Shoreline Change Detection and Forecast along the Yancheng Coast Using a Digital Shoreline Analysis System

Yan, Dandan; Yao, Xiuying; Li, Jingtai; Qi, Liping; Zhang, Luan

doi:10.1007/s13157-021-01444-3

Cited by 22 publications

(9 citation statements)

References 33 publications

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“…The shape factor balances color homogeneity, and the sum of the two parameters is 1. The shape factor is composed of smoothness and compactness, and the sum of their weights is 1 [26], [43]. Because these segmentation parameters play an important role in classification accuracy, it is necessary to determine these parameters.…”

Section: ) Multiscale Optimal Segmentationmentioning

confidence: 99%

“…Furthermore, overall classification accuracy based on pixels is not high for Spartina saltmarshes because the complexity of the wetland landscape and landscape features (e.g., shape and texture features), which are important for improving classification accuracy, cannot be utilized. As an improved technique, OBIA aims to segment the pixels (with the same attributes) into objects for classification according to certain rules [25], [26]. OBIA has been successfully used in many Spartina saltmarsh mappings [9], [27]- [29].…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the Long-Term Expansion and Dieback of Spartina Alterniflora Using Google Earth Engine and Object-Based Hierarchical Random Forest Classification

Yan

Yao

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The overwhelming spread of Spartina alterniflora (smooth cordgrass) over recent decades has put many native plant communities and coastal environments at risk. Therefore, longterm monitoring of S. alterniflora dynamics is necessary to better understand and manage the invasion of the species. However, it is difficult to map Spartina saltmarshes in China on an annual or multiyear epoch basis. To address this issue, we developed a classification approach integrating Google Earth Engine (GEE) and object-based hierarchical random forest (RF) classification, and we applied this approach to quantify the expansion and dieback of S. alterniflora at Dafeng Milu National Nature Reserve, Jiangsu, China during 1993-2020. Results showed that the area of S. alterniflora expanded from 24.48 ha in 1993 to 1,564.96 ha in 2010. However, after ecological hydrological engineering and an increase in Elaphures davidianus (Pè re David's deer) numbers in 2011, the S. alterniflora area decreased significantly to 944.28 ha in 2020. During 2011-2020, the S. alterniflora area decreased substantially at a rate of 67 ha per year and by 86% in one area studied in Dafeng Milu National Nature Reserve. In 2020, the 944.28 ha of S. alterniflora in the reserve was mainly distributed in mudflats by the sea. Overall, these results show that it is feasible to identify S. alterniflora using the GEE platform and object-based hierarchical RF classification; moreover, this approach could improve understanding and management of this invasion species.

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Section: ) Multiscale Optimal Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying the Long-Term Expansion and Dieback of Spartina Alterniflora Using Google Earth Engine and Object-Based Hierarchical Random Forest Classification

Yan

Yao

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…For modeling shoreline change along Bangladesh’s central coast, corrected and generalized multi-temporal shorelines were used in the traditional transect-based analysis of shore-normal movement utilizing an ArcView GIS plugin called Digital Shoreline Analysis System (DSAS) [ 28 ]. Perpendicular to the reference baseline, DSAS generates transects that cross the shoreline at a user-specified interval alongshore [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

“…The concentration of TSM has a significant impact on the biochemical properties of the ocean and has a direct relationship with coastal erosion and accretion [ 39 ]. In wet season TSM concentration was observed the highest (300 mg/L), which influenced by the Meghna River system [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Evaluating the long-term geomorphic process in-relation to hydrodynamics in the central coastal zone of Bangladesh

Sultana

Islam

Rahman

et al. 2023

Heliyon

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“…2032 and 2042 in this case). Analogous studies have been completed elsewhere for coastal management purposes [81][82][83][84][85][86] and for cultural heritage vulnerability assessment [87]. For this study, the forecasting tool was applied only to the LRR derived from VHR shorelines for the last decade.…”

Section: Forecasting Future Changementioning

confidence: 99%

The impact of coastal erosion on the archaeology of the Cyrenaican coast of Eastern Libya

et al. 2023

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Coastal erosion in Cyrenaica (Eastern Libya) represents a major problem for archaeology and heritage management. The area is rich in archaeological sites, often understudied or not fully documented, but also has extensive stretches of vulnerable eroding coastline. This study demonstrates the extent and impact of erosion via shoreline change assessment at two spatial scales. Firstly, wide area assessment using shorelines extracted from a time-series of medium-resolution Landsat imagery. Secondly, site-specific assessment using recent and historic Very High Resolution (VHR) satellite imagery. In both cases, extracted shorelines at different timesteps were compared using the Digital Shoreline Analysis System (DSAS) tool to quantify rates and magnitudes of shoreline movement. The results show extensive zones of erosion at and around the key ancient harbour sites of Apollonia, Ptolemais and Tocra. They also suggest increased rates of coastal retreat in recent years, which is likely linked to anthropogenic actions such as sand mining and urbanization. Forecasts based on present-day shoreline change rates, coupled with ground-level documentation of the vulnerable shorelines is used to identify archaeological features and structures which will likely be progressively damaged or destroyed over the next 20 years. The ability to actively protect archaeological sites is unclear, but there is a clear need for mitigation in the form of enhanced awareness of environmental problems (e.g. caused by sand mining) and more intensive survey/documentation of sites and areas which will be lost in the coming years.

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Shoreline Change Detection and Forecast along the Yancheng Coast Using a Digital Shoreline Analysis System

Cited by 22 publications

References 33 publications

Quantifying the Long-Term Expansion and Dieback of Spartina Alterniflora Using Google Earth Engine and Object-Based Hierarchical Random Forest Classification

Quantifying the Long-Term Expansion and Dieback of Spartina Alterniflora Using Google Earth Engine and Object-Based Hierarchical Random Forest Classification

Evaluating the long-term geomorphic process in-relation to hydrodynamics in the central coastal zone of Bangladesh

The impact of coastal erosion on the archaeology of the Cyrenaican coast of Eastern Libya

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