. Online publication date: 27-Feb-2017.
Yousef, A., Iftekharuddin, K. Shoreline extraction from the fusion of LiDAR DEM data and aerial images using mutual information and genetic algorithms. International Joint Conference on Neural Networks (IJCNN), Neural Networks (IJCNN), 2014 International Joint Conference on, pp.1007-1014. DOI: 10.1109/IJCNN.2014.6889863.
Wernette, P., Shortridge, A., Lusch, D.P. and Arbogast, A.F. Accounting for positional uncertainty in historical shoreline change analysis without ground reference information. International Journal of Remote Sensing, vol. 38:13, 2017, pp. 3906- 3922. Online publication date: 3-Apr-2017.
Kermani, S., Boutiba, M., Guendouz, M., Guettouche, M.S. and Khelfani, D. (2016) Detection and analysis of shoreline changes using geospatial tools and automatic computation: Case of jijelian sandy coast (East Algeria). Ocean & Coastal Management Vol.132, pp. 46-58. Online publication date: 1-Nov-2016.
Blain, C., Linzell, R., & McKay, P. (n.d). Simple Methodology for Deriving Continuous Shorelines from Imagery: Application to Rivers. Journal Of Waterway Port Coastal And Ocean Engineering, vol. 139(5), pp. 365-382.
Pardo-Pascual, J. E., Almonacid-Caballer, J., Ruiz, L. A., & Palomar-Vázquez, J. (2012). Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision. Remote Sensing Of Environment, vol.123, pp.1-11. DOI: 10.1016/j.rse.2012.02.024.
Gonçalves, G. )., Sousa, E. )., Figueiredo, I. )., & Duro, N. ). Automatic extraction of tide-coordinated shoreline using open source software and Landsat imagery. International Archives Of The Photogrammetry, Remote Sensing And Spatial Information Sciences - ISPRS Archives, vol. 40(7W3), 2015, pp. 953-957. DOI: 10.5194/isprsarchives-XL-7-W3-953-2015.
Al Fugura, A., Billa, L., & Pradhan, B. Semi-automated procedures for shoreline extraction using single RADARSAT-1 SAR image. Estuarine, Coastal And Shelf Science, 2011, pp. 95395-400. DOI: 10.1016/j.ecss.2011.10.009.
Lee, I.-C., Cheng, L. and Li, R. Optimal parameter determination for mean-shift segmentation-based shoreline extraction using lidar data, aerial orthophotos, and satellite imagery, 2013, BASE, EBSCOhost (accessed April 25, 2017).
Wu, H., Liu, C., Zhang, Y. and Sun, W. “Water feature extraction from aerial-image fused with airborne lidar data,” in Urban Remote Sensing Event, IEEE, 2009, pp. 1–7.
Åhlén, J., Seipel, S., Segmentation of shadows and water bodies in high resolution images using ancillary data, 16th International Multidisciplinary Scientific GeoConference SGEM 2016: SGEM2016 Conference Proceedings : Book 2, 2016, Vol. 1, 827-834.
Zhang, H., Jiang Q., Xu J. Coastline Extraction Using Support Vector Machine from Remote Sensing Image, Journal of multimedia, vol. 8(2), 2013, pp.175-182.
Hu, X., Qin, Z., Wang, J. and Su, G. Coastline Extraction on Remote Sensing Image Using Gaussian Process Classification, Atlantis Press, 2015.
Cassé, C., Viet, P.B., Nhung, P., Dao, H., International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences, 2012.
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.
