Coastline changes in mainland China from 2000 to 2015

Ying, Zhang; Qiao, Qinghua; Jia, Lingyun; Sang, Huiyong; Yang, Dazhi; Zhai, Liang; Li, Ning; Yuan, Xiaohui

doi:10.1080/19479832.2021.1943011

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…We want to point out that with the proposed method we detected only the instantaneous coastline, which is defined as the position of land/sea intersection at one instant in time, specifically the instant of remotely sensed image acquisition [53]. In fact, we could not obtain absolutely accurate coastline data from a remote sensing image: what we obtained is only an approximation [54]. For a correct monitoring, we must consider the dynamic nature of the coastline, which produces a continuous shift over a day due to tidal fluctuations, being especially large for steeply sloped beaches located at macro-tidal areas [55].…”

Section: Study Area and Datasetmentioning

confidence: 93%

A Proposal for Automatic Coastline Extraction from Landsat 8 OLI Images Combining Modified Optimum Index Factor (MOIF) and K-Means

et al. 2023

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The coastal environment is a natural and economic resource of extraordinary value, but it is constantly modifying and susceptible to climate change, human activities and natural hazards. Remote sensing techniques have proved to be excellent for coastal area monitoring, but the main issue is to detect the borderline between water bodies (ocean, sea, lake or river) and land. This research aims to define a rapid and accurate methodological approach, based on the k-means algorithm, to classify the remotely sensed images in an unsupervised way to distinguish water body pixels and detect coastline. Landsat 8 Operational Land Imager (OLI) multispectral satellite images were considered. The proposal requires applying the k-means algorithm only to the most appropriate multispectral bands, rather than using the entire dataset. In fact, by using only suitable bands to detect the differences between water and no-water (vegetation and bare soil), more accurate results were obtained. For this scope, a new index based on the optimum index factor (OIF) was applied to identify the three best-performing bands for the purpose. The direct comparison between the automatically extracted coastline and the manually digitized one was used to evaluate the product accuracy. The results were very satisfactory and the combination involving bands B2 (blue), B5 (near infrared), and B6 (short-wave infrared-1) provided the best performance.

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Section: Study Area and Datasetmentioning

confidence: 93%

A Proposal for Automatic Coastline Extraction from Landsat 8 OLI Images Combining Modified Optimum Index Factor (MOIF) and K-Means

et al. 2023

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“…This clarification is necessary since the coastline has a dynamic nature, its position is variable not only over long periods of time due to erosion and nourishment but also within a day due to tidal phenomena: this last phenomenon can also be very accentuated in coasts presenting low slopes and strong tidal excursions [65].…”

Section: Coastline Extractionmentioning

confidence: 99%

Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach

Parente,

Alcaras,

Figliomeni

2024

Remote Sensing

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In recent decades several methods have been developed to extract coastlines from remotely sensed images. In fact, this is one of the principal fields of remote sensing research that continues to receive attention, as testified by the thousands of scientific articles present in the main databases, such as SCOPUS, WoS, etc. The main issue is to automatize the whole process or at least a great part of it, so as to minimize the human error connected to photointerpretation and identification of training sites to support the classification of objects (basically soil and water) present in the observed scene. This article proposes a new fully automatic methodological approach for coastline extraction: it is based on the unsupervised classification of the most decorrelated fictitious band derived from Principal Component Analysis (PCA) applied to the satellite images. The experiments are carried out on datasets characterized by images with different geometric resolution, i.e., Landsat 9 Operational Land Imager (OLI) multispectral images (pixel size: 30 m), a Sentinel-2 dataset including blue, green, red and Near Infrared (NIR) bands (pixel size: 10 m) and a Sentinel-2 dataset including red edge, narrow NIR and Short-Wave Infrared (SWIR) bands (pixel size: 20 m). The results are very encouraging, given that the comparison between each extracted coastline and the corresponding real one generates, in all cases, residues that present a Root Mean Squared Error (RMSE) lower than the pixel size of the considered dataset. In addition, the PCA results are better than those achieved with Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) applications.

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“…In recent years, the object-oriented method to obtain coastline has been widely used. Most scholars use the optimal global segmentation scale to extract coastlines without types (Wu et al, 2017;Bi et al, 2019;Zhang et al, 2022). For complex coasts, some new methods based on object orientation have been proposed, such as the OBRGIE method, which can delineate aquaculture coastlines (Zhang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Coastline extraction based on multi-scale segmentation and multi-level inheritance classification

et al. 2022

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Detailed management of the coastline is critical to the development of coastal states. However, the current classification of the coastline is relatively weak. This study proposed an automatic method to detect coastlines with category attributes based on multi-scale segmentation and multi-level inheritance classification. Fully integrating the advantages of multi-scale segmentation and multi-level classification, it solved the problems that traditional methods could not solve, such as extracting coastlines with categorical attributes, cultivation ponds that are easily affected by tidal flats, and complex coastal terrain. The Chinese GF-2 satellite images are used to extract various types of coastlines in Jiaozhou Bay and its surrounding areas such as the harbor-wharf coastline, silt coastline, pond coastline, rocky coastline, and sandy coastline. Compared with the human interpretation, it is found that the coastline extracted by our proposed method is different by 10.104 km in the harbor-wharf coastline, 0.099 km in the silt coastline, 2.677 km in the pond coastline, 8.831 km in the rocky coastline, and 0.218 km in the sandy coastline. Furthermore, compared to the object-based region growing integrating edge detection (OBRGIE) method, it is increased by 13.52%, 2.16%, 14.48%, 52.57%, and 22.97%, respectively. The results show that our proposed method is algorithmically more reasonable, accurate, and powerful. It can provide data support for refined coastline management.

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Coastline changes in mainland China from 2000 to 2015

Cited by 7 publications

References 21 publications

A Proposal for Automatic Coastline Extraction from Landsat 8 OLI Images Combining Modified Optimum Index Factor (MOIF) and K-Means

A Proposal for Automatic Coastline Extraction from Landsat 8 OLI Images Combining Modified Optimum Index Factor (MOIF) and K-Means

Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach

Coastline extraction based on multi-scale segmentation and multi-level inheritance classification

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