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Negli ultimi anni le agenzie umanitarie hanno ampliato il campo di utilizzo di immagini satellitari nella gestione delle emergenze - tipicamente impiegate a supporto della risposta a disastri naturali quali terremoti, alluvioni ed incendi - estendendolo alle crisi umanitarie. Il presente contributo è incentrato sull’analisi dell'evoluzione del campo di rifugiati siriani di Za'atari, istituito in territorio giordano nel luglio 2012 da parte della UN Refugee Agency (UNHCR). La metodologia di analisi multi-temporale è stata applicata su tre immagini satellitari ottiche ad alta risoluzione geometrica del campo profughi, elaborate mediante tecniche di classificazione assistita sia pixel-based sia object-based. L’elaborazione delle immagini è stata principalmente finalizzata all’analisi dell’evoluzione del campo ed alla stima della popolazione residente. Si è dimostrato come il telerilevamento satellitare possa fornire informazioni e dati dettagliati per la documentazione oggettiva a supporto della gestione di un'emergenza umanitaria.

Various regions in Europe have suffered from severe flooding over the last decades. Flood disasters often have a broad extent and a high frequency. They are considered the most devastating natural hazards because of the tremendous fatalities, injuries, property damages, economic and social disruption that they cause. In this context, Earth Observation techniques have become a key tool for flood risk and damage assessment. In particular, remote sensing facilitates flood surveying, providing valuable information, e.g. flood occurrence, intensity and progress of flood inundation, spurs and embankments affected/threatened. The present work aims to investigate the use of Very High Resolution satellite imagery for mapping flood-affected areas. The case study is the November 2013 flood event which occurred in Sardinia region (Italy), affecting a total of 2,700 people and killing 18 persons. The investigated zone extends for 28 km2 along the Posada river, from the Maccheronis dam to the mouth in the Tyrrhenian sea. A post-event SPOT6 image was processed by means of different classification methods, in order to produce the flood map of the analysed area. The unsupervised classification algorithm ISODATA was tested. A pixel-based supervised technique was applied using the Maximum Likelihood algorithm; moreover, the SPOT 6 image was processed by means of object-oriented approaches. The produced flood maps were compared among each other and with an independent data source, in order to evaluate the performance of each method, also in terms of time demand.

This work focuses on the exploitation of very high-resolution (VHR) satellite imagery coupled with multi-criteria analysis (MCA) to produce flood hazard maps. The methodology was tested over a portion of the Yialias river watershed basin (Nicosia, Cyprus). The MCA methodology was performed selecting five flood-conditioning factors: slope, distance to channels, drainage texture, geology and land cover. Among MCA methods, the analytic hierarchy process technique was chosen to derive the weight of each criterion in the computation of the flood hazard index (FHI). The required information layers were obtained by processing a VHR GeoEye-1 image and a digital elevation model. The satellite image was classified using an object-based technique to extract land use/cover data, while GIS geoprocessing of the DEM provided slope, stream network and drainage texture data. Using the FHI, the study area was finally classified into seven hazard categories ranging from very low to very high in order to generate an easily readable map. The hazard seems to be severe, in particular, in some urban areas, where extensive anthropogenic interventions can be observed. This work confirms the benefits of using remote sensing data coupled with MCA approach to provide fast and cost-effective information concerning the hazard assessment, especially when reliable data are not available.