Coastline Fractal Dimension of Mainland, Island, and Estuaries Using Multi-temporal Landsat Remote Sensing Data from 1978 to 2018: A Case Study of the Pearl River Estuary Area

Abstract

The Pearl River Estuary Area was selected for this study. For the past 40 years, it has been one of the most complex coasts in China, yet few studies have analyzed the complexity and variations of the area’s different coastlines. In this investigation, the coastlines of the Pearl River Estuary Area were extracted from multi-temporal Landsat remote sensing data from 1978, 1988, 1997, 2008, and 2018. The coastline of this area was classified into mainland, island, and estuarine. To obtain more detailed results of the mainland and island, we regarded this area as the main body, rezoned into different parts. The box-counting dimension was applied to compute the bidimensional (2D) fractal dimension. Coastline length and the fractal dimension of different types of coastline and different parts of the main body were calculated and compared. The fractal dimension of the Pearl River Estuary Area was found to have increased significantly, from 1.228 to 1.263, and coastline length also increased during the study period. The island and mainland showed the most complex coastlines, while estuaries showed the least complexity during the past forty years. A positive correlation was found between length and 2D-fractal dimension in some parts of the study area. Land reclamation had the strongest influence on fractal dimension variations.

Monitoring Sand Spit Variability Using Sentinel-2 and Google Earth Engine in a Mediterranean Estuary

“…By applying remote sensing techniques, consistent and robust methodologies for sea-land mapping and coastline detection were recently obtained on a global scale [22,25,29,48,60,61]. However, heterogeneity of sea-land mapping and coastline delineation methods and the difficult access to these tools by coastal managers usually restricts their use [24,35,36,40,62,63]. The potential of GEE has already been demonstrated in previous studies [37,38,60,64,65], and we used S2 images within GEE in order to ensure accuracy and robust performance, as well as a user-friendly and accessible tool for coastal managers.…”

İzmit Körfezi Doğu Kıyısındaki Doğal ve Antropojenik Kökenli Değişimlerin DSAS Aracı İle Analizi

“…Bu nedenden dolayı yaşanması muhtemel sorunların önüne geçilmesi, kıyının dinamik işleyiş yapısının tahmin edilmesi ve diğer çalışma alanlarında kullanılmak üzere Dünya'da ve Türkiye'de kıyı değişimleri kapsamında çok farklı teknikler kullanılarak çalışmalar yapılmaktadır (Tağıl & Cürebal, 2005;Kuleli, 2010;Darwish vd., 2017;Çoban vd., 2020;Samra & Ali, 2021;Alevkayalı vd., 2023;Kazı & Karabulut, 2023;Öztürk & Uzun, 2023). Yapılan çalışmalarda farklı çözünürlükteki uydu görüntüleri Coğrafi Bilgi Sistemleri (CBS) ve Uzaktan Algılama (UA) teknikleri kullanılarak incelenmekte ve kıyının farklı değişimleri analiz edilmektedir (Ataol vd., 2019;Hu & Wang, 2020;Gómez-Pazo vd., 2022;Lazuardi vd., 2022;Siyal vd., 2022;Wu vd., 2022;Kılar, 2023;Uzun, 2023;Tağıl vd., 2023). Bu analizlerden biri de Amerika Birleşik Devletler Jeoloji Araştırma Kurumu (United States Geological Survey -USGS) ürünü olan ve ArcGIS yazılımına eklenti sağlayan Sayısal Kıyı Çizgisi Analiz Sistemi'dir (DSAS -Digital Shoreline Analysis System).…”

Analysis of Manyas Lake Surface Area and Shoreline Change Over Various Periods with DSAS Tool

“…Detection of shoreline changes is carried out using Geographic Information Systems (GIS) and Remote Sensing (RS) techniques through maps, satellite images, LIDAR and UAV images (Hu & Wang, 2020). In analysing the data, access to images at the desired date, the width of the study area, resolution and cost are very important (Paz-Delgado et al, 2022).…”