Shoreline Extraction and Change Detection using 1:5000 Scale Orthophoto Maps: A Case Study of Latvia-Riga

Abstract

Coastal management requires rapid, up-to-date, and correct information. Thus, the determination of coastal movements and its directions has primary importance for coastal managers. For monitoring the change of shorelines, remote sensing data, very high resolution aerial images and orthophoto maps are utilized for detections of change on shorelines. It is possible to monitor coastal changes by extracting the coastline from orthophoto maps. Along the Baltic Sea and Riga Gulf, Latvian coastline length is 496 km. It is rich of coastal resources and natural biodiversity. Around 120 km of coastline are affected by significant coastal changes caused by climate change, storms, erosion, human activities and other reasons and they must be monitored. In this study, an object-oriented approach has been proposed to detect shoreline and detect the changes by using 1:5000 scaled orthophoto maps of Riga-Latvia (3bands, R, G, and NIR) in the years of 2007 and 2013. As many of the authors have mentioned, object-oriented classification method can be more successful than the pixel-based methods especially for high resolution images to avoid mix-classification. In the presented study the eCognition object-oriented fuzzy image processing software has been used. The results were compared to the results derived from manual digitizing. Extracted and manually digitized shorelines have been divided in 5 m segments in x axis. The y coordinates of the new nodes were taken from the original ".dxf" file or computed by interpolation. Thus, the RMS errors of selected points were calculated.

The Efficiency of Random Forest Method for Shoreline Extraction From Landsat-8 and Gokturk-2 Imageries

“…LANDSAT-8 and GOKTURK-2 imageries are open data resources (Machado et al, 2014;Kalkan et al, 2015). Some of commonly used shoreline extraction methods are Unsupervised classification techniques (ISODATAIterative Self Organized Data Analysis) (Guariglia et al, 2006), normalized difference water index (NDWI) (Zheng et al, 2011), thresholding and morphological filtering techniques (Pardo Pascual et al, 2012), Wavelet transformation (Yu et al, 2013), active contour method (Shmittet al, 2015), genetic algorithm based methods (Yousef and Iftekharuddin, 2014), particle swarm optimization method (PSO) , Mean-shift segmentation (Bayram-b, vd, 2016), object oriented fuzzy classification methods (Bayram et al, 2015;Bayram et al 2008), normalized cut approach (Ding and Li, * Corresponding author 2014) . In this study, the shoreline of the Terkos/Istanbul has been extracted using Random Forest method (Breiman, 2001) from LANDSAT-8 and GOKTURK-2 imageries.…”

Shoreline Detection using Optical Remote Sensing: A Review

“…In [128], an object-oriented approach has been proposed to detect shoreline and its changes by using 1:5000 scaled orthophoto maps of Riga-Latvia (3bands, R, G, and NIR) in the years of 2007 and 2013.…”

“…[133] Principal Component Analysis The PCA allows the use of smaller databases and reduction of noise [120,124] Object-oriented classification It reduces salt-and-pepper effects commonly noted in pixel-based remote sensing image classification. [120,128,134] Texture analysis-based methods Texture analysis groups together a set of techniques allowing quantifying the different grey-levels present in an image in terms of intensity and distribution in order to calculate a number of parameters characteristic of the texture to be studied. It is a very important task, which is useful for image segmentation and object detection.…”

An Analysis of Shoreline Changes Using Combined Multitemporal Remote Sensing and Digital Evaluation Model

“…In mapping the shoreline changes, to date, lots of algorithms for research and separation of waterlines using remote sensing imagery with the indices of Normalized difference water index (NDWI) [12], Modified Normalized Difference Water Index (MNDWI) [13], automated water extraction index (AWEI) [14] are widely introduced over the world. To develop the waterlines, several methods applied such as Thesholding [15,16], Classification [17,18], or Band ratio techniques [19,20].Another aspect, the influences of tidal level on shoreline changes are also considered in several publications [21][22][23][24][25]. These studies are, however, only applied to shoreline positions where are often affected by tide.…”