Impatti, adattamento e vulnerabilita Le cause e le responsabilita dei cambiamenti climatici sono state trattate sul numero di ottobre della rivista Cda. Approfondiamo l’argomento presentando il documento: “Cambiamenti climatici 2007: impatti, adattamento e vulnerabilita” votato ad aprile 2007 dal secondo gruppo di lavoro del Comitato Intergovernativo sui Cambiamenti Climatici (Intergovernmental Panel on Climate Change). Si tratta del secondo di tre documenti che compongono il quarto rapporto sui cambiamenti climatici.

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Climate Change 2007: Impacts, Adaptation and Vulnerability.

Preparing the books to read every day is enjoyable for many people. However, there are still many people who also don't like reading. This is a problem. But, when you can support others to start reading, it will be better. One of the books that can be recommended for new readers is dynamics and modelling of ocean waves. This book is not kind of difficult book to read. It can be read and understand by the new readers.

Dynamics and Modelling of Ocean Waves

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

Geosynthetics for waterways and flood protection structures – Controlling the interaction of water and soil

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

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

In the present paper, the coastal risk is assessed, at a regional scale, to produce a risk map that will help to prioritize policies and economic resources in coastal management and planning activities. The DPSIR method is here used taking into account processes and cause/effect relationship between Drivers and Pressures, which induce an alteration of actual State and, hence, Impacts on the environment, society and economy. The study area is located in South Italy (Apulia region), where the Risk Index is calculated and mapped for all municipalities facing the Adriatic and Ionian Seas. Both coastal Vulnerability and Exposure Indexes are firstly calculated according to the procedure suggested in the EUrosion project (EU model) as the product of specific indicators describing the state of coastal zones, their natural characteristics and both natural and anthropic pressures. Based on both EU model results and knowledge of states and pressures of the study area, a new modified model is then proposed (Mod.E.M.) and final risk maps compared. The comparison shows that new Vulnerability and Exposure indexes better describe the ongoing coastal processes and pressures and allow us to identify hot-spot sites where more detailed analyses could be further focused on. The Mod.E.M. has more than just a local significance since the case study includes coastal areas with so different characteristics, that it can be easily applied to other coastal regions. Moreover, to take into account climate change effects, Risk Index is evaluated under Representative Concentration Pathways R C P 4.5 and R C P 8.5 , mainly affecting the sea level rise and the storm surge level.

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The DPSIR Approach for Coastal Risk Assessment under Climate Change at Regional Scale: The Case of Apulian Coast (Italy)

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

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.

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Alessandra Saponieri

Papers

New algorithms for shoreline monitoring from coastal video systems

The DPSIR Approach for Coastal Risk Assessment under Climate Change at Regional Scale: The Case of Apulian Coast (Italy)

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

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