Analysis of Coastline Extraction from Landsat-8 OLI Imagery

Abstract

Abstract:Coastline extraction is a fundamental work for coastal resource management, coastal environmental protection and coastal sustainable development. Due to the free access and long-term record, Landsat series images have the potential to be used for coastline extraction. However, dynamic features of different types of coastlines (e.g., rocky, sandy, artificial), caused by sea level fluctuation from tidal, storm and reclamation, make it difficult to be accurately extracted with coarse spatial resolution, e.g., 30 m, of Landsat images. To access this problem, we analyze the performance of coastline extraction by integrating downscaling, pansharpening and water index approaches in increasing the accuracy of coastline extraction from the latest Landsat-8 Operational Land Imager (OLI) imagery. In order to prove the availability of the proposed method, we designed three strategies: (1) Strategy 1 uses the traditional water index method to extract coastline directly from original 30 m Landsat-8 OLI multispectral (MS) image; (2) Strategy 2 extracts coastlines from 15 m fused MS images generated by integrating 15 m panchromatic (PAN) band and 30 m MS image with ten pansharpening algorithms; (3) Strategy 3 first downscales the PAN band to a finer spatial resolution (e.g., 7.5 m) band, and then extracts coastlines from pansharpened MS images generated by integrating downscaled spatial resolution PAN band and 30 m MS image with ten pansharpening algorithms. Using the coastline extracted from ZiYuan-3 (ZY-3) 5.8 m MS image as reference, accuracies of coastlines extracted from MS images in three strategies were validated visually and quantitatively. The results show that, compared with coastline extracted directly from 30 m Landsat-8 MS image (strategy 1), strategy 3 achieves the best accuracies with optimal mean net shoreline movement (MNSM) of −2.54 m and optimal mean absolute difference (MAD) of 11.26 m, followed by coastlines extracted in strategy 2 with optimal MNSM of −4.23 m and optimal MAD of 13.54 m. Further comparisons with single-band thresholding (Band 6), AWEI, and ISODATA also confirmed the superiority of strategy 3. For the various used pansharpening algorithms, five multiresolution analysis MRA-based pansharpening algorithms are more efficient than the component substitution CS-based pansharpening algorithms for coastline extraction from Landsat-8 OLI imagery. Among the five MRA-based fusion methods, the coastlines extracted from the fused images generated by Indusion, additive à trous wavelet transform (ATWT) and additive wavelet luminance proportional (AWLP) produced the most accurate and visually realistic representation. Therefore, pansharpening approaches can improve the accuracy of coastline extraction from Landsat-8 OLI imagery, and downscaling the PAN band to finer spatial resolution is able to further improve the coastline extraction accuracy, especially in crenulated coasts.

Automatic Semi-Global Artificial Shoreline Subpixel Localization Algorithm for Landsat Imagery

“…The coastline, the boundary of land and sea, is one of the 27 most important land surface features, and is vulnerable to natural processes such as coastal erosion/accretion, sea level changes and human activities [1]. Coastline mapping is, therefore, becoming a fundamental work for coastal erosion monitoring, coastal resource management, coastal environmental protection and coastal sustainable development [2][3][4][5][6].…”

“…With the advantages of cost-effectiveness and large spatial and temporal scales, satellite remote sensing data have been used widely for coastline mapping [1,[7][8][9]. When shoreline changes are sufficiently large (several tens of meters), satellite remote sensing can enable semi-automated comparison of large-scale areas by providing a common protocol for all sites [10], thus making comparisons consistent [11].…”

A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level

“…To overcome the restriction of coarse spatial resolution, different procedures have sought to increase accuracy at sub-pixel level [36]. Liu et al [37] compared three main processing options in which an original Landsat 8 (L8) was processed (30 m/pixel) by being pansharpened with its panchromatic band (15 m/pixel) and then with the same band upsampled to 7.5 m/pixel. Liu et al [38] pansharpened Landsat 7 (L7) and L8 multispectral bands and, after several segmentation steps, looked for the sub-pixel shoreline using a variation of marching squares with a linear interpolation (MSI) approach as proposed by [39].…”

Coastline Zone Extraction Using Landsat-8 Oli Imagery, Case Study: Bodrum Peninsula, Turkey

“…Density slicing methods for coastline delineation using single band Landsat TM; multiband methods to extract the water area (Du., Z., et al 2012;Mc Feeter, 1996;Xu, 2006;Wang et al, 2017;Feyisa et al, 2014) band rationing (Alesheikh et al, 2007;Niya et al, 2013) classification (Gurglia et al 2006Ekercin 2007;Garcia et al, 2015) on screen digitizing (Goksel et al, 2001;Sameh et al, 2017;Niya et al, 2013) are methods to identify coastline from multispectral satellite images. This study investigates extraction coastline of Bodrum Peninsula with using three commonly used techniques; Automatic Water Extraction Index (AWEI) (Feyisa et al, 2014;Xia et al, 2018;Liu et al, 2017) Iterative Self-Organizing Data Analysis Technique (ISODATA) unsupervised classification technique (Papakonstantinou et al, 2016) and on screen digitizing method in order to determine land-sea boundary line and its length, and area of the Bodrum Peninsula. Results of coastline length and land areas of Bodrum by using AWEI, ISODATA and on-screen digitizing are compared with each other.…”