Shoreline Detection using Optical Remote Sensing: A Review

Toure, Seynabou; Diop, Oumar; Kpalma, Kidiyo; Maiga, Amadou Seidou

doi:10.3390/ijgi8020075

Cited by 139 publications

(86 citation statements)

References 85 publications

(102 reference statements)

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“…The availability of multispectral satellite images at very high resolution (VHR) allows, in fact, acquisition in a short time and simultaneously of long stretches of coast. The geometric accuracies of submetric to decimetric order are absolutely compatible with the specific application and the availability of different bands allows semi-automatic or automatic approaches [15][16][17] such as those proposed in this paper.…”

mentioning

confidence: 77%

Automatic Shoreline Detection from Eight-Band VHR Satellite Imagery

Alicandro

Baiocchi

Brigante

et al. 2019

JMSE

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Coastal erosion, which is naturally present in many areas of the world, can be significantly increased by factors such as the reduced transport of sediments as a result of hydraulic works carried out to minimize flooding. Erosion has a significant impact on both marine ecosystems and human activities; for this reason, several international projects have been developed to study monitoring techniques and propose operational methodologies. The increasing number of available high-resolution satellite platforms (i.e., Copernicus Sentinel) and algorithms to treat them allows the study of original approaches for the monitoring of the land in general and for the study of the coastline in particular. The present project aims to define a methodology for identifying the instantaneous shoreline, through images acquired from the WorldView 2 satellite, on eight spectral bands, with a geometric resolution of 0.5 m for the panchromatic image and 1.8 m for the multispectral one. A pixel-based classification methodology is used to identify the various types of land cover and to make combinations between the eight available bands. The experiments were carried out on a coastal area with contrasting morphologies. The eight bands in which the images are taken produce good results both in the classification process and in the combination of the bands, through the algorithms of normalized difference vegetation index (NDVI), normalized difference water index (NDWI), spectral angle mapper (SAM), and matched filtering (MF), with regard to the identification of the various soil coverings and, in particular, the separation line between dry and wet sand. In addition, the real applicability of an algorithm that extracts bathymetry in shallow water using the "coastal blue" band was tested. These data refer to the instantaneous shoreline and could be corrected in the future with morphological and tidal data of the coastal areas under study. Author Contributions: Supervision, M.A., V.B., R.B. and F.R.; Methodology, M.A., V.B., R.B. and F.R.; Investigation, M.A., V.B., R.B. and F.R.; Writing-Review and editing, V.B., R.B. and F.R. Funding: This research received no external funding. Conflicts of Interest:The authors declare no conflict of interest.

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mentioning

confidence: 77%

Automatic Shoreline Detection from Eight-Band VHR Satellite Imagery

Alicandro

Baiocchi

Brigante

et al. 2019

JMSE

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“…Mapping and detection of coastline changes from satellite images have become increasingly important over recent decades, especially because satellites capture and provide data in visible and infrared spectral bands where the land and water can be easily distinguished [30][31][32]. These make optical satellite images containing visible and infrared bands of the electromagnetic spectrum widely used for coastline mapping especially when these images are easily obtainable [33].…”

Section: Lake Sevan As a Case Study: A Problem Statementmentioning

confidence: 99%

Paving the Way towards an Armenian Data Cube

Asmaryan

Muradyan

Tepanosyan

et al. 2019

Data

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Environmental issues become an increasing global concern because of the continuous pressure on natural resources. Earth observations (EO), which include both satellite/UAV and in-situ data, can provide robust monitoring for various environmental concerns. The realization of the full information potential of EO data requires innovative tools to minimize the time and scientific knowledge needed to access, prepare and analyze a large volume of data. EO Data Cube (DC) is a new paradigm aiming to realize it. The article presents the Swiss-Armenian joint initiative on the deployment of an Armenian DC, which is anchored on the best practices of the Swiss model. The Armenian DC is a complete and up-to-date archive of EO data (e.g., Landsat 5, 7, 8, Sentinel-2) by benefiting from Switzerland’s expertise in implementing the Swiss DC. The use-case of confirm delineation of Lake Sevan using McFeeters band ratio algorithm is discussed. The validation shows that the results are sufficiently reliable. The transfer of the necessary knowledge from Switzerland to Armenia for developing and implementing the first version of an Armenian DC should be considered as a first step of a permanent collaboration for paving the way towards continuous remote environmental monitoring in Armenia.

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“…Toure et al [37] reviewed various approaches used for coastline delineation from remote sensing imagery using various image processing methods. These methods to determine the coastal changes include pixel frequency count, image histogram thresholding [38], edge filter algorithm and tasseled cap transformation [18], and image classification through iterative self-organizing data analysis (ISODATA) [39].…”

Section: Coastline Delineationmentioning

confidence: 99%

Three Decades of Coastal Changes in Sindh, Pakistan (1989–2018): A Geospatial Assessment

et al. 2019

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Coastal erosion endangers millions living near-shore and puts coastal infrastructure at risk, particularly in low-lying deltaic coasts of developing nations. This study focuses on morphological changes along the~320-km-long Sindh coastline of Pakistan over past three decades. In this study, the Landsat images from 1989 to 2018 at an interval of 10 years are used to analyze the state of coastline erosion. For this purpose, well-known statistical approaches such as end point rate (EPR), least median of squares (LMS), and linear regression rate (LRR) are used to calculate the rates of coastline change. We analyze the erosion trend along with the underlying controlling variables of coastal change. Results show that most areas along the coastline have experienced noteworthy erosion during the study period. It is found that Karachi coastline experienced 2.43 ± 0.45 m/yr of erosion and 8.34 ± 0.45 m/yr of accretion, while erosion on the western and eastern sides of Indus River reached 12.5 ± 0.55 and 19.96 ± 0.65 m/yr on average, respectively. Coastal erosion is widespread along the entire coastline. However, the rate of erosion varies across the study area with a general trend of erosion increasing from west to east in the Indus Delta region (IDR), and the highest average erosion rate is 27.46 m/yr. The interdecadal change during 1989-periods depicted an increasing linear trend (R 2 = 0.78) from Karachi to Indus River (IR) East zone. The spatial trend from west to east is positively correlated with mean sea level rise, which has increased from 1.1 to 1.9 mm/year, and negatively correlated with topographic slope, which is found to be decreasing eastward along the coastline. The findings necessitate appropriate actions and have important implications to better manage coastal areas in Pakistan in the wake of global climate change.

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Shoreline Detection using Optical Remote Sensing: A Review

Cited by 139 publications

References 85 publications

Automatic Shoreline Detection from Eight-Band VHR Satellite Imagery

Automatic Shoreline Detection from Eight-Band VHR Satellite Imagery

Paving the Way towards an Armenian Data Cube

Three Decades of Coastal Changes in Sindh, Pakistan (1989–2018): A Geospatial Assessment

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