[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (10)

Search Parameters:
Keywords = net shoreline movement (NSM)

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
17 pages, 17273 KiB  
Article
Monitoring Coastal Evolution and Geomorphological Processes Using Time-Series Remote Sensing and Geospatial Analysis: Application Between Cape Serrat and Kef Abbed, Northern Tunisia
by Zeineb Kassouk, Emna Ayari, Benoit Deffontaines and Mohamed Ouaja
Remote Sens. 2024, 16(20), 3895; https://doi.org/10.3390/rs16203895 - 19 Oct 2024
Viewed by 1337
Abstract
The monitoring of coastal evolution (coastline and associated geomorphological features) caused by episodic and persistent processes associated with climatic and anthropic activities is required for coastal management decisions. The availability of open access, remotely sensed data with increasing spatial, temporal, and spectral resolutions, [...] Read more.
The monitoring of coastal evolution (coastline and associated geomorphological features) caused by episodic and persistent processes associated with climatic and anthropic activities is required for coastal management decisions. The availability of open access, remotely sensed data with increasing spatial, temporal, and spectral resolutions, is promising in this context. The coastline of Northern Tunisia is currently showing geomorphic process, such as increasing erosion associated with lateral sedimentation. This study aims to investigate the potential of time-series optical data, namely Landsat (from 1985–2019) and Google Earth® satellite imagery (from 2007 to 2023), to analyze shoreline changes and morphosedimentary and geomorphological processes between Cape Serrat and Kef Abbed, Northern Tunisia. The Digital Shoreline Analysis System (DSAS) was used to quantify the multitemporal rates of shoreline using two metrics: the net shoreline movement (NSM) and the end-point rate (EPR). Erosion was observed around the tombolo and near river mouths, exacerbated by the presence of surrounding dams, where the NSM is up to −8.31 m/year. Despite a total NSM of −15 m, seasonal dynamics revealed a maximum erosion in winter (71% negative NSM) and accretion in spring (57% positive NSM). The effects of currents, winds, and dams on dune dynamics were studied using historical images of Google Earth®. In the period from 1994 to 2023, the area is marked by dune face retreat and removal in more than 40% of the site, showing the increasing erosion. At finer spatial resolution and according to the synergy of field observations and photointerpretation, four key geomorphic processes shaping the coastline were identified: wave/tide action, wind transport, pedogenesis, and deposition. Given the frequent changes in coastal areas, this method facilitates the maintenance and updating of coastline databases, which are essential for analyzing the impacts of the sea level rise in the southern Mediterranean region. Furthermore, the developed approach could be implemented with a range of forecast scenarios to simulate the impacts of a higher future sea-level enhanced climate change. Full article
(This article belongs to the Special Issue Advances in Remote Sensing in Coastal Geomorphology (Third Edition))
Show Figures

Figure 1

Figure 1
<p>Location of the studied northern Tunisian coastal area (southern Mediterranean seashore) between Cape Serrat and Ragoubet El Golea, showing (blue rectangle) the three main dams and rivers, including Ziatine, Gamgoum, and El Harka. The study area was divided into six zones, according to their morphologies: Three zones are characterized by rocky coasts, a sandy coastal area with a tombolo, and fixed and (semi-)fixed dune zones. The background is the MapTiler Satellite map.</p>
Full article ">Figure 2
<p>Flowchart related to Landsat data analysis for the years 1985–2019. It involves the following: (<b>a</b>) the pre-processes steps: radiometric calibration, geometric, and atmospheric corrections; (<b>b</b>) the multi-time coastline extraction based on the Tasseled map transformation (greenness/wetness data extraction); and (<b>c</b>) coastline evolution.</p>
Full article ">Figure 3
<p>Net shoreline movement (NSM) in the period from 1985 to 2019 between Cape Serrat and Ragoubet el Golea points. Shoreline retreat is indicated by red lines, while green lines represent relatively unchanged areas. Shoreline advance is indicated by blue lines. The background is the MapTiler Topo map.</p>
Full article ">Figure 4
<p>Erosion forms are mainly identified around the tombolo areas, indicated by red lines.</p>
Full article ">Figure 5
<p>(<b>a</b>) Seasonal sedimentary balance of shoreline movement based on near-shore movement values (the distance between the oldest and youngest shorelines), net shoreline movement and (<b>b</b>) seasonal NSM variation showing the balance between erosion (blue color) and accretion (red color) and the total NSM value.</p>
Full article ">Figure 6
<p>Spatial distribution of dunes as examples of different stages, from stable and vegetated dunes to system instability and the development of a mobile transgressive dune system. High sand dune (Level 1 or L1); incipient dune, L2, and foredune (LN) (background map is a GoogleEarth<sup>®</sup> image of 2019).</p>
Full article ">Figure 7
<p>The disappearance of the incipient dune between 1994 and 2018, based on two satellite GoogleEarth<sup>®</sup> views. The phenomena highlight the important erosion process around the tombolo.</p>
Full article ">Figure 8
<p>Example of wind action on the dunes based on GoogleEarth<sup>®</sup> time-series imagery (a view of zone 2 (<a href="#remotesensing-16-03895-f001" class="html-fig">Figure 1</a>)). Green arrows highlight the perpendicular direction of the wind reactivation by a secondary wind from the north–east (NE) of the old dunes under the dominant north–west (NW) wind.</p>
Full article ">Figure 9
<p>An example of dune zonation in the study area in relation to wind direction. Five categories of dunes were identified, including near-shore zones, high sand dunes, incipient dunes, foredunes, and transgressive dunes.</p>
Full article ">Figure 10
<p>Examples of two dune systems in the study area: (<b>a</b>) the long dunes form caused by the interaction of multiple coastal currents or wind directions; (<b>b</b>) the short dunes form under the influence of a single, dominant current and wind direction. The white line in the sea (<b>a</b>,<b>b</b>) represents the coastal current direction.</p>
Full article ">Figure 11
<p>The retreat and removal of dunes (<b>a</b>). A series of parallel dune ridges, with the oldest dune ridges located furthest inland. (<b>b</b>) Formation and growth of flat dune deposits in zone 5 (<a href="#remotesensing-16-03895-f001" class="html-fig">Figure 1</a>), characterized by semi-fixed dunes.</p>
Full article ">Figure 12
<p>The coastal landscape in Cape Serrat (<a href="#remotesensing-16-03895-f001" class="html-fig">Figure 1</a>, zone 1) (<b>a</b>), showing the reshaping of the rocky shoreline from 1994 to 2019. Sedimentary rock layers with visible ripple marks, highlighting geomorphological features caused by weathering and erosion in the cliff, and the abrasion phenomena in the cliff (<b>b</b>).</p>
Full article ">Figure 13
<p>The dynamics of coastal erosion and dune dynamics processes, particularly in relation to waves action, dune formations, and stability. (<b>a</b>) shows the coastal features (dunes, inshore sand deposits); (<b>b</b>) the relationship between water level and micro-cliff formation caused by erosion; and (<b>c</b>) illustrates the effects of storm wave attacks on dunes.</p>
Full article ">
16 pages, 6218 KiB  
Article
Using RS and GIS Techniques to Assess and Monitor Coastal Changes of Coastal Islands in the Marine Environment of a Humid Tropical Region
by Muhamed Fasil Chettiyam Thodi, Girish Gopinath, Udayar Pillai Surendran, Pranav Prem, Nadhir Al-Ansari and Mohamed A. Mattar
Water 2023, 15(21), 3819; https://doi.org/10.3390/w15213819 - 1 Nov 2023
Cited by 3 | Viewed by 3126
Abstract
Vypin, Vallarpadam, and Bolgatty are significant tropical coastal islands situated in the humid tropical Kerala region of India, notable for their environmental sensitivity. This study conducted a comprehensive assessment of shoreline alterations on these islands by integrating Remote Sensing (RS) and Geographic Information [...] Read more.
Vypin, Vallarpadam, and Bolgatty are significant tropical coastal islands situated in the humid tropical Kerala region of India, notable for their environmental sensitivity. This study conducted a comprehensive assessment of shoreline alterations on these islands by integrating Remote Sensing (RS) and Geographic Information Systems (GIS) techniques. Utilizing satellite imagery from the LANDSAT series with a spatial resolution of 30 m, the analysis spanned the years from 1973 to 2019. The Digital Shoreline Analysis System (DSAS) tool, integrated into the ArcGIS software, was employed to monitor and analyze shoreline shifts, encompassing erosion and accretion. Various statistical parameters, including Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR), were utilized to evaluate these changes. Additionally, the study aimed to discern the root causes of shoreline modifications in the study area, encompassing disturbances and the construction of new structures on these islands. The results conclusively demonstrated the substantial impact endured by these coastal islands, with accretion on both sides leading to the creation of new landmasses. This manuscript effectively illustrates that these islands have experienced marine transgression, notably evidenced by accretion. Anthropogenic activities were identified as the primary drivers behind the observed shoreline changes, underscoring the need for careful management and sustainable practices in these fragile coastal ecosystems. Full article
Show Figures

Figure 1

Figure 1
<p>The study area (A) Vypin Island (B) Vallarpadam Island (C) Bolgatty Island.</p>
Full article ">Figure 2
<p>Flow chart describing the methodology.</p>
Full article ">Figure 3
<p>Shoreline map classification on EPR LRR and NSM on the three islands. (<b>A</b>) Vypin Island (<b>B</b>) Vallarpadam Island (<b>C</b>) Bolgatty Island.</p>
Full article ">Figure 4
<p>Shoreline changes in three islands (<b>a</b>) line diagram (<b>b</b>) Lines depicted over the island.</p>
Full article ">Figure 5
<p>Show the Google earth images southern part of three islands during 2001, 2007, 2015 and 2019.</p>
Full article ">Figure 6
<p>Graph showing the net shoreline movement (NSM) for three islands (<b>A</b>) Vypin, (<b>B</b>) Vallarpadam, (<b>C</b>) Bolgatty island.</p>
Full article ">Figure 7
<p>Graph showing the end point rate (EPR) for three islands (<b>A</b>) Vypin, (<b>B</b>) Vallarpadam, (<b>C</b>) Bolgatty island.</p>
Full article ">Figure 8
<p>Graph showing the linear regression rate (LRR) for three islands (<b>A</b>) Vypin, (<b>B</b>) Vallarpadam, (<b>C</b>) Bolgatty island.</p>
Full article ">
20 pages, 18038 KiB  
Article
Decoding Chambal River Shoreline Transformations: A Comprehensive Analysis Using Remote Sensing, GIS, and DSAS
by Saurabh Singh, Gowhar Meraj, Pankaj Kumar, Suraj Kumar Singh, Shruti Kanga, Brian Alan Johnson, Deepak Kumar Prajapat, Jatan Debnath and Dhrubajyoti Sahariah
Water 2023, 15(9), 1793; https://doi.org/10.3390/w15091793 - 7 May 2023
Cited by 20 | Viewed by 5302
Abstract
Illegal sand mining has been identified as a significant cause of harm to riverbanks, as it leads to excessive removal of sand from rivers and negatively impacts river shorelines. This investigation aimed to identify instances of shoreline erosion and accretion at illegal sand [...] Read more.
Illegal sand mining has been identified as a significant cause of harm to riverbanks, as it leads to excessive removal of sand from rivers and negatively impacts river shorelines. This investigation aimed to identify instances of shoreline erosion and accretion at illegal sand mining sites along the Chambal River. These sites were selected based on a report submitted by the Director of the National Chambal Sanctuary (NCS) to the National Green Tribunal (NGT) of India. The digital shoreline analysis system (DSAS v5.1) was used during the elapsed period from 1990 to 2020. Three statistical parameters used in DSAS—the shoreline change envelope (SCE), endpoint rate (EPR), and net shoreline movement (NSM)—quantify the rates of shoreline changes in the form of erosion and accretion patterns. To carry out this study, Landsat imagery data (T.M., ETM+, and OLI) and Sentinel-2A/MSI from 1990 to 2020 were used to analyze river shoreline erosion and accretion. The normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were used to detect riverbanks in satellite images. The investigation results indicated that erosion was observed at all illegal mining sites, with the highest erosion rate of 1.26 m/year at the Sewarpali site. On the other hand, the highest accretion was identified at the Chandilpura site, with a rate of 0.63 m/year. We observed significant changes in river shorelines at illegal mining and unmined sites. Erosion and accretion at unmined sites are recorded at −0.18 m/year and 0.19 m/year, respectively, which are minor compared to mining sites. This study’s findings on the effects of illegal sand mining on river shorelines will be helpful in the sustainable management and conservation of river ecosystems. These results can also help to develop and implement river sand mining policies that protect river ecosystems from the long-term effects of illegal sand mining. Full article
(This article belongs to the Special Issue Advances in Hydrology: Flow and Velocity Analysis in Rivers)
Show Figures

Figure 1

Figure 1
<p>Illustration of a sand and gravel streambed and the impact of illegal sand mining. (<b>a</b>) Nick points, where the streambed is abruptly lowered due to pit excavation, can be seen. (<b>b</b>) Downstream from the nick point, the streambed deteriorates when flow rates are high due to the lack of sediment replenishment caused by the mining.</p>
Full article ">Figure 2
<p>Comparison of channel cross-sections. (<b>a</b>) shows a typical sand–gravel bar inside the low-flow channel, while (<b>b</b>) depicts the impact of uncontrolled mining resulting in a large shallow channel with severe bank erosion.</p>
Full article ">Figure 3
<p>Illegal sand mining sites along the Chambal River. (<b>a</b>) Map of India ap providing regional context, (<b>b</b>) illegal sand mining site in Dholpur and Morena regions within the Chambal River, and (<b>c</b>) inset showing stretch of illegal mining sites and unmined site. Map coordinates are UTM WGS 84.</p>
Full article ">Figure 4
<p>Flowchart diagram illustrating the methodology for evaluating erosion and accretion at mining sites. The diagram outlines the steps to analyze the satellite images and geospatial data, including spectral indices and a digital shoreline analysis system (DSAS) to calculate shoreline erosion and accretion rates.</p>
Full article ">Figure 5
<p>NDWI and MNDWI cover a 10 km riverbank buffer for 20 years. Remote sensing indices including the NDWI and MNDWI identify and map open water bodies such as rivers and wetlands and estimate water extent changes over time. (<b>a</b>) NDWI 2000, (<b>b</b>) MNDWI 2000, (<b>c</b>) NDWI 2010, (<b>d</b>) MNDWI 2010, (<b>e</b>) NDWI 2020, and (<b>f</b>) MNDWI 2020.</p>
Full article ">Figure 6
<p>Net shoreline movement profiles of Chambal River showing the changes in shoreline position over time, as calculated by the digital shoreline analysis system (DSAS) model. Panel (<b>a</b>) presents the profiles for Jhiri sand mining zones, panel (<b>b</b>) presents the profiles for Sewarpali sand mining zones, and panel (<b>c</b>) presents the profiles for Chandilpura sand mining zones.</p>
Full article ">Figure 6 Cont.
<p>Net shoreline movement profiles of Chambal River showing the changes in shoreline position over time, as calculated by the digital shoreline analysis system (DSAS) model. Panel (<b>a</b>) presents the profiles for Jhiri sand mining zones, panel (<b>b</b>) presents the profiles for Sewarpali sand mining zones, and panel (<b>c</b>) presents the profiles for Chandilpura sand mining zones.</p>
Full article ">Figure 7
<p>Endpoint Rate Profiles of Sand Mining Zones of the Chambal River. This figure presents the endpoint rate profiles of the three sand mining zones, namely (<b>a</b>) Jhiri (<b>b</b>) Sewarpali (<b>c</b>) Chandilpura.</p>
Full article ">Figure 7 Cont.
<p>Endpoint Rate Profiles of Sand Mining Zones of the Chambal River. This figure presents the endpoint rate profiles of the three sand mining zones, namely (<b>a</b>) Jhiri (<b>b</b>) Sewarpali (<b>c</b>) Chandilpura.</p>
Full article ">Figure 8
<p>Comparative endpoint Rate of all transects of Sand Mining Zones.</p>
Full article ">Figure 9
<p>Comparative erosion and accretion rates at three study sites. This figure presents the estimated erosion and accretion rates for the three study sites as determined by the DSAS model. These estimates suggest notable variations in the study area’s geomorphic processes and sediment dynamics.</p>
Full article ">Figure 10
<p>Endpoint Rate Profile of Unmined Site (Basai Neem).</p>
Full article ">Figure 11
<p>Comparative Erosion and Accretion Rates at Four Study Sites, including Basai Neem (Unmined Site).</p>
Full article ">
21 pages, 15896 KiB  
Article
Analysis of Multi-Temporal Shoreline Changes Due to a Harbor Using Remote Sensing Data and GIS Techniques
by Sanjana Zoysa, Vindhya Basnayake, Jayanga T. Samarasinghe, Miyuru B. Gunathilake, Komali Kantamaneni, Nitin Muttil, Uttam Pawar and Upaka Rathnayake
Sustainability 2023, 15(9), 7651; https://doi.org/10.3390/su15097651 - 6 May 2023
Cited by 11 | Viewed by 4468
Abstract
Coastal landforms are continuously shaped by natural and human-induced forces, exacerbating the associated coastal hazards and risks. Changes in the shoreline are a critical concern for sustainable coastal zone management. However, a limited amount of research has been carried out on the coastal [...] Read more.
Coastal landforms are continuously shaped by natural and human-induced forces, exacerbating the associated coastal hazards and risks. Changes in the shoreline are a critical concern for sustainable coastal zone management. However, a limited amount of research has been carried out on the coastal belt of Sri Lanka. Thus, this study investigates the spatiotemporal evolution of the shoreline dynamics on the Oluvil coastline in the Ampara district in Sri Lanka for a two-decade period from 1991 to 2021, where the economically significant Oluvil Harbor exists by utilizing remote sensing and geographic information system (GIS) techniques. Shorelines for each year were delineated using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager images. The Normalized Difference Water Index (NDWI) was applied as a spectral value index approach to differentiate land masses from water bodies. Subsequently, the Digital Shoreline Analysis System (DSAS) tool was used to assess shoreline changes, including Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR). The results reveal that the Oluvil coast has undergone both accretion and erosion over the years, primarily due to harbor construction. The highest SCE values were calculated within the Oluvil harbor region, reaching 523.8 m. The highest NSM ranges were recorded as −317.1 to −81.3 m in the Oluvil area and 156.3–317.5 m in the harbor and its closest point in the southern direction. The maximum rate of EPR was observed to range from 3 m/year to 10.7 m/year towards the south of the harbor, and from −10.7 m/year to −3.0 m/year towards the north of the harbor. The results of the LRR analysis revealed that the rates of erosion anomaly range from −3 m/year to −10 m/year towards the north of the harbor, while the beach advances at a rate of 3 m/year to 14.3 m/year towards the south of the harbor. The study area has undergone erosion of 40 ha and accretion of 84.44 ha. These findings can serve as valuable input data for sustainable coastal zone management along the Oluvil coast in Sri Lanka, safeguarding the coastal habitats by mitigating further anthropogenic vulnerabilities. Full article
Show Figures

Figure 1

Figure 1
<p>Location map of the study area—Oluvil Harbor, Sri Lanka.</p>
Full article ">Figure 2
<p>Schematic diagram of methodological procedures.</p>
Full article ">Figure 3
<p>Extracted shorelines from Landsat satellite imageries (1991–2021).</p>
Full article ">Figure 4
<p>Spatial variation of Oluvil coastline trends: (<b>a</b>) For 1991–2000; (<b>b</b>) For 2000–2008; (<b>c</b>) For 2008–2021.</p>
Full article ">Figure 5
<p>Google Earth Pro Images used to study the periodic change of Oluvil Coastline from 2000–2021 (accessed on 30 December 2022).</p>
Full article ">Figure 6
<p>DSAS Statics of the Oluvil coast area: (<b>a</b>) For SCE; (<b>b</b>) For NSM; (<b>c</b>) For EPR; (<b>d</b>) For LRR.</p>
Full article ">Figure 6 Cont.
<p>DSAS Statics of the Oluvil coast area: (<b>a</b>) For SCE; (<b>b</b>) For NSM; (<b>c</b>) For EPR; (<b>d</b>) For LRR.</p>
Full article ">Figure 7
<p>Eroded and accreted areas in the Oluvil coast during 2008–2021 (accessed on 30 December 2022).</p>
Full article ">Figure 8
<p>Eroded and accreted areas in the Oluvil coastline (accessed on 7 January 2023).</p>
Full article ">
22 pages, 11608 KiB  
Article
Assessment and Forecast of Shoreline Change Using Geo-Spatial Techniques in the Gulf of California
by Yedid Guadalupe Zambrano-Medina, Wenseslao Plata-Rocha, Sergio Alberto Monjardin-Armenta and Cuauhtémoc Franco-Ochoa
Land 2023, 12(4), 782; https://doi.org/10.3390/land12040782 - 30 Mar 2023
Cited by 13 | Viewed by 4276
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 [...] Read more.
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. Full article
Show Figures

Figure 1

Figure 1
<p>Study area location. Northwest Mexico, Gulf of California shoreline.</p>
Full article ">Figure 2
<p>Schematic representation of methodology.</p>
Full article ">Figure 3
<p>Example of transects and baseline.</p>
Full article ">Figure 4
<p>Shoreline: (<b>a</b>) Sinaloa, (<b>b</b>) Sonora, (<b>c</b>) Baja California, and (<b>d</b>) Baja California Sur.</p>
Full article ">Figure 5
<p>Shoreline dynamics and evaluation, (<b>a</b>) NSM (m), (<b>b</b>) LRR (m/year), and (<b>c</b>) WLR (m/year) along Sinaloa.</p>
Full article ">Figure 6
<p>Shoreline dynamics and evaluation, (<b>a</b>) NSM (m), (<b>b</b>) LRR (m/year), and (<b>c</b>) WLR (m/year) along Sonora.</p>
Full article ">Figure 7
<p>Shoreline dynamics and evaluation, (<b>a</b>) NSM (m), (<b>b</b>) LRR (m/year), and (<b>c</b>) WLR (m/year) for Baja California.</p>
Full article ">Figure 8
<p>Shoreline dynamics and evaluation, (<b>a</b>) NSM (m), (<b>b</b>) LRR (m/year), and (<b>c</b>) WLR (m/year) for Baja California Sur.</p>
Full article ">Figure 9
<p>Current shoreline (2020) and forecast shorelines for 2030 and 2050, (<b>a</b>) Sinaloa, (<b>b</b>) Sonora, (<b>c</b>) Baja California, and (<b>d</b>) Baja California Sur.</p>
Full article ">Figure 10
<p>LRR change for the original and forecasted 2020 shoreline, (<b>a</b>) Sinaloa, (<b>b</b>) Sonora, (<b>c</b>) Baja California, (<b>d</b>) Baja California Sur, and (<b>e</b>) GC.</p>
Full article ">
14 pages, 5197 KiB  
Article
Assessment of Shoreline Changes for the Selangor Coast, Malaysia, Using the Digital Shoreline Analysis System Technique
by Khairul Nizam Abdul Maulud, Siti Norsakinah Selamat, Fazly Amri Mohd, Noorashikin Md Noor, Wan Shafrina Wan Mohd Jaafar, Mohd Khairul Amri Kamarudin, Effi Helmy Ariffin, Nor Aizam Adnan and Anizawati Ahmad
Urban Sci. 2022, 6(4), 71; https://doi.org/10.3390/urbansci6040071 - 12 Oct 2022
Cited by 16 | Viewed by 3927
Abstract
Coastal areas are fragile and changeable due to natural and anthropogenic factors. The resulting changes could have a significant impact on the coastal community. Thus, monitoring shoreline changes for environmental protection in the Selangor coastal area is an important task to address these [...] Read more.
Coastal areas are fragile and changeable due to natural and anthropogenic factors. The resulting changes could have a significant impact on the coastal community. Thus, monitoring shoreline changes for environmental protection in the Selangor coastal area is an important task to address these issues. The main objective of this study is to analyse the pattern of shoreline changes and predict the shoreline position along the Selangor coast. The geospatial approach can provide information on the history and pattern of shoreline changes. This study used temporal datasets and satellite imagery (SPOT 5) to monitor the shoreline changes throughout the 11 identified study areas. It comprises three methods: shoreline change envelope (SCE), net shoreline movement (NSM), and end-point rate (EPR). The findings indicated that the Selangor coast was more exposed to the erosion phenomenon than to the accretion phenomenon, with 77.3% and 22.7%, respectively. This study reveals significant erosion phenomena in 2 out of 11 areas: Bagan Pasir and Pantai Kelanang. Meanwhile, significant accretion occurred at Bagan Sungai Burong and Sungai Nibong. Consequently, providing complete information would be helpful for researchers, decision-makers, and those in charge of planning and managing the coastal zone. Full article
Show Figures

Figure 1

Figure 1
<p>Photo showing SAUH installed at Sungai Tegar.</p>
Full article ">Figure 2
<p>Photo showing geotube installed at Pantai Kelanang in 2019.</p>
Full article ">Figure 3
<p>Study areas along coast of Selangor represented by the red line.</p>
Full article ">Figure 4
<p>The variation of the erosion and accretion phenomena along the study area.</p>
Full article ">Figure 5
<p>Photo showing the accumulation of garbage as the high tide occurred at Sungai Pulai (2017). Red line indicating erosion along the shoreline.</p>
Full article ">Figure 6
<p>Shoreline change along the study area from (<b>a</b>) 1993–2004 and (<b>b</b>) 2004–2014.</p>
Full article ">Figure 7
<p>Prediction of shoreline changes for the years 2030 and 2040.</p>
Full article ">Figure 8
<p>Predicted shoreline changes in 2030 (orange line) and 2040 (red line) based on 2014 shoreline (green line).</p>
Full article ">Figure 9
<p>Land loss impacts on erosion phenomena in Bagan Pasir.</p>
Full article ">Figure 10
<p>Facilities damage impacts on shoreline changes in Pantai Kelanang.</p>
Full article ">
22 pages, 6995 KiB  
Article
Estimating Quantitative Morphometric Parameters and Spatiotemporal Evolution of the Prokopos Lagoon Using Remote Sensing Techniques
by Dionysios N. Apostolopoulos, Pavlos Avramidis and Konstantinos G. Nikolakopoulos
J. Mar. Sci. Eng. 2022, 10(7), 931; https://doi.org/10.3390/jmse10070931 - 6 Jul 2022
Cited by 18 | Viewed by 2626
Abstract
The Prokopos Lagoon is part of the Kotychi Strofilias National Wetlands Park, which is supervised by the Ministry of Environment, Energy and Climate Change of Greece. The lagoon is situated at the northwestern coast of the Peloponnese and is protected by the Ramsar [...] Read more.
The Prokopos Lagoon is part of the Kotychi Strofilias National Wetlands Park, which is supervised by the Ministry of Environment, Energy and Climate Change of Greece. The lagoon is situated at the northwestern coast of the Peloponnese and is protected by the Ramsar Convention. It is an important ecosystem with ecological services providing habitats for many plants and animals and essential goods and services for humans as well. No previous relevant studies for the wider wetland area are available, and given that lagoons are important ecosystems, their diachronic evolution should be under constant monitoring. Using remote sensing techniques in Geographic Information System (GIS) environment, alterations in critical parameters could be measured and applied for the protection of the area. The present study examines the spatiotemporal changes of the water extent of the Prokopos Lagoon, estimating landscape metrics and several morphometric parameters and indices related to the geomorphological features of the lagoon for the 1945–2021 period. Moreover, the adjacent shoreline was studied for each past decade evolution from 1945 to present, and it is discussed to whether there is a relationship between shoreline changes and the lagoon. High resolution satellite images and air photos at scale 1:30,000 were used to digitize the shorelines and the polygons of the lagoon’s surface. Linear Regression Rates (LRR), Net Shoreline Movement (NSM), End Point Rate (EPR) and Shoreline Change Envelope (SCE) provided by the Digital Shoreline Analysis System (DSAS) were used to determine the changes. Finally, future shoreline positions for 2021 and 2031 are estimated, while based on statistic models, we found that in the coastal area, the erosion–accretion cycle is predicted to be completed in 2031, after almost 86 years since 1945. Full article
(This article belongs to the Special Issue Changes of the Coastal Zones Due to Climate Change)
Show Figures

Figure 1

Figure 1
<p>Coastal lagoons sub-divided into (<b>a</b>) choked, (<b>b</b>) restricted and (<b>c</b>) leaky (remodified after [<a href="#B3-jmse-10-00931" class="html-bibr">3</a>]).</p>
Full article ">Figure 2
<p>The major coastal lagoonal ecosystems of western Peloponnese and the location of the study area (Prokopos Lagoon).</p>
Full article ">Figure 3
<p>Prokopos Lagoon morphometric parameters interpretation.</p>
Full article ">Figure 4
<p>Diachronic shoreline evolution of the Prokopos Lagoon. Basemap of 2021.</p>
Full article ">Figure 5
<p>Lagoon restriction ratio index fluctuations during the study period.</p>
Full article ">Figure 6
<p>Lagoon’s orientation parameter fluctuations during the study period.</p>
Full article ">Figure 7
<p>Lagoon’s shoreline development index fluctuations during the study period.</p>
Full article ">Figure 8
<p>Water surface area fluctuations during the study period.</p>
Full article ">Figure 9
<p>Lagoon’s perimeter fluctuations during the study period.</p>
Full article ">Figure 10
<p>Ratio of change in lagoon area and perimeter during the study period.</p>
Full article ">Figure 11
<p>Multitemporal fluctuations in the Prokopos Lagoon. The gridded polygon represents the oldest surface, which is compared to the newer shoreline (blue line) at each time interval. Basemap of 2021.</p>
Full article ">Figure 12
<p>Diachronic shoreline changes using the EPR (m/yr) rates. Basemap of 2021.</p>
Full article ">Figure 13
<p>Erosion and accretion rates along the Prokopos Lagoon sea zone.</p>
Full article ">Figure 14
<p>Correlation of EPR and LRR change rates for all transects.</p>
Full article ">Figure 15
<p>NSM rates showing erosion (red color) and accretion (green color) in conjunction with the SCE rates (black line) for the 1945–2021 period.</p>
Full article ">Figure 16
<p>Forecasted EPR for the periods 2021–2031 and 2021–2041 vs. LRR (1945–2021). The prediction interval has been calculated at ±0.26 m/yr.</p>
Full article ">
27 pages, 34719 KiB  
Article
The Stability and Suitability of the Bhasan Char Island as an Accommodation for the Forcibly Displaced Myanmar Nationals (FDMN)
by Md. Yousuf Gazi, A. S. M. Maksud Kamal, Md. Nazim Uddin, Md. Anwar Hossain Bhuiyan and Md. Zillur Rahman
Sustainability 2022, 14(2), 747; https://doi.org/10.3390/su14020747 - 11 Jan 2022
Cited by 9 | Viewed by 4656
Abstract
Assessing the dynamics of Bhasan Char is very crucial, as the Government of Bangladesh (GoB) has recently selected the island as the accommodation of the FDMN. This article critically evaluates the spatiotemporal morphological variations due to erosion, accretion, and subsurface deformation of the [...] Read more.
Assessing the dynamics of Bhasan Char is very crucial, as the Government of Bangladesh (GoB) has recently selected the island as the accommodation of the FDMN. This article critically evaluates the spatiotemporal morphological variations due to erosion, accretion, and subsurface deformation of the island through multi-temporal geospatial and geophysical data analysis, groundwater quality-quantity, and also determines the nature and rate of changes from 2003 to 2020. This is the first study in this island on which multi-temporal Landsat Satellite Imagery and seismic data have been used with geospatial techniques with Digital Shoreline Analysis System (DSAS) and petrel platform, respectively. The analysis of satellite images suggests that the island first appeared in 2003 in the Bay of Bengal, then progressively evolved to the present stable condition. Significant changes have taken place in the morphological and geographical conditions of the island since its inception. Since 2012, the island has been constantly accreted by insignificant erosion. It receives tidally influenced fluvial sediments from the Ganges-Brahmaputra-Meghna (GBM) river system and the sedimentary accretion, in this case, is higher than the erosion due to relatively weaker wave action and longshore currents. It has gained approximately 68 km2 area, mostly in the northern part and because of erosion in the south. Although the migration of the Bhasan Char was ubiquitous during 2003–2012, it has been concentrated in a small area to the east since 2018. The net shoreline movements (NSM) suggest that the length of the shoreline enlarged significantly by around 39 km in 2020 from its first appearance. Seismic and GPS data clearly indicate that the island is located on the crest of a slowly uplifting low-amplitude anticline, which may result in a stable landform around the island. Based on the analysis of historical data, it has been assessed that the current configuration of Bhasan Char would not be severely affected by 10–15-foot-high cyclone. Therefore, FDMN rehabilitation here might be safer that would be a good example for future geo-environmental assessment for any areas around the world for rehabilitation of human in remote and vulnerable island. The findings of this research will facilitate the government’s decision to rehabilitate FDMN refugees to the island and also contribute to future research in this area. Full article
Show Figures

Figure 1

Figure 1
<p>Location map of the study area (Bhasan Char, Bay of Bengal, Bangladesh).</p>
Full article ">Figure 2
<p>Tidal height with low and high during image acquisition date from 2003 to 2020 (Tidal station: Charchanga, Bangladesh).</p>
Full article ">Figure 3
<p>Methodology workflow process used in this study.</p>
Full article ">Figure 4
<p>Blue lines showing seismic data used in this study.</p>
Full article ">Figure 5
<p>Morphological variations of the Bhasan Char Island from 2003–2020.</p>
Full article ">Figure 6
<p>Trend of areal change of the island (2003–2020).</p>
Full article ">Figure 7
<p>Erosion-Accretion Trend on the island throughout the study timeframe (2003 to 2020).</p>
Full article ">Figure 8
<p>Erosion-Accretion scenarios of the Bhasan Char Island (2003–2020).</p>
Full article ">Figure 9
<p>Spatiotemporal migration of island. (<b>a</b>) Boundary shifting and (<b>b</b>) Depocenter (Lob Shifting) of the Bhasan Char Island.</p>
Full article ">Figure 10
<p>Showing four segments of the curvilinear shorelines to ease shoreline shifting calculation. (<b>A</b>: Northwestern segment, <b>B</b>: Northeastern Segment, <b>C</b>: Southwestern Segment, <b>D</b>: Southeastern Segment).</p>
Full article ">Figure 11
<p>Net Shoreline Movement (NSM) of the Bhasan Char Island (A, B, C, and D segments).</p>
Full article ">Figure 12
<p>End Point Rate (EPR) of the Bhasan Char Island (A, B, C, and D segments).</p>
Full article ">Figure 13
<p>NSM and EPR along the shoreline (shoreline shifting trend).</p>
Full article ">Figure 14
<p>2D seismic section (<b>LINE: CEB-97-415W</b>) running from SSW to NNEacross the Bhasan Char Island. This a dip line shows the subsurface structural arrangement.</p>
Full article ">Figure 15
<p>2D seismic section (<b>LINE: CEB-97-486</b>) running from SSE to NNW across the Bhasan Char Island. The strike line shows the subsurface structural arrangement.</p>
Full article ">Figure 16
<p>Time structure map produced approximately at upper Pliocene time. This map shows an NW-SE trending of gentle anticline. Bhasan Char Island is situated at the crest of the gentle anticline.</p>
Full article ">Figure 17
<p>Water depth in and around the study area ranges from only two to four meters.</p>
Full article ">Figure 18
<p>Intensity of siltation and shaded relief map of Bhasan Char area.</p>
Full article ">Figure 19
<p>Storm surge height from 1960 to 2020 and the height of the dams constructed around the Bhasan Char Island.</p>
Full article ">Figure 20
<p>Showing the historical cyclone tracks from 1960 to recent. The red circle outlines the location of the study area.</p>
Full article ">Figure 21
<p>(<b>a</b>) There are three layers of protective measures to save the island from tidal waves and storm surges. This figure shows the outermost layer of engineering measure (vertical and horizontal steel pipes) along the western side of the Bhasan Char to reduce the energy of the tidal waves and strong surges. Behind is the second layer of protection that is made of gravel and geobags. Behind the mangrove forest, there is an earth embankment (third layer of protection) around the Ashrayan-3 Project implemented for the relocation of the FDMN displaced people. (<b>b</b>) The earth embankment (third layer of protection) is constructed around the Ashrayan-3 Project Area (1702 Acres). The length of the embankment is 12.1 km, and the height is nine feet (to be raised to 19 feet in the future). The slope ratio of the embankment is 1:5. The width of the top surface of the embankment will be 25 feet. (<b>c</b>) Cluster houses (red) with cyclone shelter and sweet water pond. (<b>d</b>) Cluster houses with cyclone shelters for the FDMN displaced people to be relocated in Bhasan Char.</p>
Full article ">
8 pages, 2578 KiB  
Proceeding Paper
A Comparison of Landsat-8 OLI, Sentinel-2 MSI and PlanetScope Satellite Imagery for Assessing Coastline Change in El-Alamein, Egypt
by Kamal Darwish and Scot Smith
Eng. Proc. 2021, 10(1), 23; https://doi.org/10.3390/ecsa-8-11258 - 1 Nov 2021
Cited by 9 | Viewed by 1999
Abstract
The objective of this study was to provide an assessment of coastline extraction and change analysis using different sensors from three satellites over time. Imagery from Landsat-8 OLI, Sentinel-2A MSI, and PlanetScope-3B were used to detect geomorphological changes along the El-Alamein coastline on [...] Read more.
The objective of this study was to provide an assessment of coastline extraction and change analysis using different sensors from three satellites over time. Imagery from Landsat-8 OLI, Sentinel-2A MSI, and PlanetScope-3B were used to detect geomorphological changes along the El-Alamein coastline on the Mediterranean Sea between August 2016 and August 2021. The normalized difference water index (NDWI) was applied to automate, detect and map water bodies based on thresholding techniques and coastline extraction. The extracted coastlines were analyzed using geographic information systems (GIS)-based digital shoreline analysis system (DSAS.v5) model, a GIS software tool for the estimation of shoreline change rates calculated through two statistical techniques: net shoreline movement (NSM) and end point rate (EPR). The results indicate that measuring coastline morphological change using satellite-based imagery depends very much on the resolution of the imagery. It is necessary to tailor the selection of imagery to the accuracy of the measurement needed. Higher resolution imagery such at PlanetScope (3 m) produces higher resolution measurements. However, medium resolution imagery from Landsat may be sufficiently good for objectives requiring less spatial resolution. Full article
Show Figures

Figure 1

Figure 1
<p>Study area. (<b>a</b>) Study area (image from PlanetScope taken in 2021). (<b>b</b>) Location of study area in northern Egypt.</p>
Full article ">Figure 2
<p>Coastline feature extraction. Sub-figure (<b>a</b>) Landsat-8 MNDWI image August 2021; sub-figure (<b>b</b>) Sentinel-2A NDWI image August 2021; sub-figure (<b>c</b>) PlanetScope NDWI image August 2021; and sub-figure (<b>d</b>) spatial displacement between coastlines at the same time, August 2021.</p>
Full article ">Figure 3
<p>Coastline change analysis using DSAS.</p>
Full article ">Figure 4
<p>Comparing coastline change rates using different satellite data between 2016 and 2021.</p>
Full article ">Figure 5
<p>Correlation and linear relationship between different coastline rates. Sub-figure (<b>a</b>) Correlation between Landsat 8 and Sentinel-2 shoreline change rates, sub-figure (<b>b</b>) the correlation between PlanetScope and Sentinel-2 shoreline change, and sub-figure (<b>c</b>) the correlation between Landsat 8 and PlanetScope for shoreline change.</p>
Full article ">
11552 KiB  
Article
Spatio-Temporal Change Detection of Ningbo Coastline Using Landsat Time-Series Images during 1976–2015
by Xia Wang, Yaolin Liu, Feng Ling, Yanfang Liu and Feiguo Fang
ISPRS Int. J. Geo-Inf. 2017, 6(3), 68; https://doi.org/10.3390/ijgi6030068 - 2 Mar 2017
Cited by 70 | Viewed by 8588
Abstract
Ningbo City in Zhejiang Province is one of the largest port cities in China and has achieved high economic development during the past decades. The port construction, land reclamation, urban development and silt deposition in the Ningbo coastal zone have resulted in extensive [...] Read more.
Ningbo City in Zhejiang Province is one of the largest port cities in China and has achieved high economic development during the past decades. The port construction, land reclamation, urban development and silt deposition in the Ningbo coastal zone have resulted in extensive coastline change. In this study, the spatio-temporal change of the Ningbo coastlines during 1976–2015 was detected and analysed using Landsat time-series images from different sensors, including Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI). Fourteen individual scenes (covering seven phases) of cloud-free Landsat images within the required tidal range of ±63 cm were collected. The ZiYuan-3 (ZY-3) image of 2015 was used to extract the reference coastline for the accuracy assessment. The normalised difference water index (NDWI) and the modified normalized difference water index (MNDWI) were applied to discriminate surface water and land features, respectively. The on-screen digitising approach was then used to further refine the extracted time-series coastlines in the period from 1976 to 2015. Six relevant indices, length, length change, annual length change, fractal dimension (FD), average net shoreline movement (NSM) and average annual NSM, were calculated to analyse and explore the spatio-temporal change features of Ningbo coastlines. Results show that the length of the Ningbo coastlines increased from 910 km to 986 km, and the value of FD increased from 1.09 to 1.12, and the coastline morphology changed from sinuous to straight. The average NSM increased from 187 m to 298 m and the average annual NSM reached 85 m/year, indicating the advance of coastlines towards the sea at a high level. The spatio-temporal change patterns also varied in different areas. In Hangzhou Bay, significant advancement along the coastlines was experienced since 2001 mainly because of urban construction and land reclamation. In Xiangshan Bay, the forces of nature played a major role in coastline dynamics before 2008, whilst port construction, urban construction and island link projections moved the coastlines towards the sea. The coastline changes of Sanmen Bay were affected by the interaction of nature and human activities. All these observations indicate that forces of nature and human activities were the two important influential factors for the observed coastline change. In this case, the coastline complexity variation was considered responsible for various coastline patterns change of the Ningbo coast. In addition, erosion and accretion occurred in turn because of forces of nature and human activities, such as urban development and agricultural exploitation. Full article
(This article belongs to the Special Issue Earth/Community Observations for Climate Change Research)
Show Figures

Figure 1

Figure 1
<p>Geographic location of the study area and the Landsat OLI image of Ningbo as a false colour composite image (R: band 7, G: band 5, B: band 3).</p>
Full article ">Figure 2
<p>Flowchart of spatio-temporal change detection of Ningbo coastline during 1976–2015.</p>
Full article ">Figure 3
<p>Process of coastline extraction in Hangzhou Bay (the first column is the false colour composite image (R: band 7, G: band 5, B: band 3).</p>
Full article ">Figure 4
<p>Coastline extracting errors between the reference coastline produced from the ZY-3 multispectral image (<b>yellow</b>) and the coastline produced from the Landsat OLI image (<b>red</b>) in 2015.</p>
Full article ">Figure 5
<p>Coastlines of Ningbo since 1976 to 2015.</p>
Full article ">Figure 6
<p>Changes of coastline length and FD values during 1976–2015.</p>
Full article ">Figure 7
<p>Illustrations of time-series coastlines of Hangzhou Bay from 1976 to 2015: (<b>a</b>) multi-temporal positions of coastlines; (<b>b</b>) changes of coastline length and fractal dimension; and (<b>c</b>) displacements along the coastlines.</p>
Full article ">Figure 8
<p>Illustrations of time-series coastlines of Xiangshan Bay from 1976 to 2015: (<b>a</b>) multi-temporal positions of coastlines and land exploitation projection; (<b>b</b>) changes of coastline length and fractal dimension; and (<b>c</b>) displacements along the coastlines.</p>
Full article ">Figure 9
<p>Illustrations of Sanmen Bay coastline from 1976 to 2015: (<b>a</b>) multi-temporal positions of coastlines; (<b>b</b>) changes of coastline length and fractal dimension; and (<b>c</b>) displacements along the coastlines.</p>
Full article ">
Back to TopTop