Agricultural terraces represent one of the best ways to prevent land degradation in hilly and mountainous landscapes. However, it is widely recognized that terraced slopes are threatened by agricultural land abandonment. In the literature, very few studies have quantitatively examined the influence of agricultural abandonment on the stability of terraced slopes. The goal of this research is to investigate the relationships between landslide magnitude and land use conditions of agricultural terraced slopes. In particular, Light Detection And Ranging (LiDAR) elevation data, coupled with aerial photo interpretation, were used for the computation of shallow landslide mobilized volumes on terraced slopes affected by an intense rainfall event. We performed the analysis within the Vernazza basin, a small Mediterranean coastal catchment located in the “Cinque Terre” area (Liguria, northwestern Italy), comparing pre‐event and post‐event LiDAR datasets. The results revealed that abandoned terraced slopes have been affected by a higher amount of mobilized debris volumes than still‐cultivated terraces. Furthermore, terraces abandoned for a short time (less than 25–30 years) resulted in the most hazardous land use class, showing erosion rates that were approximately 2 and 3 times higher than terraced slopes abandoned a long time ago (more than 25–30 years) and still‐cultivated terraces, respectively. These findings highlight that land abandonment and agricultural mismanagement can intensify the magnitude of rainfall‐induced shallow landslides. Copyright © 2016 John Wiley & Sons, Ltd.

Accurate flood mapping is important for both planning activity during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this work, a Bayesian Network (BN) is proposed to integrate remotely sensed data, such as multi-temporal SAR intensity images and InSAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%

Periodic monitoring of biodiversity changes at a landscape scale constitutes a key issue for conservation managers. Earth observation (EO) data offer a potential solution, through direct or indirect mapping of species or habitats. Most national and international programs rely on the use of land cover (LC) and/or land use (LU) classification systems. Yet, these are not as clearly relatable to biodiversity in comparison to habitat classifications, and provide less scope for monitoring. While a conversion from LC/LU classification to habitat classification can be of great utility, differences in definitions and criteria have so far limited the establishment of a unified approach for such translation between these two classification systems.Focusing on five Mediterranean NATURA 2000 sites, this paper considers the scope for three of the most commonly used global LC/LU taxonomies-CORINE Land Cover, the Food and Agricultural Organisation (FAO) land cover classification system (LCCS) and the International Geosphere-Biosphere Programme to be translated to habitat taxonomies. Through both quantitative and expert knowledge based qualitative analysis of selected taxonomies, FAO-LCCS turns out to be the best candidate to cope with the complexity of habitat description and provides a framework for EO and in situ data integration for habitat mapping, reducing uncertainties and class overlaps and bridging the gap between LC/LU and habitats domains for 123Landscape Ecol (2013) 28:905-930 DOI 10.1007 landscape monitoring-a major issue for conservation. This study also highlights the need to modify the FAO-LCCS hierarchical class description process to permit the addition of attributes based on class-specific expert knowledge to select multi-temporal (seasonal) EO data and improve classification. An application of LC/LU to habitat mapping is provided for a coastal Natura 2000 site with high classification accuracy as a result.

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