Assessment and Forecast of Shoreline Change Using Geo-Spatial Techniques in the Gulf of California

Abstract

In coastal regions, the combined effects of natural processes, human activity, and climate change have caused shoreline changes that may increase in the future. The assessment of these changes is essential for forecasting their future position for proper management. In this context, shoreline changes in the Gulf of California (GC), Mexico, have received little attention and no previous studies have addressed future forecasting. In this study, the researchers assessed the historical shoreline changes to forecast the long-term shoreline positions. To address this, shoreline data were obtained from Landsat satellite images for the years 1981, 1993, 2004, 2010, and 2020. The Net Shoreline Movement (NSM), Linear Regression Rate (LRR), End Point Rate (EPR), and Weighted Linear Regression (WLR) geo-spatial techniques were applied to estimate the shoreline change rate by using a Digital Shoreline Analysis System (DSAS) in the GIS environment. A Kalman filter model was used to forecast the position of the shoreline for the years 2030 and 2050. The results show that approximately 72% of the GC shoreline is undergoing steady erosion, and this trend is continuing in the future. This study has provided valuable and comprehensive baseline information on the state of the shoreline in the GC that can guide coastal engineers, coastal managers, and policymakers in Mexico to manage the risk. It also provides both long-term and large-scale continuous datasets that are essential for future studies focused on improving the shoreline forecast models.

Coastal resilience and shoreline dynamics: assessing the impact of a hybrid beach restoration strategy in Puducherry, India

“…Shoreline is the dynamic line of interface that distinguishes land and water, which has been recognized as a significant instrument for determining the spatial and temporal fluctuation of the coastline (Hayden et al, 1978;Pajak and Leatherman, 2002;Boak and Turner, 2005;Maiti and Bhattacharya, 2009;Cui and Li, 2011). Both natural (e.g., tides, waves, rivers, storms, geology, geomorphology, sea level rise) and anthropogenic activity interfering with natural coastal processes are accountable for the dynamic shoreline migration pattern (Palanques and Guilleń, 1998;Morton et al, 2005;Aedla et al, 2015;Chenthamil Selvan et al, 2016;Zambrano-Medina et al, 2023).…”

Analysis of shoreline changes in the severe storm surge region of upper Coromandel coast, India, using Remote sensing (RS) and Geographic information system (GIS)

“…Several studies on the determination of shoreline change have recently been undertaken using DSAS in combination with RS and GIS. For example, Shoreline changes analysis in Kuwaru coastal area (Mutaqin, 2017), coastline changes analysis for the Qingdao coastline (Yasir et al, 2020), shoreline mapping and coastal change studies along Chennai Coast (Jose et al, 2018), shoreline change monitoring in Nellore coast (Kannan et al, 2016), assessment of shoreline change along Nagapattinam coast (Mageswaran et al, 2015), assessment and forecast of shoreline change using geospatial techniques in the Gulf of California (Zambrano-Medina et al, 2023). The present research uses multi-temporal Landsat satellite imageries and the DSAS extension of ArcGIS software to explore the change in the coastline of Bapatla that has occurred due to erosion and accretion.…”