We propose a methodology for flood mapping by remote sensing considering the alteration of the spectral response of water due to turbidity conditions using principal component analysis (PCA) of multitemporal satellite imagery limited to the visible and the near IR region of the electromagnetic spectrum. Crosta technique is applied to the resulting load matrix to select the PCs of interest, and to interpret the nature of the changes. Finally, it is proposed the use of linear unmixing, using the selected PCs as input, to classify the categories required for flood mapping. The proposed methodology is applied to mapping the flood areas after the tropical storm Manuel during September 2013 in Acapulco, Mexico. The proposed procedure is a more robust alternative to quotients or index-orientated approaches based on fixed spectral response of the water, e.g. the Normalized Difference Water Index. This approach can be useful to authorities, civil protection and other organizations dedicated to risk management during natural contingences to assess quickly the dimension of affected areas, without the expensive and complicated mobilization of recourses to the site, and to give a more efficient response.

Cash crops are agricultural crops intended to be sold for profit as opposed to subsistence crops, meant to support the producer, or to support livestock. Since cash crops are intended for future sale, they translate into large financial value when considered on a wide geographical scale, so their production directly involves financial risk. At a national level, extreme weather events including destructive rain or hail, as well as drought, can have a significant impact on the overall economic balance. It is thus important to map such crops in order to set up insurance and mitigation strategies. Using locally generated data—such as municipality-level records of crop seeding—for mapping purposes implies facing a series of issues like data availability, quality, homogeneity, etc. We thus opted for a different approach relying on global datasets. Global datasets ensure homogeneity and availability of data, although sometimes at the expense of precision and accuracy. A typical global approach makes use of spaceborne remote sensing, for which different land cover classification strategies are available in literature at different levels of cost and accuracy. We selected the optimal strategy in the perspective of a global processing chain. Thanks to a specifically developed strategy for fusing unsupervised classification results with environmental constraints and other geospatial inputs including ground-based data, we managed to obtain good classification results despite the constraints placed. The overall production process was composed using “good-enough" algorithms at each step, ensuring that the precision, accuracy, and data-hunger of each algorithm was commensurate to the precision, accuracy, and amount of data available. This paper describes the tailored strategy developed on the occasion as a cooperation among different groups with diverse backgrounds, a strategy which is believed to be profitably reusable in other, similar contexts. The paper presents the problem, the constraints and the adopted solutions; it then summarizes the main findings including that efforts and costs can be saved on the side of Earth Observation data processing when additional ground-based data are available to support the mapping task.

An event-based approach for probabilistic flood risk assessment for financial and civil protection applications is proposed. The risk parameters are evaluated in an event-based probabilistic framework for a set of hazard events and then all these results are probabilistically integrated, including, in a formal way, all uncertainties related to each and every part of the process. The hazard is defined as a stochastic or historic set of events, collectively exhaustive and mutually exclusive, that describes the spatial distribution, the annual frequency, and the randomness of the hazard intensity. The risk is expressed in different economic terms: the average annual loss, the pure risk premium, and the loss exceedance curve. As an illustrative example, this approach is applied to Acapulco city located on the Pacific coast of Mexico. These metrics are of particular importance for risk retention (financing) schemes or risk transfer instruments, and, therefore, they will be a particularly valuable contribution to further studies in defining strategies for financial protection against disasters. With these results, further results may be obtained for civil protection and land use applications.

Two closed-form mathematical expressions are presented for the evaluation of the mean annual failure rate of deteriorating structures at the end of a given time interval. It is assumed that the structural capacity deteriorates linearly in time in both expressions. The assumption that the structural demand varies as a function of time is added to one of them. The approach is based on the probabilistic seismic demand hazard analysis used in SAC-FEMA project. The expressions are applied to a 10-storey building. The difference in expected number of failures after 150 years of constructing the building results about 16.3% higher with respect to the case when the structural deterioration is neglected. Attention is called to the importance of considering not only the time variation of the structural capacity, but also that of the structural demand in the evaluation of time-dependent failure probabilities of deteriorating structures.