L’industrializzazione dell’agricoltura nella Piana del Sele: una prospettiva geografica basata sull’Urban Atlas Copernicus

Il contributo nasce da un approccio interdisciplinare allo studio delle tra-sformazioni territoriali: ne sono autori, infatti, geografi, fisici e ingegneri ambientali esperti di telerilevamento. L’obiettivo è quello di studiare la recente espansione del capitalismo agricolo in una regione strategica per l’economia del Mezzogiorno: la Piana del Sele. In pochi anni, questo territorio a forte vocazione agri-cola ha conosciuto una drastica alterazione della copertura del suolo, che, se nel 2012 registrava un sostanziale equilibrio tra seminativi e serre, nel 2018, in appena sei anni, ha visto una crescita ponderosa delle serre a scapito proprio dei seminativi. È questa la conseguen-za dell’espansione della cosiddetta “quarta gamma” nel mercato or-tofrutticolo - esplosa anche in Italia nelle ultime due decadi – ovvero la produzione in serra di prodotti freschi, lavati e pronti al consumo. Si tratta di un fenomeno che, oltre alla valenza commerciale, ha dei riverberi geo-economici, sociali e ambientali di prim’ordine. In primo luogo, la penetrazione nei mercati locali dei grandi capitalisti agricoli; in secondo luogo, la riduzione della capacità degli ecosistemi di assi-curare beni e servizi; in terzo luogo, il depauperamento del paesaggio e il potenziale aumento del rischio idrogeologico. La metodologia d’indagine prevede l’analisi dei cambiamenti del-la land cover nei comuni di Battipaglia e Bellizzi, nella provincia di Salerno, nel segmento temporale 2012-2018 attraverso l’Urban Atlas Copernicus, realizzato utilizzando dati satellitari ad altissima risolu-zione. Attraverso il software InVEST, ai cambiamenti osservati è stata abbinata la quantificazione della perdita generata dalla riduzione dei beni e servizi ecosistemici.ritorial transformations: in fact, its authors are geographers, physicists and environmental engineers who are experts in remote sensing. The aim is to study the recent expansion of agricultural capitalism in a strategic region for the economy of the South: the Sele Plain. In just a few years, this territory with a strong agricultural vocation has experienced a drastic alteration of the land cover: in 2012 there was a substantial bal-ance between arable land and greenhouses, but in 2018, in just six years, there was a substantial growth of greenhouses own detriment of arable land. This is the consequence of the expansion of the so-called “fourth range” in the fruit and vegetable market - which has also grown up in Italy in the last two decades - or rather the greenhouse production of fresh, washed and ready-to-eat products. It is a phenomenon which, in addition to its commercial value, has first-rate geo-economic, social and environmental effects. First, the penetration of local markets by large ag-ricultural capitalists; secondly, the reduction of the ability of ecosystems to provide goods and services; thirdly, the depletion of the landscape and the potential increase in hydrogeological risk. The survey methodology involves the analysis of land cover changes in the municipalities of Battipaglia and Bellizzi, in the province of Salerno, in the 2012-2018 time span through the Urban Atlas Copernicus, created using very high resolution satellite data. Through the InVEST software, the observed changes were combined with the quantification of the loss generated by the reduction of the ecosystem goods and services

100-year flood susceptibility maps for the continental U.S. derived with a geomorphic method

Binary raster dataset (.txt format) containing flood susceptibility maps related to 100-year river floods occurring in the continental U.S. These mapping products were derived through terrain analysis and a technique of pattern classification performed on DEMs obtained from HydroSHEDS (http://hydrosheds.cr.usgs.gov/overview.php) with a 3 arc-second resolution (0.00083333 degree, approximatively 90 m at the equator). Specifically, the flood-prone areas were identified by applying a linear binary classifier based upon the Geomorphic Flood Index (Manfreda et al., 2015; Samela et al., 2015; Samela et al., 2016 ). The raster maps have a 90 m resolution and are geo-referenced. The coordinate system of the maps is UTM (Universal Transverse Mercator) Zone 17N, the projection is Transverse Mercator, and the geodetic system is NAD (North American Datum) 1983. To simplify the management and the use of the data, the continental U.S. was divided into eighteen major water resources regions according to the hydrologic units identified by the United States Geological Survey

Geomorphic classifiers for flood-prone areas delineation for data-scarce environments

Knowing the location and the extent of the areas exposed to flood hazards is essential to any strategy for minimizing the risk. Unfortunately, in ungauged basins the use of traditional floodplain mapping techniques is prevented by the lack of the extensive data required. The present work aims to overcome this limitation by defining an alternative simplified procedure for a preliminary floodplain delineation based on the use of geomorphic classifiers. To validate the method in a data-rich environment, eleven flood-related morphological descriptors derived from remotely sensed elevation data have been used as linear binary classifiers over the Ohio River basin and its sub-catchments. Their performances have been measured at the change of the topography and the size of the calibration area, allowing to explore the transferability of the calibrated parameters, and to define the minimum extent of the calibration area. The best performing classifiers among those analysed have been applied and validated across the continental U.S. The results suggest that the classifier based on the Geomorphic Flood Index (GFI), is the most suitable to detect the flood-prone areas in data-scarce regions and for large-scale applications, providing good accuracies with low requirements in terms of data and computational costs. This index is defined as the logarithm of the ratio between the water depth in the element of the river network closest to the point under exam (estimated using a hydraulic scaling function based on contributing area) and the elevation difference between these two points

Dataset of 100-year flood susceptibility maps for the continental U.S. derived with a geomorphic method

Efficient strategies for preparing communities to protect against, respond to, recover from, and mitigate flood hazard are often hampered by the lack of information about the position and extent of flood-prone areas. Hydrologic and hydraulic analyses allow to obtain detailed flood hazard maps, but are a computationally intensive exercise requiring a significant amount of input data, which are rarely available both in developing and developed countries. As a consequence, even in data-rich environments, official flood hazard graduations are often affected by extensive gaps. In the U.S., for instance, the detailed floodplain delineation produced by the Federal Emergency Management Agency (FEMA) is incomplete, with many counties having no floodplain mapping at all. In this article we present a mapping dataset containing 100-year flood susceptibility maps for the continental U.S. with a 90 m resolution. They have been obtained performing a linear binary classification based on the Geomorphic Flood Index (GFI). To the best knowledge of the authors, there are no available flood-prone areas maps for the entire continental U.S. with resolution lower that 30׳׳×30׳׳ (approximatively 1 km at the equator)

Geomorphic classifiers for flood-prone areas delineation for data-scarce environments

Knowing the location and the extent of the areas exposed to flood hazards is essential to any strategy for minimizing the risk. Unfortunately, in ungauged basins the use of traditional floodplain mapping techniques is prevented by the lack of the extensive data required. The present work aims to overcome this limitation by defining an alternative simplified procedure for a preliminary floodplain delineation based on the use of geomorphic classifiers. To validate the method in a data-rich environment, eleven flood-related morphological descriptors derived from remotely sensed elevation data have been used as linear binary classifiers over the Ohio River basin and its sub-catchments. Their performances have been measured at the change of the topography and the size of the calibration area, allowing to explore the transferability of the calibrated parameters, and to define the minimum extent of the calibration area. The best performing classifiers among those analysed have been applied and validated across the continental U.S. The results suggest that the classifier based on the Geomorphic Flood Index (GFI), is the most suitable to detect the flood-prone areas in data-scarce regions and for large-scale applications, providing good accuracies with low requirements in terms of data and computational costs. This index is defined as the logarithm of the ratio between the water depth in the element of the river network closest to the point under exam (estimated using a hydraulic scaling function based on contributing area) and the elevation difference between these two points

Flood-Prone Areas Assessment Using Linear Binary Classifiers based on Morphological Indices

The identification of flood-prone areas is a critical issue becoming everyday more pressing for our society. A preliminary delineation can be carried out by DEM-based procedures that relay on basin geomorphologic features. In the present paper, we investigated the dominant topographic controls for the flood exposure using techniques of pattern classification through linear binary classifiers based on DEM derived morphologic features. With this aim, local features - which are generally used to describe the hydrological characteristics of a basin - and composite morphological indices are taken into account in order to identify the most significant one. The analyses highlight the potential of each morphological descriptor for the identification of the extend of flood-prone areas. Our findings may help the definition of new strategies for the delineation of flood-prone areas with DEM-based procedures

A GIS tool for cost-effective delineation of flood-prone areas

Delineation of flood hazard and flood risk areas is a critical issue, but practical difficulties regularly make complete achievement of the task a challenge. In data-scarce environments (e.g. ungauged basins, large-scale analyses), useful information about flood hazard exposure can be obtained using geomorphic methods. In order to advance this field of research, we implemented in the QGIS environment an automated DEM-based procedure that exhibited high accuracy and reliability in identifying the flood-prone areas in several test sites located in Europe, the United States and Africa. This tool, named Geomorphic Flood Area tool (GFA tool), enables rapid and cost-effective flood mapping by performing a linear binary classification based on the recently proposed Geomorphic Flood Index (GFI). The GFA tool provides a user-friendly strategy to map flood exposure over large areas. A demonstrative application of the GFA tool is presented in which a detailed flood map was derived for Romania

Advances in Large-Scale Flood Monitoring and Detection

The last decades have seen a massive advance in technologies for Earth observation (EO) and environmental monitoring, which provided scientists and engineers with valuable spatial information for studying hydrologic processes. At the same time, the power of computers and newly developed algorithms have grown sharply. Such advances have extended the range of possibilities for hydrologists, who are trying to exploit these potentials the most, updating and re-inventing the way hydrologic and hydraulic analyses are carried out. A variety of research fields have progressed significantly, ranging from the evaluation of water features, to the classification of land-cover, the identification of river morphology, and the monitoring of extreme flood events. The description of flood processes may particularly benefit from the integrated use of recent algorithms and monitoring techniques. In fact, flood exposure and risk over large areas and in scarce data environments have always been challenging topics due to the limited information available on river basin hydrology, basin morphology, land cover, and the resulting model uncertainty. The ability of new tools to carry out intensive analyses over huge datasets allows us to produce flood studies over large extents and with a growing level of detail. The present Special Issue aims to describe the state-of-the-art on flood assessment, monitoring, and management using new algorithms, new measurement systems and EO data. More specifically, we collected a number of contributions dealing with: (1) the impact of climate change on floods; (2) real time flood forecasting systems; (3) applications of EO data for hazard, vulnerability, risk mapping, and post-disaster recovery phase; and (4) development of tools and platforms for assessment and validation of hazard/risk models