Surface water mapping is essential for monitoring climate change, water resources, ecosystem services and the hydrological cycle. In this study, we adopt a multilayer perceptron (MLP) neural network to identify surface water in Landsat 8 satellite images. To evaluate the performance of the proposed method when extracting surface water, eight images of typical regions are collected, and a water index and support vector machine are employed for comparison. Through visual inspection and a quantitative index, the performance of the proposed algorithm in terms of the entire scene classification, various surface water types and noise suppression is comprehensively compared with those of the water index and support vector machine. Moreover, band optimization, image preprocessing and a training sample for the proposed algorithm are analyzed and discussed. We find that (1) based on the quantitative evaluation, the performance of the surface water extraction for the entire scene when using the MLP is better than that when using the water index or support vector machine. The overall accuracy of the MLP ranges from 98.25–100%, and the kappa coefficients of the MLP range from 0.965–1. (2) The MLP can precisely extract various surface water types and effectively suppress noise caused by shadows and ice/snow. (3) The 1–7-band composite provides a better band optimization strategy for the proposed algorithm, and image preprocessing and high-quality training samples can benefit from the accuracy of the classification. In future studies, the automation and universality of the proposed algorithm can be further enhanced with the generation of training samples based on newly-released global surface water products. Therefore, this method has the potential to map surface water based on Landsat series images or other high-resolution images and can be implemented for global surface water mapping, which will help us better understand our changing planet.

Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images

Coastline extraction is a fundamental work for coastal resource management, coastal environmental protection and coastal sustainable development. Due to the free access and long-term record, Landsat series images have the potential to be used for coastline extraction. However, dynamic features of different types of coastlines (e.g., rocky, sandy, artificial), caused by sea level fluctuation from tidal, storm and reclamation, make it difficult to be accurately extracted with coarse spatial resolution, e.g., 30 m, of Landsat images. To access this problem, we analyze the performance of coastline extraction by integrating downscaling, pansharpening and water index approaches in increasing the accuracy of coastline extraction from the latest Landsat-8 Operational Land Imager (OLI) imagery. In order to prove the availability of the proposed method, we designed three strategies: (1) Strategy 1 uses the traditional water index method to extract coastline directly from original 30 m Landsat-8 OLI multispectral (MS) image; (2) Strategy 2 extracts coastlines from 15 m fused MS images generated by integrating 15 m panchromatic (PAN) band and 30 m MS image with ten pansharpening algorithms; (3) Strategy 3 first downscales the PAN band to a finer spatial resolution (e.g., 7.5 m) band, and then extracts coastlines from pansharpened MS images generated by integrating downscaled spatial resolution PAN band and 30 m MS image with ten pansharpening algorithms. Using the coastline extracted from ZiYuan-3 (ZY-3) 5.8 m MS image as reference, accuracies of coastlines extracted from MS images in three strategies were validated visually and quantitatively. The results show that, compared with coastline extracted directly from 30 m Landsat-8 MS image (strategy 1), strategy 3 achieves the best accuracies with optimal mean net shoreline movement (MNSM) of −2.54 m and optimal mean absolute difference (MAD) of 11.26 m, followed by coastlines extracted in strategy 2 with optimal MNSM of −4.23 m and optimal MAD of 13.54 m. Further comparisons with single-band thresholding (Band 6), AWEI, and ISODATA also confirmed the superiority of strategy 3. For the various used pansharpening algorithms, five multiresolution analysis MRA-based pansharpening algorithms are more efficient than the component substitution CS-based pansharpening algorithms for coastline extraction from Landsat-8 OLI imagery. Among the five MRA-based fusion methods, the coastlines extracted from the fused images generated by Indusion, additive a trous wavelet transform (ATWT) and additive wavelet luminance proportional (AWLP) produced the most accurate and visually realistic representation. Therefore, pansharpening approaches can improve the accuracy of coastline extraction from Landsat-8 OLI imagery, and downscaling the PAN band to finer spatial resolution is able to further improve the coastline extraction accuracy, especially in crenulated coasts.

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

Field Guide to Visual and Ophthalmic Optics

Coastal areas are quite fragile landscapes as they are among the most vulnerable to climate change and natural hazards. Coastline mapping and change detection are essential for safe navigation, resource management, environmental protection, and sustainable coastal development and planning. In this paper, we proposed a new methodology for the automatic extraction of coastline, using aerial images. This method is based on edge detection and active contours (snake method). Initially the noise of the image is reduced which is followed by an image segmentation. The output images are further processed to remove all small spatial objects and to concentrate on the spatial objects of interests. Then, the morphological operators are applied. We used aerial images taken from an aircraft and high-resolution satellite images from a coastal area in Crete, Greece, and we compared the results with geodetic measurements, to validate the methodology.

https://www.mdpi.com/2076-3263/8/11/407/pdf

Automatic Coastline Extraction Using Edge Detection and Optimization Procedures

The current review study focuses on a statistical analysis (Meta-analysis) of the most common methods, materials, software, and indices used by researchers over the last twenty years to evaluate an...

https://www.tandfonline.com/doi/pdf/10.1080/22797254.2021.1904293

A review and meta-analysis of remote sensing data, GIS methods, materials and indices used for monitoring the coastline evolution over the last twenty years

Video systems have become widely used all around the world in coastal monitoring strategies, allowing both high temporal and spatial sampling frequency, with low logistic and costs efforts. The present paper deals with a new tool for coastal images processing, aimed at the automatic shoreline detection and data analysis. The tool is composed by a shoreline detection routine implemented in a web-application, addressed at images processing (i.e. shoreline extraction and geo-rectification), data analysis and sharing results about beach actual state and shore evolution in quasi-real time. The Shoreline Detection Model (SDM) is based on a new algorithm, implementing image-processing procedures, which allows extracting the sea/land boundary from automatic segmented Timex images. The SDM calibration and validation has been performed on different coastal images derived from a video monitoring system installed at Alimini (Lecce, IT) in 2005, by comparing automatic shoreline contours with the manual detected ones. Moreover, in December 2015, new video monitoring systems were installed in South Italy (Porto Cesareo and Torre Canne, Apulia region), at sandy beaches affected by erosion phenomena. The application of the SDM on images recorded by the new systems has allowed testing the model feasibility at sites characterized by different morphological features and geographical exposition. The present describes in detail the SDM algorithm and the image processing procedures used. The results of the model calibration and validation performed at Alimini and the tests performed at Porto Cesareo on first images are reported.

New algorithms for shoreline monitoring from coastal video systems

A new video monitoring system in support of Coastal Zone Management at Apulia Region, Italy

Coastal video monitoring has proven to be a valuable ground-based technique to investigate ocean processes. Presently, there is a growing need for automatic, technically efficient, and inexpensive solutions for image processing. Moreover, beach and coastal water quality problems are becoming significant and need attention. This study employs a methodological approach to exploit low-cost smartphone-based images for coastal image classification. The objective of this paper is to present a methodology useful for supervised classification for image semantic segmentation and its application for the development of an automatic warning system for Sargassum algae detection and monitoring. A pixel-wise convolutional neural network (CNN) has demonstrated optimal performance in the classification of natural images by using abstracted deep features. Conventional CNNs demand a great deal of resources in terms of processing time and disk space. Therefore, CNN classification with superpixels has recently become a field of interest. In this work, a CNN-based deep learning framework is proposed that combines sticky-edge adhesive superpixels. The results indicate that a cheap camera-based video monitoring system is a suitable data source for coastal image classification, with optimal accuracy in the range between 75% and 96%. Furthermore, an application of the method for an ongoing case study related to Sargassum monitoring in the French Antilles proved to be very effective for developing a warning system, aiming at evaluating floating algae and algae that had washed ashore, supporting municipalities in beach management.

Assessment of a Smartphone-Based Camera System for Coastal Image Segmentation and Sargassum monitoring

Storm surge and wave runup are key determinants of the potential for beach overwashing during storm events. However, the prediction and quantification of wave runup on embayed beaches is strongly influenced by particular characteristics (e.g., irregular morphology, low tides, absence of swell, etc.) which differ from those on open beaches, and have rarely been investigated in literature. In the present paper, a model framework aimed at predicting wave-induced runup on an embayed sandy beach is validated by means of measurements derived from a video-monitoring station, recently installed in South Italy, during two storm events in 2016. The numerical approach employs MeteOcean forecasted waves within SWAN and SWASH models (in both 2-d and 1-d mode). The combination of multibeam and d-RTK surveys with Unmanned Aerial Vehicle (UAV) imagery provides high resolution depth grid (m 0.015), particularly required in shallow waters, where wave hydrodynamics is highly influenced by the bottom. The results show and discuss the agreement between video measurements and 2-d predictions of runup. A sensitivity analysis of the Manningfls roughness factor is needed in 1-d simulations. The accuracy of the empirical formulas in predicting wave runup in an embayed beach is also investigated , showing mainly an overestimation of the observations.

Exploiting remote imagery in an embayed sandy beach for the validation of a runup model framework

A new experimental campaign on a 2D movable-bed physical model, reproducing a typical nourishment sandy beach profile, is being carried out in the wave flume of the Laboratory of Coastal Engineering at Politecnico di Bari (Bari, Italy). The main aim is to assess the short-term evolution of a sandy beach nourishment, relying on a mixed solution built on the deployment of a Beach Drainage System (BDS) and a rubble-mound detached submerged breakwater. This paper aims at illustrating the experimental findings. Tests presented herein deal with both unprotected and protected configurations, focusing on the hydrodynamic and morphodynamic processes under erosive conditions. Results show that, with respect to the unprotected conditions, BDS reduces the shoreline retreat and the beach steepen within swash and surf zone as well. Moreover, a reduction of net sediment transport rate is observed. When BDS is coupled with the submerged sill, a reversal of the prevalent direction of the net sediment transport seaward occurs offshore the sheltered region. Less considerable positive effects on shoreline retreat are induced by the submerged structure, whereas the mean beach slope remains quite stable. Secondary effects of drain on the submerged sill performance are also highlighted. BDS reduces wave-induced setup on beach, by mitigating the mean water level raising, typically experienced by such structures.

/pdf/laboratory-investigation-on-the-evolution-of-a-sandy-beach-15a71chu2r.pdf

Nico Valentini

Papers

New algorithms for shoreline monitoring from coastal video systems

A new video monitoring system in support of Coastal Zone Management at Apulia Region, Italy

Assessment of a Smartphone-Based Camera System for Coastal Image Segmentation and Sargassum monitoring

Exploiting remote imagery in an embayed sandy beach for the validation of a runup model framework

Laboratory Investigation on the Evolution of a Sandy Beach Nourishment Protected by a Mixed Soft–Hard System