Shoreline extraction based on an active connection matrix (ACM) image enhancement strategy

Coastal environments are facing constant changes over time due to their dynamic nature and geological, geomorphological, hydrodynamic, biological, climatic and anthropogenic factors. For these reasons, the monitoring of these areas is crucial for the safeguarding of the cultural heritage and the populations living there. The focus of this paper is shoreline extraction by means of an experimental algorithm, called J-Net Dynamic (Semeion Research Center of Sciences of Communication, Rome, Italy). It was tested on two types of image: a very high resolution (VHR) multispectral image (WorldView-2) and a high resolution (HR) radar synthetic aperture radar (SAR) image (Sentinel-1). The extracted shorelines were compared with those manually digitized for both images independently. The results obtained with the J-Net Dynamic algorithm were also compared with common algorithms, widely used in the literature, including theWorldView water index and the Canny edge detector. The results show that the experimental algorithm is more effective than the others, as it improves shoreline extraction accuracy both in the optical and SAR images

Spectral characterization of the Nigerian shoreline using Landsat imagery

The challenges of shoreline mapping include the high costs of acquiring up-to-date survey data over the coastal area. As a result, in many developing countries, the shoreline has not been consistently mapped. The variety of methods used for this mapping and the large time differences between the surveys (on the order of decades) could result in inaccuracies in shoreline data. This study presents the development of a shoreline characterization procedure for the Nigerian coastline using satellite remote sensing technology. The study goal is to produce a complete, consistent and continuous shoreline map using publicly available data processed in a GIS environment. A spectral analysis using different satellite bands was conducted to define the land/water boundary and characterize the coastal area around the shoreline. The satellite-derived shorelines were compared to charted shorelines for adequacy and consistency. The procedure was developed based on study sites along the Nigerian coastline. Although the shoreline characterization procedure is developed based on datasets from Nigeria, the procedure should be suitable for use in mapping other developing areas around world

DIGITAL IMAGE PROCESSING OF SPOT-4 FOR SHORELINE EXTRACTION IN LAMPUNG BAY

Shoreline is an imaginary line separating land and seawater. The intensification of land used/land cover at Lampung bay causes shoreline change either abrasions or accretions. The objectives of this study were to compare the shoreline extraction based on the digital image processing of SPOT-4 using ratio band of infrared and green band, Normalized Difference Vegetation Index (NDVI), and (band infrared) methods and to analyze shoreline change at Lampung Bay. Those methods applied on both cloudy free and cloudy SPOT-4 images and the result compared with RBI map as reference. The result showed that the best metod for shoreline axtraction was ratio band due to accuracy high and stable eventhough it applied on cloudy image. The shoreline changes at Lampung Bay along 2008 to 2012 caused by accretions. The total area of accretion at Lampung Bay for fours years were 662 Ha with the rates 165 Ha/year. The high of accretion rate caused by reclamation for urban built up, fishponds and mangrove

New Approaches for Evaluating Lidar-Derived Shoreline

This study presents and compares two new methods of assessing the uncertainty of lidar-derived National Shoreline mapped by NOAA’s National Geodetic Survey: an empirical (ground-based) approach and a stochastic (Monte Carlo) approach. OCIS codes: (280.3640) Lidar; (120.2830) Height measurements; (000.4430) Numerical approximation and analysi

Effect of different segmentation methods using optical satellite imagery to estimate fuzzy clustering parameters for Sentinel-1A SAR images

Optical and SAR data are efficient data sources for shoreline monitoring. The processing of SAR data such as feature extraction is not an easy task since the images have totally different structure than optical imagery. Determination of threshold value is a challenging task for SAR data. In this study, SENTINEL-2A optical data was used as ancillary data to predict fuzzy membership parameters for segmentation of SENTINEL-1A SAR data to extract shoreline. SENTINEL-2A and SENTINEL-1A satellite images used were taken in September 9, 2016 and September 13, 2016 respectively. Three different segmentation algorithms which are selected from object, learning and pixel-based methods. They have been exploited to obtain land and water classes which have been used as an input data for parameter estimation. Thus, the performance of different segmentation algorithm has been investigated and analysed. In the first step of the study, Mean-Shift, Random Forest and Whale Optimization algorithms have been employed to obtain water and land classes from the SENTINEL-2A image. Water and land classes derived from each algorithm – are used as input data, and then the required parameters for the fuzzy clustering of SENTINEL-1A SAR image, were calculated. Lake Constance, Germany has been chosen as the study area. In this study, additionally an interface plugin has been developed and integrated into the open source Quantum GIS software platform. The developed interface allows non-experts to process and extract the shorelines without using any parameters. But, this system requires pre-segmented data as input. Thus, the batch process calculates the required parameters

Characterization of the Nigerian Shoreline using Publicly-Available Satellite Imagery

Current methods of shoreline mapping include aerial and high-resolution satellite imagery and ground-based surveying, all of which require considerable investment of human and material resources. Mapping and continuous updating of the shoreline for developing countries, such as Nigeria, is a challenge. Most of the information on the Nigerian shoreline is based on ‘surveys of opportunity’ performed by various government agencies over a wide time span. Additional surveys conducted by the multi-national oil and gas companies exploring in the region are typically not available for use by government agencies. In cases where the data are available, the variety in methods used for shoreline mapping can result in inconsistencies

SHORELINE CHANGES AFTER THE SUNDA STRAIT TSUNAMI ON THE COAST OF PANDEGLANG REGENCY, BANTEN

The Sunda Strait tsunami occurred on the coast of west Banten and South Lampung at 22nd December 2018, resulting in 437 deaths, with10 victims missing. The disaster had various impacts on the environment and ecosystem, with this area suffering the greatest effects from the disaster. The utilisation of remote sensing technology enables the monitoring of coastal areas in an effective and low-cost manner. Shoreline extraction using the Google Earth Engine, which is an open-source platform that facilitates the processing of a large number of data quickly. This study used Landsat-8 Surface Reflectance Tier 1 data that was geometrically and radiometrically corrected, with processing using the Modification of Normalized Difference Water Index (MNDWI) algorithm. The results show that 30.1% of the coastline in Pandeglang Regency occurred suffered abrasion, 20.2% suffered accretion,while 40.7% saw no change. The maximum abrasion of 130.2 meters occurred in the village of Tanjung Jaya. Moreover, the maximum shoreline accretion was 43.3 meters in the village of Panimbang Jaya. The average shorelinechange in Pandeglang Regencywas 3.9 meters

Monitoring coastal storms’ effects on the Trabucador barrier beach (Ebro Delta) through Sentinel-2 derived shorelines

The vulnerable Trabucador barrier beach has recently suffered significant storm-induced geomorphological changes. This study presents the monitoring of its shoreline during storm events for assessing their effects on beach dynamics. After fine-tuning the CoastSat tool (i.e. optimal NDWI threshold) for shoreline extraction from Sentinel-2 imagery (S2), results were validated with GNSS-RTK reference shorelines (RMSE = 6.8 m). Shorelines were extracted from Dec-2019 to Feb-2021, encompassing 11 storms (Hs > 2m; duration = 24h), including Gloria (Jan-2020). Results showed that S2 imagery provides enough temporal and spatial resolution to capture the storm effects on the site. The shoreline timeseries gave relevant information about the geomorphological processes occurring during storm events (barrier breaching, erosion, washover), allowing the assessment of their cumulative effects. These results might be important for coastal management, in a site suffering from chronic flooding.Peer ReviewedPostprint (author's final draft

Majority voting of ensemble classifiers to improve shoreline extraction of medium resolution satellite images

Coastal zones are constantly exposed to changes caused by natural processes, anthropogenic activities or both, which can precariously alter the coastal landscapes of many countries. Thus, monitoring of coastal zones is needed to provide important information about current conditions of a countrys coastal areas by examining changes that are taking place. In this respect, such monitoring can be carried out by traditional ground survey, airborne aerial photo, or remote sensing. However, the former is more effective and efficient as it can extract vital boundary information from satellite images using appropriate image analysis. Nonetheless, shoreline extraction has a number of challenges, and many methods have been proposed to improve such extraction, such as the use of machine learning methods. Thus, this study was carried out to determine the most effective ensemble voting classifier based on two different types of classifiers, comprising 11 single classifiers and 4 ensemble classifiers. Performance criteria of the classifiers were based on the overall accuracy, training time, and testing time. The analysis of the experimental data revealed several interesting results. First, for the combination of single and ensemble classifiers, ensemble classifiers with majority voting of Random Forest and Support Vector Machine RBF kernel were the most effective classifiers, attaining high overall accuracy. Second, for the combination of two single classifiers, Multilayer Perceptron and k-Nearest Neighbor attained high overall accuracy, rendering them as the most effective classifiers in this category of classifiers. Third, there were trade-offs between performance measures, as increased overall accuracy was accompanied by longer training and testing time in the performance of such classifiers as both of voting-based ensemble classifiers increased significantly