Francisco Gutierres

Remote Sensing Technologies for the Assessment of Marine and Coastal Ecosystems

This chapter reviews the Remote Sensing (RS) technologies that are particularly appropriate for marine and coastal ecosystem research and management. RS techniques are used to perform analysis of water quality in coastal water bodies; to identify, characterize and analyze river plumes; to extract estuarine/coastal sandy bodies; to identify beach features/patterns; and to evaluate the changes and integrity (health) of the coastal lagoon habitats. For effective management of these ecosystems, it is essential to have satellite data available and complementary accurate information about the current state of the coastal regions, in addition to well-informed forecasts about its future state. In recent years, the use of space, air and ground-based RS strategies has allowed for the rapid data collection, Image processing (Pixel-Based and Object-Based Image Analysis (OBIA) classification) and dissemination of such information to reduce vulnerability to natural hazards, anthropic pressures, and to monitoring essential ecological processes, life support systems and biological diversity.

A Hybrid CA-ANN-Fuzzy Model for Simulating Coastal Changing Patterns

Shoreline erosion is a problem that causes major concerns to coastal cities worldwide. About 70% of the world’s sandy beaches retreated at a rate of 0.5–1.0 m.year−1. Therefore, the protection against beach loss and appropriate land management along the shoreline are critical issues that need to be addressed. The modelling and simulation of dynamic and complex systems, such as coastal areas, are important for the definition of an innovative planning and management strategy. To explore sandy beaches threatened by shoreline retreat, this works aims to develop a geosimulation hybrid model. The geosimulation (geocomputation) is an emergent field of analysis embracing heuristic search, artificial neural networks and cellular automata, among others. In this chapter we present a method to simulate both the coast line and the land use/cover evolution in a developed costal area reality, by coupling cellular automata (CA) and multi-layer perceptron (MLP) artificial neural network (ANN) with fuzzy set theory (CA–ANN-Fuzzy) in a GIS environment. Such alterations simulation solely by means of cellular automata isn’t suitable, because these models, in its more conventional structure, comprise limitations in the space parameters and transition rules. In this work a neural network is used to calibrate the importance degree that each prediction variable (probability) has in the geographic constraints (weights), i.e. considers spatial and temporal nonlinearities of the driving forces underlying the urban growth processes, while fuzzy set theory captures the uncertainty associated with transition rules. The proposed method predict high shoreline drawbacks in only 14 years, mainly at North (40 meters) and West (20 meters). The model has an overall accuracy of 86% (14% of error in 60 years).

Spatially Explicit Models in Local Dynamics Analysis: The Potential Natural Vegetation (PNV) as a Tool for Beach and Coastal Management

The concept of Potential Natural Vegetation (PNV) and its mapping have become extremely important within the scope of habitat restoration in almost every European country. The aim of this study is to predict the PNV in the sites of Natura 2000 Network ‘Sado Estuary’ and ‘Comporta-Gale’ based on the vegetation series and the main environmental variables. The modelling approach is based on the distribution of communities referred to as classification then modelling.

Remote Sensing Data and Image Classification Algorithms in the Identification of Beach Patterns

Remote sensing data and image classification algorithms can be very useful in the identification of beach patterns and therefore can be used as inputs in beach classification models. In this work, one aerial photograph, one IKONOS-2 image and one FORMOSAT-2 image were applied to a part of the northwest coast of Portugal. Several image processing algorithms were employed and compared: pixel-based approach, object-based approach, Principal Components Analysis (PCA), Artificial Neural Network (ANN) and Decision Trees (DT). The ANN and DT algorithms employed conduced to better results than the traditional classification methodologies (pixel-based, object-based and PCA), allowed a more accurate identification of rip currents. Regarding the data used, the high spatial resolution of aerial photograph allows for the better discrimination of different micro patterns. The FORMOSAT-2 image presents a lower spatial resolution, which did not allow for the identification of small microforms. Concluding, the conjugation of better spatial and spectral resolution of IKONOS-2 data and the data mining algorithms seems to be the better approach to accurately identify beach patterns through remotely sensed data.

Assessment of Potential Impacts in Tourism of the Increase in the Average Sea Level

Climate change and its effects are inevitable according to many authors; policies should be assumed regarding their mitigation and adaptation. Some economic sectors may suffer negative impacts, being tourism one with greater potential for impact. The increase in average sea level is one of the potential effects of climate change that can have consequences on tourism, particularly in the travel destinations that include coastal regions. The main objective of this work is to propose an approach for the assessment of potential impacts of the increase in the average sea level of tourism in a coastal area with a tripartite methodology. This methodology includes the assessment of physical vulnerability of the coast, including a coastal vulnerability index composed by nine physical variables – elevation, distance to shore, tide amplitude, significant wave weight, erosion/accretion rates, geology, geomorphology, ground cover vegetation and anthropogenic actions – followed by a quantification of coastal recession, based on the Bruun rule and the data of Special Report on Emissions Scenarios (SRES) developed by the Intergovernmental Panel on Climate Change (IPCC), on the rise in average sea level. Finally, it is estimated the total economic value of an area of recreation and tourism, based on the travel cost method. The proposed methodology was applied to a case study in the Portuguese coast, corresponding to the beach of Sao Jacinto, in Aveiro.