Assessment of environment, land management, and spatial variables on recent changes in montado land cover in southern Portugal

Montado decline has been reported since the end of the nineteenth century in southern Portugal and increased markedly during the 1980s. Consensual reports in the literature suggest that this decline is due to a number of factors, such as environmental constraints, forest diseases, inappropriate management, and socioeconomic issues. An assessment on the pattern of montado distribution was conducted to reveal how the extent of land management, environmental variables, and spatial factors contributed to montado area loss in southern Portugal from 1990 to 2006. A total of 14 independent variables, presumably related to montado loss, were grouped into three sets: environmental variables, land management variables, and spatial variables. From 1990 to 2006, approximately 90,054 ha disappeared in the montado area, with an estimated annual regression rate of 0.14 % year-1. Variation partitioning showed that the land management model accounted for the highest percentage of explained variance (51.8 %), followed by spatial factors (44.6 %) and environmental factors (35.5 %). These results indicate that most variance in the large-scale distribution of recent montado loss is due to land management, either alone or in combination with environmental and spatial factors. The full GAM model showed that different livestock grazing is one of the most important variables affecting montado loss. This suggests that optimum carrying capacity should decrease to 0.18–0.60 LU ha-1 for livestock grazing in montado under current ecological conditions in southern Portugal.

The science of assessment : identifying and predicting environmental impacts

The historical challenge of environmental impact assessment (EIA) has been to predict project-based impacts accurately. Both EIA legislation and the practice of EIA have evolved over the last three decades in Canada, and the development of the discipline and science of environmental assessment has improved how we apply environmental assessment to complex projects. The practice of environmental assessment integrates the social and natural sciences and relies on an eclectic knowledge base from a wide range of sources. EIA methods and tools provide a means to structure and integrate knowledge in order to evaluate and predict environmental impacts.----- This Chapter will provide a brief overview of how impacts are identified and predicted. How do we determine what aspect of the natural and social environment will be affected when a mine is excavated? How does the practitioner determine the range of potential impacts, assess whether they are significant, and predict the consequences? There are no standard answers to these questions, but there are established methods to provide a foundation for scoping and predicting the potential impacts of a project.----- Of course, the community and publics play an important role in this process, and this will be discussed in subsequent chapters. In the first part of this chapter, we will deal with impact identification, which involves appplying scoping to critical issues and determining impact significance, baseline ecosystem evaluation techniques, and how to communicate environmental impacts. In the second part of the chapter, we discuss the prediction of impacts in relation to the complexity of the environment, ecological risk assessment, and modelling.

Review of vibration-based damage detection and condition assessment of bridge structures using structural health monitoring

As a part of vital infrastructure and transportation networks, bridge structures must function safely at all times. However, due to heavier and faster moving vehicular loads and function adjustment, such as Busway accommodation, many bridges are now operating at an overload beyond their design capacity. Additionally, the huge renovation and replacement costs always make the infrastructure owners difficult to undertake. Structural health monitoring (SHM) is set to assess condition and foresee probable failures of designated bridge(s), so as to monitor the structural health of the bridges. The SHM systems proposed recently are incorporated with Vibration-Based Damage Detection (VBDD) techniques, Statistical Methods and Signal processing techniques and have been regarded as efficient and economical ways to solve the problem. The recent development in damage detection and condition assessment techniques based on VBDD and statistical methods are reviewed. The VBDD methods based on changes in natural frequencies, curvature/strain modes, modal strain energy (MSE) dynamic flexibility, artificial neural networks (ANN) before and after damage and other signal processing methods like Wavelet techniques and empirical mode decomposition (EMD) / Hilbert spectrum methods are discussed here.