Land use management in mountainous areas: combining ground-based and EO (Earth Observation) data to investigate the shallow landsliding susceptibility in the Duron valley (Trento, Italy).

Throughout the alpine domain, shallow landslides represent a serious geologic hazard, often causing severe damages to infrastructures, private properties, natural resources and in the most catastrophic events, threatening human lives. Landslides are a major factor of landscape evolution in mountainous and hilly regions and represent a critical issue for mountainous land management, since they cause loss of pastoral lands. In several alpine contexts, shallow landsliding distribution is strictly connected to the presence and condition of vegetation on the slopes. With the aid of high-resolution satellite images, it's possible to divide automatically the mountainous territory in land cover classes, which contribute with different magnitude to the stability of the slopes. The aim of this research is to combine EO (Earth Observation) land cover maps with ground-based measurements of the land cover properties. In order to achieve this goal, a new procedure has been developed to automatically detect grass mantle degradation patterns from satellite images. Moreover, innovative surveying techniques and instruments are tested to measure in situ the shear strength of grass mantle and the geomechanical and geotechnical properties of these alpine soils. Shallow landsliding distribution is assessed with the aid of physically based models, which use the EO-based map to distribute the resistance parameters across the landscape.

LCA-GIS Integrated approach for a Sustainable Land Use Under Energy Crops

In genere, negli studi di vocazionalità delle colture, vengono presi in considerazione solo variabili ambientali pedo-climatiche. La coltivazione di una coltura comporta anche un impatto ambientale derivante dalle pratiche agronomiche ed il territorio può essere più o meno sensibile a questi impatti in base alla sua vulnerabilità. In questo studio si vuole sviluppare una metodologia per relazionare spazialmente l’impatto delle colture con le caratteristiche sito specifiche del territorio in modo da considerare anche questo aspetto nell’allocazione negli studi di vocazionalità. LCA è stato utilizzato per quantificare diversi impatti di alcune colture erbacee alimentari e da energia, relazionati a mappe di vulnerabilità costruite con l’utilizzo di GIS, attraverso il calcolo di coefficienti di rischio di allocazione per ogni combinazione coltura-area vulnerabile. Le colture energetiche sono state considerate come un uso alternativo del suolo per diminuire l’impatto ambientale. Il caso studio ha mostrato che l’allocazione delle colture può essere diversa in base al tipo e al numero di impatti considerati. Il risultato sono delle mappe in cui sono riportate le distribuzioni ottimali delle colture al fine di minimizzare gli impatti, rispetto a mais e grano, due colture alimentari importanti nell’area di studio. Le colture con l’impatto più alto dovrebbero essere coltivate nelle aree a vulnerabilità bassa, e viceversa. Se il rischio ambientale è la priorità, mais, colza, grano, girasole, e sorgo da fibra dovrebbero essere coltivate solo nelle aree a vulnerabilità bassa o moderata, mentre, le colture energetiche erbacee perenni, come il panico, potrebbero essere coltivate anche nelle aree a vulnerabilità alta, rappresentando cosi una opportunità per aumentare la sostenibilità di uso del suolo rurale. Lo strumento LCA-GIS inoltre, integrato con mappe di uso attuale del suolo, può aiutare a valutarne il suo grado di sostenibilità ambientale.

Land use change to perennial energy crops in Northern Italy: Effects on soil organic carbon sequestration and distribution, soil enzyme activities and microbial communities.

Studies on soil organic carbon (SOC) sequestration in perennial energy crops are available for North-Central Europe, while there is insufficient information for Southern Europe. This research was conducted in the Po Valley, a Mediterranean-temperate zone characterised by low SOC levels, due to intensive management. The aim was to assess the factors influencing SOC sequestration and its distribution through depth and within soil fractions, after a 9-year old conversion from two annual systems to Miscanthus (Miscanthus × giganteus) and giant reed (Arundo donax). The 13C natural abundance was used to evaluate the amount of SOC in annual and perennial species, and determine the percentage of carbon derived from perennial crops. SOC was significantly higher under perennial species, especially in the topsoil (0-0.15 m). After 9 years, the amount of C derived from Miscanthus was 18.7 Mg ha-1, mostly stored at 0-0.15 m, whereas the amount of C derived from giant reed was 34.7 Mg ha-1, evenly distributed through layers. Physical soil fractionation was combined with 13C abundance analysis. C derived from perennial crops was mainly found in macroaggregates. Under giant reed, more newly derived-carbon was stored in microaggregates and mineral fraction than under Miscanthus. A molecular approach based on denaturing gradient gel electrophoresis (DGGE) allowed to evaluate changes on microbial community, after the introduction of perennial crops. Functional aspects were investigated by determining relevant soil enzymes (β-glucosidase, urease, alkaline phosphatase). Perennial crops positively stimulated these enzymes, especially in the topsoil. DGGE profiles revealed that community richness was higher in perennial crops; Shannon index of diversity was influenced only by depth. In conclusion, Miscanthus and giant reed represent a sustainable choice for the recovery of soils exhausted by intensive management, also in Mediterranean conditions and this is relevant mainly because this geographical area is notoriously characterised by a rapid turnover of SOC.

Quality assessment of a landslide inventory map and its application to land‐use planning. A case study in the Northern Apennines (Emilia‐Romagna region, Italy)

Landslide hazard and risk are growing as a consequence of climate change and demographic pressure. Land‐use planning represents a powerful tool to manage this socio‐economic problem and build sustainable and landslide resilient communities. Landslide inventory maps are a cornerstone of land‐use planning and, consequently, their quality assessment represents a burning issue. This work aimed to define the quality parameters of a landslide inventory and assess its spatial and temporal accuracy with regard to its possible applications to land‐use planning. In this sense, I proceeded according to a two‐steps approach. An overall assessment of the accuracy of data geographic positioning was performed on four case study sites located in the Italian Northern Apennines. The quantification of the overall spatial and temporal accuracy, instead, focused on the Dorgola Valley (Province of Reggio Emilia). The assessment of spatial accuracy involved a comparison between remotely sensed and field survey data, as well as an innovative fuzzylike analysis of a multi‐temporal landslide inventory map. Conversely, long‐ and short‐term landslide temporal persistence was appraised over a period of 60 years with the aid of 18 remotely sensed image sets. These results were eventually compared with the current Territorial Plan for Provincial Coordination (PTCP) of the Province of Reggio Emilia. The outcome of this work suggested that geomorphologically detected and mapped landslides are a significant approximation of a more complex reality. In order to convey to the end‐users this intrinsic uncertainty, a new form of cartographic representation is needed. In this sense, a fuzzy raster landslide map may be an option. With regard to land‐use planning, landslide inventory maps, if appropriately updated, confirmed to be essential decision‐support tools. This research, however, proved that their spatial and temporal uncertainty discourages any direct use as zoning maps, especially when zoning itself is associated to statutory or advisory regulations.

Modelizzazione spazio-temporale delle dinamiche di uso del suolo ed analisi comparativa di differenti approcci predittivi. Uso integrato di SIG e telerilevamento nello studio dei processi di deforestazione nella regione La Joyanca in Peten, Guatemala

L’uso frequente dei modelli predittivi per l’analisi di sistemi complessi, naturali o artificiali, sta cambiando il tradizionale approccio alle problematiche ambientali e di rischio. Il continuo miglioramento delle capacità di elaborazione dei computer facilita l’utilizzo e la risoluzione di metodi numerici basati su una discretizzazione spazio-temporale che permette una modellizzazione predittiva di sistemi reali complessi, riproducendo l’evoluzione dei loro patterns spaziali ed calcolando il grado di precisione della simulazione. In questa tesi presentiamo una applicazione di differenti metodi predittivi (Geomatico, Reti Neurali, Land Cover Modeler e Dinamica EGO) in un’area test del Petén, Guatemala. Durante gli ultimi decenni questa regione, inclusa nella Riserva di Biosfera Maya, ha conosciuto una rapida crescita demografica ed un’incontrollata pressione sulle sue risorse naturali. L’area test puó essere suddivisa in sotto-regioni caratterizzate da differenti dinamiche di uso del suolo. Comprendere e quantificare queste differenze permette una migliore approssimazione del sistema reale; é inoltre necessario integrare tutti i parametri fisici e socio-economici, per una rappresentazione più completa della complessità dell’impatto antropico. Data l’assenza di informazioni dettagliate sull’area di studio, quasi tutti i dati sono stati ricavati dall’elaborazione di 11 immagini ETM+, TM e SPOT; abbiamo poi realizzato un’analisi multitemporale dei cambi uso del suolo passati e costruito l’input per alimentare i modelli predittivi. I dati del 1998 e 2000 sono stati usati per la fase di calibrazione per simulare i cambiamenti nella copertura terrestre del 2003, scelta come data di riferimento per la validazione dei risultati. Quest’ultima permette di evidenziare le qualità ed i limiti per ogni modello nelle differenti sub-regioni.

Environmental Assessment of the Agricultural Sector

One of the main problems recognized in sustainable development goals and sustainable agricultural objectives is Climate change. Farming contributes significantly to the overall Greenhouse gases (GHG) in the atmosphere, which is approximately 10-12 percent of total GHG emissions, but when taking in consideration also land-use change, including deforestation driven by agricultural expansion for food, fiber and fuel the number rises to approximately 30 percent (Smith et. al., 2007). There are two distinct methodological approaches for environmental impact assessment; Life Cycle Assessment (a bottom up approach) and Input-Output Analysis (a top down approach). The two methodologies differ significantly but there is not an immediate choice between them if the scope of the study is on a sectorial level. Instead, as an alternative, hybrid approaches which combine these two approaches have emerged. The aim of this study is to analyze in a greater detail the agricultural sectors contribution to Climate change caused by the consumption of food products. Hence, to identify the food products that have the greatest impact through their life cycle, identifying their hotspots and evaluating the mitigation possibilities for the same. At the same time evaluating methodological possibilities and models to be applied for this purpose both on a EU level and on a country level (Italy).

Effect of different management intensities on natural and human-induced carbon fluxes in a golf course in northern Italy

Turfgrasses are ubiquitous in urban landscape and their role on carbon (C) cycle is increasing important also due to the considerable footprint related to their management practices. It is crucial to understand the mechanisms driving the C assimilation potential of these terrestrial ecosystems Several approaches have been proposed to assess C dynamics: micro-meteorological methods, small-chamber enclosure system (SC), chrono-sequence approach and various models. Natural and human-induced variables influence turfgrasses C fluxes. Species composition, environmental conditions, site characteristics, former land use and agronomic management are the most important factors considered in literature driving C sequestration potential. At the same time different approaches seem to influence C budget estimates. In order to study the effect of different management intensities on turfgrass, we estimated net ecosystem exchange (NEE) through a SC approach in a hole of a golf course in the province of Verona (Italy) for one year. The SC approach presented several advantages but also limits related to the measurement frequency, timing and duration overtime, and to the methodological errors connected to the measuring system. Daily CO2 fluxes changed according to the intensity of maintenance, likely due to different inputs and disturbances affecting biogeochemical cycles, combined also to the different leaf area index (LAI). The annual cumulative NEE decreased with the increase of the intensity of management. NEE was related to the seasonality of turfgrass, following temperatures and physiological activity. Generally on the growing season CO2 fluxes towards atmosphere exceeded C sequestered. The cumulative NEE showed a system near to a steady state for C dynamics. In the final part greenhouse gases (GHGs) emissions due to fossil fuel consumption for turfgrass upkeep were estimated, pinpointing that turfgrass may result a considerable C source. The C potential of trees and shrubs needs to be considered to obtain a complete budget.

A Web GIS Decision Support System For Coastal Risk Assessment and Mitigation Planning

Coastal flooding poses serious threats to coastal areas around the world, billions of dollars in damage to property and infrastructure, and threatens the lives of millions of people. Therefore, disaster management and risk assessment aims at detecting vulnerability and capacities in order to reduce coastal flood disaster risk. In particular, non-specialized researchers, emergency management personnel, and land use planners require an accurate, inexpensive method to determine and map risk associated with storm surge events and long-term sea level rise associated with climate change. This study contributes to the spatially evaluation and mapping of social-economic-environmental vulnerability and risk at sub-national scale through the development of appropriate tools and methods successfully embedded in a Web-GIS Decision Support System. A new set of raster-based models were studied and developed in order to be easily implemented in the Web-GIS framework with the purpose to quickly assess and map flood hazards characteristics, damage and vulnerability in a Multi-criteria approach. The Web-GIS DSS is developed recurring to open source software and programming language and its main peculiarity is to be available and usable by coastal managers and land use planners without requiring high scientific background in hydraulic engineering. The effectiveness of the system in the coastal risk assessment is evaluated trough its application to a real case study.