55-year (1960–2015) spatiotemporal shoreline change analysis using historical DISP and Landsat time series data in Shanghai

Qiao, Gang; Mi, Huan; Wang, Weian; Tong, Xiaohua; Li, Zhongbin; Li, Tan; Liu, Shijie; Hong, Yang

doi:10.1016/j.jag.2018.02.009

Cited by 56 publications

(35 citation statements)

References 33 publications

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“…Li et al [24] determined the rate of change of the coasts at the center of the mouth of the river Yangtze from 1987 to 2010 using remote sensing images from different times. Qiao et al [25] identified changes in the coastline in Shanghai Lake over the past 55 years from 1960 to 2015, using historical declassified intelligence satellite photography and Landsat time-series data at five-year intervals. Behling et al [26] used images from a 30-year period (1984-2014) for monitoring changes in the coastline of two Namibian coastal lagoons.…”

Section: Satellite Imagesmentioning

confidence: 99%

Monitoring Coastline Dynamics of Alakol Lake in Kazakhstan Using Remote Sensing Data

et al. 2019

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Alakol Lake is one of the largest hydrologically closed lake located in Balkash-Alakol River Basin in southeast Kazakhstan. Having a coastline approximately at 490 km, Alakol Lake has faced multiple threats due to both natural and anthropogenic factors as a result of tectonic movements, geology, wind-wave conditions, growing tourism activities, fishing, and transport, etc. The present study aims to investigate the historical trends in coastline changes along Alakol Lake in Kazakhstan and estimate its change rate by using remote sensing data in particular scale-space images Landsat-5 TM, 7 ETM+, 8 OLI, and Sentinel-2A. Based on Landsat and Sentinel data, the modified normalized difference water index was calculated to demonstrate the coastline changes along Alakol Lake between 1990 and 2018. Moreover, the monitoring and analysis of coastline dynamics is based on the main morphometric characteristics of Alakol Lake including water surface area, coastline length, geomorphology of the coast, etc. Our results reveal that there is a continuous coastline retreat, depending on the coast types. For example, in the case of the denudation coasts, a land inundation was from 120 to 270 m between 1990 and 2018. In the case of the accumulative coast (mainly northeast, north, and northwest coasts) a land inundation was from 200 to 900 m. A vast area of agricultural land around Alakol Lake become flooded and lost. This study demonstrates the importance of monitoring coastline dynamics because it provides essential information for understanding the coastal response to contemporary nature and anthropogenic impacts.

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Section: Satellite Imagesmentioning

confidence: 99%

Monitoring Coastline Dynamics of Alakol Lake in Kazakhstan Using Remote Sensing Data

et al. 2019

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“…Human-induced modifications of river flow might tremendously alter sediment transportation and affect the development of delta regions. In this context, the evolution of coastlines was analyzed in ∼3% of all reviewed studies at regional and subbasin scale including the Mississippi [144], Nile [145,146], Yangtze [147][148][149], and Yellow river basin [150][151][152]. Among these, Amer et al [144] studied the Mississippi river delta which is facing a land loss due to various drivers such as sea level rise or wave erosion.…”

Section: Biosphere: Coastlinementioning

confidence: 99%

“…Moreover, Hereher [146] analyzed the evolution of the Nile river delta using Landsat images between 1973 and 2008 to find that coastal area experienced severe erosion affecting approximately 12 km 2 . In addition, Qiao et al [149] focused on the Yangtze river delta and quantified coastline dynamics for a period of 55 years. To this aim, they classified the shoreline in 5 year intervals using historical aerial and Landsat imagery.…”

Section: Biosphere: Coastlinementioning

confidence: 99%

A Review of Earth Observation-Based Analyses for Major River Basins

Uereyen

Kuenzer

2019

Remote Sensing

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Regardless of political boundaries, river basins are a functional unit of the Earth’s land surface and provide an abundance of resources for the environment and humans. They supply livelihoods supported by the typical characteristics of large river basins, such as the provision of freshwater, irrigation water, and transport opportunities. At the same time, they are impacted i.e., by human-induced environmental changes, boundary conflicts, and upstream–downstream inequalities. In the framework of water resource management, monitoring of river basins is therefore of high importance, in particular for researchers, stake-holders and decision-makers. However, land surface and surface water properties of many major river basins remain largely unmonitored at basin scale. Several inventories exist, yet consistent spatial databases describing the status of major river basins at global scale are lacking. Here, Earth observation (EO) is a potential source of spatial information providing large-scale data on the status of land surface properties. This review provides a comprehensive overview of existing research articles analyzing major river basins primarily using EO. Furthermore, this review proposes to exploit EO data together with relevant open global-scale geodata to establish a database and to enable consistent spatial analyses and evaluate past and current states of major river basins.

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“…Coastal areas are exposed to erosion due to both human activities and natural phenomena [2, 3]. Coastal erosion appears as changes in the coastline; therefore, the coastline monitoring has a great significance for identifying the factors causing the coastal erosion [4, 5]. Coastline detection plays an important role in automatic navigation, coastline erosion monitoring, coastal area mapping, warnings of future disasters etc.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical coastline detection in SAR images based on spectral‐textural features and global–local information

Modava

Akbarizadeh

Soroosh

2019

IET Radar, Sonar & Navigation

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This study presents a novel approach to detect the coastline from single-polarisation synthetic aperture radar (SAR) images. The proposed method encompasses land/sea segmentation, coastline detection, and refinement. A novel spectraltextural segmentation framework (STSF) is proposed by using the spectral-textural features extracted from the input image patches. The STSF distinguishes various coastal/sea types and is robust to noise. Also, a hierarchical region-based level set method (LSM) is proposed to detect the coastline, accurately. The first LSM step applies global information for evolution. The LSM initialisation is performed using the obtained rough segmentation, which is very practical as the final LSM evolution depends on the initial value, particularly on complex SAR images. The global region-based LSM (GRB-LSM) step modifies the previous segmentation and approaches to the coastline. To improve accuracy, a local region-based LSM (LRB-LSM) is proposed. Therefore, in the second LSM step, the LRB-LSM applies to the results of GRB-LSM step. The LRB-LSM improves the accuracy of the detected coastline while ensuring its smoothness. To verify the performance of the proposed method, several high-resolution SAR images from different microwave bands and various coastal environments are used. The performance of the proposed method is confirmed by the given experiments.

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55-year (1960–2015) spatiotemporal shoreline change analysis using historical DISP and Landsat time series data in Shanghai

Cited by 56 publications

References 33 publications

Monitoring Coastline Dynamics of Alakol Lake in Kazakhstan Using Remote Sensing Data

Monitoring Coastline Dynamics of Alakol Lake in Kazakhstan Using Remote Sensing Data

A Review of Earth Observation-Based Analyses for Major River Basins

Hierarchical coastline detection in SAR images based on spectral‐textural features and global–local information

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