scholarly journals The Application of GIS and Remote Sensing in a Spatiotemporal Analysis of Coastline Retreat in Rufisque, Senegal

2021 ◽  
Vol 15 (3) ◽  
pp. 55-80
Author(s):  
Cheikh Tidiane Koulibaly ◽  
Johnson O. Ayoade
Keyword(s):  
Coastal Erosion ◽  
Shoreline Change ◽  
Spatiotemporal Analysis ◽  
The United States ◽  
United States Geological Survey ◽  
Landsat Images ◽  
Change Analysis ◽  
Gis And Remote Sensing ◽  
Local Residents ◽  
Analysis System

This paper is aimed at analyzing the phenomenon of shoreline retreat in the locality of Rufisque from 1978 to 2018 mainly using geospatial data and field visits. A set of Landsat images from different dates at 10 year intervals was then acquired through the United States Geological Survey platform and shoreline change analysis was run using the Digital Shoreline Analysis System. In addition to that desktop work, interactions with local residents allowed the determination of ongoing adaptation strategies actually in place to cope with coastal erosion. The study showed that Rufisque is subject to serious rates of erosion reaching −19.48 m/year from 1978–1988, close to −8 m/year from 1988–1998, −5.88 m/year from 1998–2008 and −6.67 m/year from 2008–2018. Beside that coastal erosion, it has been noticed that the coastline also experienced in some of its parts cases of accretion reaching 4.94 m/year for 1988–1998, 7.29 m/year from 1998–2008 and 7.68 m/year during the period 2008–2018. In terms of surfaces, Rufisque’ shoreline respectively lost 156.81 ha (1978–1988), 80.55 ha (1988–1998), 6.94 ha (1998–2008), 12.93 ha (2008–2018) and in the same note gained 2.86 ha (1988–1998), 32.51 ha (1998–2008) and 19.16 ha (2008–2018) attesting to the fact that the coastline is subject to both spatiotemporal changes. Finally, this study also reveals that while authorities’ reaction is taking place at much lower pace, local communities are actually using their ingenuity to put in place strategies to tackle coastal erosion.

Post-Dorian shoreline change at Cape Hatteras National Seashore: 2019 report

2021 ◽  
Author(s):  
Lisa Baron
Keyword(s):  
High Tide ◽  
Monitoring Program ◽  
Shoreline Change ◽  
The United States ◽  
United States Geological Survey ◽  
Vital Signs Monitoring ◽  
Cape Hatteras ◽  
Cape Hatteras National Seashore ◽  
National Seashore

In 2018 and 2019 the Southeast Coast Network (SECN), with assistance from park staff, collected long-term shoreline monitoring data at Cape Hatteras National Seashore as part of the National Park Service (NPS) Vital Signs Monitoring Program. Monitoring was conducted following methods developed by the NPS Northeast Coastal and Barrier Network and consisted of mapping the high-tide swash line using a Global Positioning System unit in the spring of each year (Psuty et al. 2010). Shoreline change was calculated using the Digital Shoreline Analysis System (DSAS) developed by the United States Geological Survey (USGS; Himmelstoss et al. 2018). Following the same field methods used for monitoring long-term shoreline change, geospatial data were collected as part of the Hurricane Dorian (or Dorian) Incident Response from September 12–16, 2019. This report summarizes the post-Dorian data and the previous two shoreline data collection efforts (spring 2019 and fall 2018).

scholarly journals An Approach Based on Landsat Images for Shoreline Monitoring to Support Integrated Coastal Management—A Case Study, Ezbet Elborg, Nile Delta, Egypt

2020 ◽  
Vol 9 (4) ◽  
pp. 199 ◽  
Author(s):  
Mohamed T. Elnabwy ◽  
Emad Elbeltagi ◽  
Mahmoud M. El Banna ◽  
Mohamed M.Y. Elshikh ◽  
Ibrahim Motawa ◽  
...  
Keyword(s):  
Coastal Management ◽  
Nile Delta ◽  
Accuracy Assessment ◽  
Shoreline Change ◽  
Coastal Zones ◽  
Landsat Images ◽  
Change Analysis ◽  
Integrated Coastal Management ◽  
Rtk Gps ◽  
Landsat Satellite

Monitoring the dynamic behavior of shorelines is an essential factor for integrated coastal management (ICM). In this study, satellite-derived shorelines and corresponding eroded and accreted areas of coastal zones have been calculated and assessed for 15 km along the coasts of Ezbet Elborg, Nile Delta, Egypt. A developed approach is designed based on Landsat satellite images combined with GIS to estimate an accurate shoreline changes and study the effect of seawalls on it. Landsat images for the period from 1985 to 2018 are rectified and classified using Supported Vector Machines (SVMs) and then processed using ArcGIS to estimate the effectiveness of the seawall that was constructed in year 2000. Accuracy assessment results show that the SVMs improve images accuracy up to 92.62% and the detected shoreline by the proposed method is highly correlated (0.87) with RTK-GPS measurements. In addition, the shoreline change analysis presents that a dramatic erosion of 2.1 km2 east of Ezbet Elborg seawall has occurred. Also, the total accretion areas are equal to 4.40 km2 and 10.50 km2 in between 1985-and-2000 and 2000-and-2018, respectively, along the southeast side of the study area.

scholarly journals Shoreline Change Analysis of Pontang Cape of Serang Regency of Banten Province

Omni-Akuatika ◽  
2020 ◽  
Vol 16 (2) ◽  
pp. 90
Author(s):  
Abdurrahman Al Farrizi ◽  
Ankiq Taofiqurohman ◽  
Subiyanto Subiyanto
Keyword(s):  
Coastal Zone ◽  
Shoreline Change ◽  
Sand Mining ◽  
Change Analysis ◽  
Factors Affecting ◽  
Shoreline Changes ◽  
Sea Sand ◽  
Digital Shoreline Analysis System ◽  
Analysis System ◽  
Shoreline Change Analysis

Coastal areas, being vulnerable to environmental problems, have one of the most frequent problems which are the change in the shorelines. Shoreline changes, namely abrasions, can cause problems such as land degradations or loss of land in a coastal zone. This problem occurs in many areas, one of which is Pontang Cape. This study aims to determine the distance and rate of shoreline changes that occured in the Cape and its surroundings, as well as explaining the analysis points based on similar studies that had been conducted. This research used ArcMap software and Digital Shoreline Analysis System (DSAS) toolset to determine the distance and rate of shoreline changes for 19 years (1999-2018). Based on the results, there were two shoreline segments where different phenomena of shoreline change took place, namely Banten Bay (accretion) and Pontang Cape-Lontar (abrasion). The most likely causes of changes in the shorelines are sediment runoffs from rivers that lead to bay and sediment transports that affect Banten Bay accretions, while sea sand mining and conversions of mangrove swamps into fishery ponds are factors affecting abrasions in Pontang Cape.Keywords: Abrasion, Accretion, Pontang Cape, Banten Bay, DSAS

Contemporary challenges for Shoreline Change Analysis

2020 ◽  
Author(s):  
Sue Brooks ◽  
Jamie Pollard ◽  
Tom Spencer
Keyword(s):  
Water Level ◽  
Methodological Approach ◽  
Shoreline Change ◽  
Data Availability ◽  
Aerial Photographs ◽  
Publication Date ◽  
Landsat Images ◽  
Change Analysis ◽  
Decadal Scale ◽  
Shoreline Change Analysis

<p>Shoreline change analysis has been deployed across a range of spatio-temporal scales. Accordingly, shoreline change studies have sought to capture shoreline dynamics at a variety of scales, ranging from the local impacts of individual storms to global trends measured over multiple decades. The scale at which we can approach the issue of shoreline change is, to a large extent, determined by the availability of data over time and space. With existing threats from the interactions between accelerated sea level rise, changing storminess and human intervention, shoreline change analysis has never been more relevant or challenging. Historic, centennial-scale shoreline change analysis relies on historic maps where there is normally just a single proxy indicator for consistent shoreline position; the mean water level of ordinary tides on UK Ordnance Survey maps, for example. Occasionally where there are specific coastal landforms that can be mapped, there might be a second proxy such as cliff top position. Shoreline change rates can be determined by extracting these proxies from sequential map surveys, provided the survey dates (ie: not the map publication date) are known.</p><p> </p><p>Shoreline change quantification for more recent decadal-scale periods has been greatly enhanced by increased data availability. This is exemplified by analyses that use widespread coverage available from aerial photographs (past 3 decades). Even more recently on near-annual scales Light Detection and Ranging (LiDAR) data are becoming the norm for capturing storm impacts and shoreline change, enabling volumetric assessments of change in addition to the more traditional linear approaches. LiDAR is enhanced by ground survey Real Time Kinematic (RTK) Instrumentation that can be timed to coincide with storms. As the frequency of dataset capture has increased so has the spatial scale of coverage. Hence the latest shoreline change assessments are global in scale and use Landsat images to focus on hotspots of shoreline change (advance as well as retreat) over the past 30 years. Considering all scales together raises three central questions for shoreline change analysis and these are addressed in this paper.</p><p> </p><p>Firstly, what methodological approach is most suitable for delimiting shorelines and generating the underpinning digitised shorelines for shoreline change assessment?</p><p>Secondly, what lessons can be learnt from using an approach that combines both proxy-based (visually discernible signatures) and datum-based (related to a particular water level) shorelines that change differentially with respect to different process-drivers?</p><p>Thirdly, given the current state-of-the-art around data availability, what is the most appropriate scale to approach shoreline change assessments?</p>

scholarly journals Local Search for Optimal Global Map Generation Using Mid-Decadal Landsat Images

AI Magazine ◽  
2009 ◽  
Vol 30 (2) ◽  
pp. 84 ◽  
Author(s):  
Lina Khatib ◽  
Robert A. Morris ◽  
John Gasch
Keyword(s):  
Local Search ◽  
The United States ◽  
Thematic Mapper ◽  
Sensor Data ◽  
United States Geological Survey ◽  
Landsat Images ◽  
Remote Sensor ◽  
Map Generation ◽  
Landsat 7 ◽  
Generation Problem

NASA and the United States Geological Survey (USGS) are collaborating to produce a global map of the Earth using Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) remote sensor data from the period of 2004 through 2007. The map is comprised of thousands of scene locations and, for each location, there are tens of different images of varying quality to chose from. Constraints and preferences on map quality make it desirable to develop an automated solution to the map generation problem. This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving it using local search. The paper also describes the integration of a GMG solver into a graphical user interface for visualizing and comparing solutions, thus allowing for solutions to be generated with human participation and guidance.

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