Waterline Extraction Research Articles

Impacts of El Niño-Southern Oscillation on multi-scale morphodynamics of an embayed beach in southern China

Increased coastal erosion and extreme oceanographic forcing driven by El Niño-Southern Oscillation (ENSO) along the Pacific coast are increasingly receiving concerns. Despite considerable attention, the specific impact of these phenomena on Chinese shorelines, particularly along the South China beaches, remains inadequately understood. To address this gap, this study conducted high-frequency surveys on the Qing'an Bay beach in South China and employ a profile fitting model to refined waterline extraction from monthly satellite images. The goal was to investigate the relationship between high-frequency shoreline changes and interannual climate variability (such as ENSO). The findings indicate that the coastal evolution is intricately linked to a combination of strong wave events, summer storms, winter cold waves, and the amplifying effects of ENSO. During La Niña phase, the sea level in the western Pacific experiences a rise, with associated storms proving particularly destructive to the beach environment, leading to violent oscillations in the coastline. Specifically, in the strong La Niña phases, significant wave heights markedly surpass neutral conditions, precipitating intensified sediment movement. However, it is imperative to recognize that shoreline changes alone do not fully characterize coastal erosion. Assessment must also encompass alterations in beach volume. By calculating beach volume per unit width that accounts for the influence of ENSO variability on the beach, the seasonal characteristics of the beach showed sediment losses in summer and autumn, and gains in winter and spring, which was more obvious during strong La Niña. Additionally, conceptual model analysis reveals that during the La Niña phase, the bay is fully exposed, whereas during the El Niño phase, significant headland sheltering is observed.

Shoreliner: A Sub-Pixel Coastal Waterline Extraction Pipeline for Multi-Spectral Satellite Optical Imagery

Beach morphology can be observed over large spatio-temporal scales, and future shoreline positions can be predicted and coastal risk indicators can be derived by measuring satellite-derived instantaneous waterlines. Long-term satellite missions, such as Landsat and Sentinel-2, provide decades of freely available, high-resolution optical measurement datasets, enabling large-scale data collection and relatively high-frequency monitoring of sandy beaches. Satellite-Derived Shoreline (SDS) extraction methods are emerging and are increasingly being applied over large spatio-temporal scales. SDS generally consists of two steps: a mathematical relationship is applied to obtain a ratio index or pixel classification by machine-learning algorithms, and the land/sea boundary is then determined by edge detection. Indexes from lake waterline detection, such as AWEI or NDWI, are often transferred towards the shore without taking into account that these indexes are inherently affected by wave breaking. This can be overcome by using pixel classification to filter the indices, but this comes at a computational cost. In this paper, we carry out a thorough evaluation of the relationship between scene-dependent variables and waterline extraction accuracy, as well as a robust and efficient thresholding method for coastal land–water classification that optimises the index to satellite radiometry. The method developed for sandy beaches combines a new purpose-built multispectral index (SCoWI) with a refinement method of Otsu’s threshold to derive sub-pixel waterline positions. Secondly, we present a waterline extraction pipeline, called Shoreliner, which combines the SCoWI index and the extraction steps to produce standardised outputs. Implemented on the CNES High Performance Cluster (HPC), Shoreliner has been quantitatively validated at Duck, NC, USA, using simultaneous Sentinel-2 acquisitions and in situ beach surveys over a 3-year period. Out of six dates that have a satellite acquisition and an in situ survey, five dates have a sub-pixel RMS error of less than 10 m. This sub-pixel performance of the extraction processing demonstrates the ability of the proposed SDS extraction method to extract reliable, instantaneous and stable waterlines. In addition, preliminary work demonstrates the transferability of the method, initially developed for Sentinel-2 Level1C imagery, to Landsat imagery. When evaluated at Duck on the same day, Sentinel-2 and Landsat imagery several minutes apart provide similar results for the detected waterline, within the method’s precision. Future work includes global validation using Landsat’s 40 years of data in combination with the higher resolution Sentinel-2 data at different locations around the world.

Extracting waterline and inverting tidal flats topography based on Sentinel-2 remote sensing images: A case study of the northern part of the North Jiangsu radial sand ridges

Muddy tidal flats with landforms vary widely over space and time. The evolution of tidal flats topography has had a significant impact on the region. Therefore, there is a need to develop methods for rapidly and accurately characterizing tidal flats topography. This study applied thresholding segmentation (TS), machine learning, and two image classification methods to Sentinel-2 remote sensing images to extract the instantaneous waterline of the northern part of the North Jiangsu Radial sand Ridges (NJRSR). Tidal level data at specific time intervals were obtained using the Delft3D and TPXO9 models, after which tidal flats topography inversion was conducted. As a result, varied among the different classification methods, with the K-Nearest Neighbors (KNN) algorithm producing the optimal waterline extraction. Furthermore, the accuracy of tidal level simulation was improved by employing Delft3D software and constructing a scaled-down model of the study area. Comparisons with Delft3D model simulations of tidal levels with observations at three tide gauge stations yielded accuracies of 17.5 cm, 18.5 cm and 17.9 cm, this result showed that the establishment of the tidal level model can enhance the accuracy of tidal flats Digital Elevation Model (DEM) inversion. In fact, the combined application of the KNN algorithm and Delft3D model within retrieval of tidal level provided the most accurate topographic inversion, with an average error of 0.297 m, which was reduced to 0.292 m after median filtering, and the results indicated a trend of southward erosion in the tidal flats in the study area, with a turning point roughly located in the southern part of Doulong Port. The results of this study can help improve the future monitoring of dynamic changes in intertidal wetland topography and the conservation and development of tidal flats areas.

A Novel Approach for Instantaneous Waterline Extraction for Tidal Flats

For many remote sensing applications, the instantaneous waterline on the image is critical boundary information to separate land and water and for other purposes. Accurate waterline extraction from satellite images is a desirable feature in such applications. Due to the complex topography of low tidal flats and their indistinct spatial and spectral characteristics on satellite imagery, the waterline extraction for tidal flats (especially at low tides) from remote sensing images has always been a technically challenging problem. We developed a novel method to extract waterline from satellite images, assuming that the waterline’s elevation is level. This paper explores the utilization of bathymetry during waterline extraction and presents a novel approach to tackle the waterline extraction issue, especially for low tidal flats, using remote sensing images at mid/high tide, when most of the tidal flat area is filled with seawater. Repeated optical satellite images are easily accessible in the current days; the proposed approach first generates the bathymetry map using the mid/high-tide satellite image, and then the initial waterline is extracted using traditional methods from the low-tide satellite image; the isobath (depth contour lines of bathymetry), which corresponds to the initial waterline is robustly estimated, and finally an area-based optimization algorithm is proposed and applied to both isobath and initial waterline to obtain the final optimized waterline. A series of experiments using Sentinel-2 multispectral images are conducted on Jibei Island of Penghu Archipelago and Chongming Island to demonstrate this proposed strategy. The results from the proposed approach are compared with the Normalized Difference Water Index (NDWI) and Support Vector Machine (SVM) methods. The results indicate that more accurate waterlines can be extracted using the proposed approach, and it is very suitable for waterline extraction for tidal flats, especially at low tides.

Difference-Focusing Fusion Decision Method: An Ensemble Learning Framework and Its Application in Improving Deep Learning Sea–Land Segmentation for Waterline Extraction in Synthetic Aperture Radar Imagery

Difference-Focusing Fusion Decision Method: An Ensemble Learning Framework and Its Application in Improving Deep Learning Sea–Land Segmentation for Waterline Extraction in Synthetic Aperture Radar Imagery

On the extraction of the reservoirs’ waterline using polarimetric X-band SAR measurements: the case study of the San Giuliano reservoir, Italy

ABSTRACT According to the World Bank data catalogue, records of reservoirs and their associated dams are present summing up a capacity of km3 of water. They play a crucial role in providing potable and irrigation water and, therefore, it is of paramount interest to effectively monitor such critical infrastructures. An effective approach is based on satellite remote sensing and, in particular, on the Synthetic Aperture Radar (SAR). In this paper, we critically investigate the use of polarimetric SAR measurements for reservoirs’ waterline estimation. Measurements of the novel COSMO-SkyMed Second Generation (CSG) X-band quad-polarimetric SAR related to the San Giuliano reservoir, in the South of Italy, are used to carry out an electromagnetic analysis of the different polarimetric scattering returns. Experimental results show that the cross-polarized channel, as well as the inter-channel phase, are noisy and, therefore, uninformative when used to design coherent polarimetric waterline extraction methods. From an electromagnetic viewpoint, this is due to the peculiarities of the reservoirs that call for low surface roughness and negligible wave pattern that, at once, result in a joint combination of un-tilted Bragg scattering and specular reflection. This implies that a low co-polarized backscatter and a cross-polarized signal largely below the system noise floor are to be expected. As a consequence, waterline extraction approaches that do not exploit the inter-channel phase, the so-called incoherent approaches, are shown to outperform the coherent ones.

Mapping intertidal topographic changes in a highly turbid estuary using dense Sentinel-2 time series with deep learning

Intertidal mudflats are an important component of the coastal geomorphological system at the interface between ocean and land. Accurate and up-to-date mapping of intertidal topography at high spatial resolution, and tracking of its changes over time, are essential for coastal habitat protection, sustainable management and vulnerability analysis. Compared with ground-based or airborne terrain mapping, the satellite-based waterline method is more cost-effective for constructing large-scale intertidal topography. However, the accuracy of the waterline method is affected by the extraction of waterlines and the calibration of waterline height. The blurred boundary between turbid water and mudflats in the tide-dominated estuary brings enormous challenges in accurate waterline extraction, and the errors in estuarine water level simulations prevent the direct calibration of waterline heights. To address these issues, this paper developed a novel deep learning method using a parallel self-attention mechanism and boundary-focused hybrid loss to extract turbid estuarine waterlines accurately from dense Sentinel-2 time series. UAV photogrammetric surveys were employed to calibrate waterline heights rather than the simulated water levels, such that the error propagation is constrained effectively. Annual intertidal topographic maps of the Yangtze estuary in China were generated from 2020 to 2022 using the optimized waterline method. Experimental results demonstrate that the proposed deep learning method could achieve excellent performance in land and water segmentation in time-varying tidal environments, with better generalization capability compared with benchmark U-Net, U-Net++ and U-Net+++ models. The comparison between the generated topography and UAV photogrammetric observations resulted in an RMSE of 13 cm, indicating the effectiveness of the optimized waterline method in monitoring morphological changes in estuarine mudflats. The generated topographic maps successfully identified hotspots of mudflat erosion and deposition. Specifically, the mudflats connected to the land predominantly experienced deposition of 10–20 cm over the two-year period, whereas the offshore sandbars exhibited instability and significant erosion of 20–60 cm during the same period. These topographic maps serve as valuable datasets for providing scientific baseline information to support coastal management decisions.

A Transformer Model for Coastline Prediction in Weitou Bay, China

The simulation and prediction of coastline changes are of great significance for the development and scientific management of coastal zones. Coastline changes are difficult to capture completely but appear significantly periodic over a long time series. In this paper, the transformer model is used to learn the changing trend of the coastline so as to deduce the position of the coastline in the coming year. First, we use the distance regularization level set evolution (DRLSE) model for instantaneous waterline extraction (IWE) from preprocessed Landsat time-series images from 2010–2020 in Weitou Bay, China. Then, tidal correction (TC) is performed on the extracted instantaneous waterline dataset to obtain coastlines projected to a single reference tidal datum. Finally, the coastline datasets from 2010–2019 are used for model training, and the coastline in 2020 is used for accuracy assessment. Three precision evaluation methods, including receiver operating characteristic curve matching, the mean offset, and the root mean square error, were used to verify the predicted coastline data. The receiver operating characteristic curve was specifically designed and improved to evaluate the accuracy of the obtained coastline. Compared with the support vector regression (SVR) and long–short-term memory (LSTM) methods, the results showed that the coastline predicted by the transformer model was the closest to the accurate extracted coastline. The accuracies of the correct values corresponding to SVR, LSTM, and transformer models were 88.27%, 94.08%, and 98.80%, respectively, which indicated the accuracy of the coastline extraction results. Additionally, the mean offset and root mean square error were 0.32 pixels and 0.57 pixels, respectively. In addition, the experimental results showed that tidal correction is important for coastline prediction. Moreover, through field investigations of coastlines, the predicted results obtained for natural coastlines were more accurate, while the predicted results were relatively poor for some artificial coastlines that were intensely influenced by human activities. This study shows that the transformer model can provide natural coastline changes for coastal management.

Automatic Coastline Extraction Based on the Improved Instantaneous Waterline Extraction Method and Correction Criteria Using SAR Imagery

Coastlines with different morphologies form boundaries between the land and ocean, and play a vital role in tourism, integrated coastal zone management, and marine engineering. Therefore, determining how to extract the coastline from satellite images quickly, accurately, and intelligently without manual intervention has become a hot topic. However, the instantaneous waterline extracted directly from the image must be corrected to the coastline using the tide survey station data. This process is challenging due to the scarcity of tide stations. Therefore, an improved instantaneous waterline extraction method was proposed in this paper with an integrated Otsu threshold method, a region-growing algorithm, Canny edge detection, and a morphology operator. Based on SAR feature extraction and screening, the multi-scale segmentation method and KNN classification algorithms were used to achieve object-oriented automatic classification. According to different types of ground features, the correction criteria were presented and used in correcting the instantaneous waterline in biological coasts and undeveloped silty coasts. As a result, the accurate extraction of the coastline was accomplished in the area of the Yellow River Delta. The coastline was compared with that extracted from the GF-1 optical image. The result shows that the deviation degree was less than the field distance represented by three pixels.

MVCNN: A Deep Learning-Based Ocean–Land Waveform Classification Network for Single-Wavelength LiDAR Bathymetry

Ocean–land waveform classification (OLWC) is crucial in airborne LiDAR bathymetry (ALB) data processing and can be used for ocean–land discrimination and waterline extraction. However, the accuracy of OLWC for single-wavelength ALB systems is low given the nature of the green laser waveform in complex environments. Thus, in this article, a deep learning-based OLWC method called the multichannel voting convolutional neural network (MVCNN) is proposed based on the comprehensive utilization of multichannel green laser waveforms. First, multiple green laser waveforms collected in deep and shallow channels are input into a multichannel input module. Second, a one-dimensional (1-D) convolutional neural network (CNN) structure is proposed to handle each green channel waveform. Finally, a multichannel voting module is introduced to perform majority voting on the predicted categories derived by each 1-D CNN model and output the final waveform category (i.e., ocean or land waveforms). The proposed MVCNN is evaluated using the raw green laser waveforms collected by Optech coastal zone mapping and imaging LiDAR (CZMIL). Results show that the overall accuracy, kappa coefficient, and standard deviation of the overall accuracy for the OLWC utilizing green laser waveforms based on MVCNN can reach 99.41%, 0.9800, and 0.03%, respectively. Results further show that the classification accuracy of the MVCNN is improved gradually with the increase in the number of laser channels. The multichannel voting module can select the correct waveform category from the deep and shallow channels. The proposed MVCNN is highly accurate and robust, and it is slightly affected by aquaculture rafts and the merging effect of green laser waveform in very shallow waters. Thus, the use of MVCNN in OLWC for single-wavelength ALB systems is recommended. In addition, this article explores the relationships between green deep and shallow channel waveforms based on the analysis of CZMIL waveform data.

MULTISCALE SPATIAL RELATION EXTRACTION OF A REMOTELY SENSED WATERLINE IN A MUDDY COASTAL ZONE WITH CHONGMING DONGTAN AS AN EXAMPLE

Abstract. To address the difficulty of waterline extraction in areas with gentle slopes in the muddy coastal zone, Chongming Dongtan in the Yangtze River estuary is selected as the research object. This paper analyzes the spatial relationship of the waterline in Landsat images at three spatial scales, gives a method for numerically calculating the spatial relationship characteristics at different spatial scales and the application means in waterline extraction, and constructs a model for waterline extraction from Landsat images using the multiscale spatial relationship. Finally, the execution efficiency and positioning accuracy of the multiscale spatial relationship waterline extraction model are quantitatively evaluated. The results show that the spatial relationship is effectively used to solve the problem of discontinuous extraction of a weak waterline in the muddy coastal zone, and the waterline recognition ability for remote sensing images is significantly improved. The position accuracy of waterlines obtained by the improved method can reach 12.4 meters. Compared with other methods, this method shows great advantages in terms of automation, waterline continuity, and positioning accuracy and provides technical support for the use of remote sensing technology to study the dynamic changes in coastal zones.

Waterline Extraction for Artificial Coast With Vision Transformers

Accurate acquisition for the positions of the waterlines plays a critical role in coastline extraction. However, waterline extraction from high-resolution images is a very challenging task because it is easily influenced by the complex background. To fulfill the task, two types of vision transformers, segmentation transformers (SETR) and semantic segmentation transformers (SegFormer), are introduced as an early exploration of the potential of transformers for waterline extraction. To estimate the effects of the two methods, we collect the high-resolution images from the web map services, and the annotations are created manually for training and test. Through extensive experiments, transformer-based approaches achieved state-of-the-art performances for waterline extraction in the artificial coast.

A New Adaptive Remote Sensing Extraction Algorithm for Complex Muddy Coast Waterline

Coastline is an important geographical element of the boundary between ocean and land. Due to the impact of the ocean-land interactions at multiple temporal-spatial scales and the intensified human activities, the waterline of muddy coast is undergoing long-term and continuous dynamic changes. Using traditional remote sensing-based waterline extraction methods, it is difficult to achieve ideal results for muddy coast waterlines, which are faced with problems such as limited algorithm stability, weak algorithm migration, and discontinuous coastlines extraction results. In response to the above challenges, three different types of muddy coasts, Yancheng, Jiuduansha and Xiangshan were selected as the study areas. Based on the Sentinel-2 MSI images, we proposed an adaptive remote sensing extraction algorithm framework for the complex muddy coast waterline, named AEMCW (Adaptive Extraction for Muddy Coast Waterline), including main procedures of high-pass filtering, histogram statistics and adaptive threshold determination, which has the capability to obtain continuous and high-precision muddy coastal waterline. NDWI (Normalized Difference Water Index), MNDWI (Modified Normalized Difference Water Index) and ED (Edge Detection) methods were selected to compare the extraction effect of AEMCW method. The length and spatial accuracy of these four methods were evaluated with the same criteria. The accuracy evaluation presented that the length errors of ED method in all three study areas were minimum, but the waterline results were offset more to the land side, due to spectral similarity, turbid water and tidal flats having similar values of NDWI and MNDWI. Therefore, the length and spatial accuracies of NDWI and MNDWI methods were lower than AEMCW method. The length errors of the AEMCW algorithm in Yancheng, Jiuduansha, and Xiangshan were 14.4%, 18.0%, and 7.7%, respectively. The producer accuracies were 94.3%, 109.6%, and 94.2%, respectively. The user accuracies were 82.4%, 92.9%, and 87.5%, respectively. These results indicated that the proposed AEMCW algorithm can effectively restrain the influence of spectral noise from various land cover types and ensure the continuity of waterline extraction results. The adaptive threshold determination equation reduced the influence of human factors on threshold selection. The further application on ZY-1 02D hyperspectral images in the Yancheng area verified the proposed algorithm is transferable and has good stability.

Automatic Waterline Extraction and Topographic Mapping of Tidal Flats From SAR Images Based on Deep Learning

AbstractThis study presented an intuitive approach to derive large‐scale tidal flat's Digital Elevation Model (DEM). We first developed an automated method for accurately extracting the waterline from Synthetic Aperture Radar images acquired in Subei Sandbanks along the Yellow Sea coast of China between 2015 and 2020 based on deep convolutional neural networks. The statistical results show this method has appreciable accuracy for efficient waterline extraction even under complex imaging conditions with a mean recall and precision of 0.90 and 0.80, respectively. Then the pixel‐level extracted waterlines are calibrated with a global tide model to construct the large‐scale tidal flat's DEM in the study region. The comparison against in situ topographic data shows an error of 29 cm, demonstrating the usefulness of monitoring the morpho‐sedimentary evolution in intertidal areas. Furthermore, the Subei Sandbanks remained stable from 2015 to 2020, while the coastal region changed drastically due to human activities.

Statistical Analysis for Tidal Flat Classification and Topography Using Multitemporal SAR Backscattering Coefficients

Coastal zones are very dynamic natural systems that experience short-term and long-term morphological changes. Their highly dynamic behavior requires frequent monitoring. Tidal flat topography for a large spatial coverage has been generated mainly by the waterline extraction method from multitemporal remote sensing observations. Despite the efficiency and robustness of the waterline extraction method, the waterline-based digital elevation model (DEM) is limited to representing small scale topographic features, such as localized tidal tributaries. Tidal flats show a rapid increase in SAR backscattering coefficients when the tide height is lower than the tidal flat topography compared to when the tidal flat is covered by water. This leads to a tidal flat with a distinct statistical behavior on the temporal variability of our multitemporal SAR backscattering coefficients. Therefore, this study aims to suggest a new method that can overcome the constraints of the waterline-based method by using a pixel-based DEM generation algorithm. Jenks Natural Break (JNB) optimization was applied to distinguish the tidal flat from land and ocean using multitemporal Senitnel-1 SAR data for the years 2014–2020. We also implemented a logistic model to characterize the temporal evolution of the SAR backscattering coefficients along with the tide heights and estimated intertidal topography. The Sentinel-1 DEM from the JNB classification and logistic function was evaluated by an airborne Lidar DEM. Our pixel-based DEM outperformed the waterline-based Landsat DEM. This study demonstrates that our statistical approach to intertidal classification and topography serves to monitor the near real-time spatiotemporal distribution changes of tidal flats through continuous and stable SAR data collection on local and regional scales.

Mapping Australia's dynamic coastline at mean sea level using three decades of Landsat imagery

Accurate, robust and consistent coastline mapping is critical for characterising and managing coastal change. Satellite earth observation provides an unparalleled source of freely available data for studying dynamic coastlines through time and across large spatial extents. However, previous satellite-derived shoreline mapping approaches have been challenged by two key limitations: the medium spatial resolution of freely available satellite data, and the confounding influence of tides that can obscure longer term patterns of coastal change. In this study we present Digital Earth Australia Coastlines, a new continental dataset documenting three decades of coastal change across Australia. We combine sub-pixel waterline extraction with a new pixel-based tidal modelling method to seamlessly map almost 2 million km of tide-datum shorelines along the entire Australian coast from 1988 to 2019. Our tidally-constrained median composite approach maps the dominant annual position of the shoreline at 0 m Above Mean Sea Level each year, suppressing the short-term influence of tides and sub-annual shoreline variability. Using this robust mid-term shoreline proxy, long-term coastal change rates spanning the last three decades were accurately quantified and mapped at the continental scale. We find that 22% of Australia's non-rocky coastline has retreated or grown significantly since 1988, with 16% changing at greater than 0.5 m per year. Although trends of retreat and growth were closely balanced across the Australian continent, our results highlight significant regional variability and extreme local hotspots of coastal change. Our findings provide new insights into patterns and trends of coastal change across Australia, and highlight advantages and limitations of tide modelling and composite-based methods for extracting consistent shoreline data and long-term coastal trends from earth observation data at continental scale. Digital Earth Australia Coastlines is made available to the public as free and open interactive tools and code to support future coastal research and management across Australia, and any coastal region globally with access to free and open medium resolution satellite data.

An optimal waterline approach for studying tidal flat morphological changes using remote sensing data: A case of the northern coast of Vietnam

Tidal flats on the north coast of Vietnam suffer diurnal tide with a tide range varying between 0.3 m and 3.5 m. Along the 350 km long coastline, the diversity of environmental conditions induces various tidal flats with different characteristics. This study applies the waterline method for multi-temporal satellite images to build Digital Elevation Models (DEMs) of tidal flats during the last 25 years. 117 Landsat images acquired with TM, ETM+, and OLI have been processed to construct tidal flat DEMs in 1989, 2000, and 2014. Waterlines extracted from single spectral bands (near-infrared [NIR], short wave infrared [SWIR]) or band ratios (normalized difference water index [NDWI], normalized difference vegetation index [NDVI], Green/SWIR) of the Landsat data have been compared with waterlines digitalized on Spot, Aster and Worldview 2 images. This experiment allows us to determine the best band (or band ratio) for extracting waterlines depending on local conditions. Consequently, the study shows that the Green/SWIR ratio image is a good solution for extracting waterlines in the black coal tidal flats of Cam Pha. However, the NDWI index appears to be a better choice for the other parts of the study area. The vertical accuracy of the tidal flat DEMs reaches 0.144 m. The change analysis of the DEMs also emphasizes the tidal flat evolution in both vertical and horizontal dimensions, i.e. erosion or accretion. The erosion of the tidal flats along the northern coast of Vietnam is particularly developed in the area extending from Yen Hung to Mong Cai, especially in Mong Cai with an amount of about 50 × 106 m3 of sediments lost between 1989 and 2014. On the contrary, the tidal flats in the south of the study area show a high rate of deposition due to the sediments fed by Red and Thai Binh rivers. About 35 × 106 m3 of sediments deposited in the tidal flat surrounding the Red River mouth between 1989 and 2014. This study represents a development of the waterline extraction method to investigate the evolution of tidal flat at a large scale and a diversified coastal environment using optical satellite images and fieldwork.

Multi-polarization time-series of Sentinel-1 SAR imagery to analyze variations of reservoirs’ water-body

In this article, a two-year time-series of multipolarization Sentinel-1 synthetic aperture radar (SAR) imagery is exploited to analyze the changes in the water-covered area of the Monte Cotugno (Italy) reservoir. A two-step processing chain, which includes water/land separation and waterline extraction, is proposed to accomplish this task. Experimental results, verified using independent in situ measurements, demonstrate that: first, Sentinel-1 time series can be successfully used to support the smart water management of reservoirs. In fact, the changes in the water-covered area inferred from the SAR time series agree with the seasonal behavior and they also fit anomalies; and second, multipolarization feature outperforms single-polarization ones in terms of accuracy of the extracted waterline profile.

Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

Accurately mapping the boundary between land and water (the ‘waterline’) is critical for tracking change in vulnerable coastal zones, and managing increasingly threatened water resources. Previous studies have largely relied on mapping waterlines at the pixel scale, or employed computationally intensive sub-pixel waterline extraction methods that are impractical to implement at scale. There is a pressing need for operational methods for extracting information from freely available medium resolution satellite imagery at spatial scales relevant to coastal and environmental management. In this study, we present a comprehensive evaluation of a promising method for mapping waterlines at sub-pixel accuracy from satellite remote sensing data. By combining a synthetic landscape approach with high resolution WorldView-2 satellite imagery, it was possible to rapidly assess the performance of the method across multiple coastal environments with contrasting spectral characteristics (sandy beaches, artificial shorelines, rocky shorelines, wetland vegetation and tidal mudflats), and under a range of water indices (Normalised Difference Water Index, Modified Normalised Difference Water Index, and the Automated Water Extraction Index) and thresholding approaches (optimal, zero and automated Otsu’s method). The sub-pixel extraction method shows a strong ability to reproduce both absolute waterline positions and relative shape at a resolution that far exceeds that of traditional whole-pixel methods, particularly in environments without extreme contrast between the water and land (e.g., accuracies of up to 1.50–3.28 m at 30 m Landsat resolution using optimal water index thresholds). We discuss key challenges and limitations associated with selecting appropriate water indices and thresholds for sub-pixel waterline extraction, and suggest future directions for improving the accuracy and reliability of extracted waterlines. The sub-pixel waterline extraction method has a low computational overhead and is made available as an open-source tool, making it suitable for operational continental-scale or full time-depth analyses aimed at accurately mapping and monitoring dynamic waterlines through time and space.

Topographical Mapping of a Bare Tidal Flat Outside a Mangrove Area based on the Waterline Method and an Iterative Hydrodynamic Model: A Case Study of Yingluo Bay, South China

abstractA change in the elevation of bare tidal flats outside a mangrove area is an indispensable factor for the sustainable development of mangroves. Waterline extraction, as an effective and economical tool used in reconstructing the terrain of an intertidal zone, has been widely applied to open-coast tidal flats by constructing a digital elevation model (DEM). However, mangrove wetlands are usually located in wave-sheltered sites, such as estuaries and bays that have narrow tidal channels flanked by tidal flats. Changes in water level are affected by the dry-wet processes of complex landforms caused by tides. This article takes as a study case the area of Yingluo Bay, which covers the core region of the Zhanjiang and Shankou National Mangrove National Nature Reserve in southwestern China. Waterline extraction based on seventeen multisource and multispectral satellite images obtained from December 2014 to April 2015, combining the finite-volume coastal ocean model (FVCOM) hydrodynamic model in an iterative process, was used to generate a topographical map of the bare tidal flat outside the mangrove area in Yingluo Bay. The quality of the iterative DEMs was evaluated via six transects of a ground-based survey using Real - time kinematic (RKT) GPS in May 2015. The mean absolute error (MAE) and root mean square error (RMSE) of the DEM decreased with an increase in the number of iterations. In this study, the DEM in the third iteration was used as the final output because the difference from the previous iterative DEM satisfied an inversion-stopping criterion. The MAE and RMSE of the final DEM with the measured data were 0.072 and 0.09 m, respectively, without considering small tidal creeks. The method used in this study can be an effective and highly precise approach for detecting and reconstructing the historical terrain of a bare tidal flat outside a mangrove area. This work also has great importance regarding intertidal resource management and the sustainable development of mangroves facing the vulnerable coastal ecological environment.