Downscaling landsat land surface temperature over the urban area of Florence

A new downscaling algorithm for land surface temperature (LST) images retrieved from Landsat Thematic Mapper (TM) was developed over the city of Florence and the results assessed against a high-resolution aerial image. The Landsat TM thermal band has a spatial resolution of 120 m, resampled at 30 m by the US Geological Survey (USGS) agency, whilst the airborne ground spatial resolution was 1 m. Substantial differences between Landsat USGS and airborne thermal data were observed on a 30 m grid: therefore a new statistical downscaling method at 30 m was developed. The overall root mean square error with respect to aircraft data improved from 3.3 °C (USGS) to 3.0 °C with the new method, that also showed better results with respect to other regressive downscaling techniques frequently used in literature. Such improvements can be ascribed to the selection of independent variables capable of representing the heterogeneous urban landscape

Chapter Employability-Oriented Curriculum: Strategies and Tools to Train Young Graduates. The PRIN EMP&Co. Project

This paper focuses on the impact of employability-oriented modules on the design, planning, and implementation of work transitions. It takes its lead from the PRIN EMP&Co project developed by the University of Florence in 2014-2017, and how its research protocol allows a mapping of the construction of employability during the Master’s Degree Cours

Waltz with development:insights on the developmentalization of climate-induced migration

The idea of migration as an adaptation strategy has gained traction in the debates on climate change and mobility. It emphasises migrants’(economic) agency and praises remittances as source of funding for household and community resilience. The environmental determinism of the previously dominant narratives on ‘climate refugees’ gives way to more accurate understanding of how environmental conditions interact with migration processes, thereby facilitating a convinced engagement by the migration and development communities. This article interrogates the discourses on migration as adaptation through the long-standing ‘migration and development’ debates. We show that, despite their aura of novelty within climate policy, the ‘new’ discourses build on ‘old’ foundations – i.e. the optimistic swings of the “migration anddevelopment pendulum” (de Haas 2012). Moreover, the ‘migration as adaptation’ thesis has not come with a deeper engagement with the structural inequalities that (re)produce socio-ecological vulnerabilities, impeding the mobility of some while forcing others into displacement. Rather, it mirrors the neoliberal version of the classical optimist take on the migration-development nexus, through which mainstream international agendas have tried to foster development and discipline mobility in the last few decades. The extent to which this proves a positive turn in climate (migration) policy is up to debate

Sensible and latent heat flux from radiometric surface temperatures at the regional scale: methodology and validation

The CarboEurope Regional Experiment Strategy (CERES) was designed to develop and test a range of methodologies to assess regional surface energy and mass exchange of a large study area in the south-western part of France. This paper describes a methodology to estimate sensible and latent heat fluxes on the basis of net radiation, surface radiometric temperature measurements and information obtained from available products derived from the Meteosat Second Generation (MSG) geostationary meteorological satellite, weather stations and ground-based eddy covariance towers. It is based on a simplified bulk formulation of sensible heat flux that considers the degree of coupling between the vegetation and the atmosphere and estimates latent heat as the residual term of net radiation. Estimates of regional energy fluxes obtained in this way are validated at the regional scale by means of a comparison with direct flux measurements made by airborne eddy-covariance. The results show an overall good matching between airborne fluxes and estimates of sensible and latent heat flux obtained from radiometric surface temperatures that holds for different weather conditions and different land use types. The overall applicability of the proposed methodology to regional studies is discusse

Increased CO₂ loss from vegetated drained lake tundra ecosystems due to flooding

Tundra ecosystems are especially sensitive to climate change, which is particularly rapid in high northern latitudes resulting in significant alterations in temperature and soil moisture. Numerous studies have demonstrated that soil drying increases the respiration loss from wet Arctic tundra. And, warming and drying of tundra soils are assumed to increase CO2 emissions from the Arctic. However, in this water table manipulation experiment (i.e., flooding experiment), we show that flooding of wet tundra can also lead to increased CO2 loss. Standing water increased heat conduction into the soil, leading to higher soil temperature, deeper thaw and, surprisingly, to higher CO2 loss in the most anaerobic of the experimental areas. The study site is located in a drained lake basin, and the soils are characterized by wetter conditions than upland tundra. In experimentally flooded areas, high wind speeds (greater than ~4 m s−1) increased CO2 emission rates, sometimes overwhelming the photosynthetic uptake, even during daytime. This suggests that CO2 efflux from C rich soils and surface waters can be limited by surface exchange processes. The comparison of the CO2 and CH4 emission in an anaerobic soil incubation experiment showed that in this ecosystem, CO2 production is an order of magnitude higher than CH4 production. Future increases in surface water ponding, linked to surface subsidence and thermokarst erosion, and concomitant increases in soil warming, can increase net C efflux from these arctic ecosystems

Analysing student comments on RateMyProfessors.com using NLP techniques

The assessment of teaching methods and faculty performance is an important step enabling educational institutions to continuously improve their teaching methods and study offers. Typically, schools conduct internal surveys to assess their performance. In most cases, however, the results of these surveys are not disclosed to the public. Therefore, several online platforms have emerged, which allow students to evaluate their teachers publicly. The most popular online evaluation platform, RateMyProfessors.com, currently features over 15 million evaluations covering more than 1.8 million teachers. So, how can schools use this large amount of publicly available data to generate useful insights? In order to answer this research question, a dataset containing 1,637,435 evaluations for 134,375 teachers from 605 schools selected with a random approach using web scraping techniques was built. Intermediate questions were defined in order to answer the research question, such as whether it is possible to use computational techniques to distinguish good from bad teachers based on the language used by the students. The individual questions were elaborated and answered using theoretical knowledge and statistical models. Using natural language processing and machine learning techniques it was demonstrated that it is possible to distinguish positive evaluations from negative evaluations, easy subjects from difficult subjects as well as good teachers from bad teachers with accuracies of over 90%. Furthermore, thanks to the correlations discovered between the quality of teaching as perceived by students, the level of difficulty as perceived by students and the helpfulness of the teacher, it was possible to predict the quality of teaching and the level of difficulty based on the students language. Finally, it was demonstrated that, using statistical models, it is possible to identify topics concerning the faculty performance and teaching methods in evaluations of online courses. Although random approaches to data collection have been chosen to allow the results to be generalized, this cannot be considered universally valid, as the platform from which the data was extracted offers the possibility to evaluate only institutes in the United States, Canada and the United Kingdom. It is therefore necessary to consider possible differences in the way teachers in other cultures are evaluated. In conclusion, natural language processing and machine learning techniques can be applied for the analysis of online evaluations. Schools can therefore use these techniques to generate useful information about their teachers and their teachers’ performance based on online evaluations. This approach, however, should not be looked at by schools as an alternative to the typical evaluation activity, but as an extension, allowing them to analyze aspects not normally considered in typical school evaluations

Introduzione

Introduzione al volume, numero monografico della rivista "Nuovi Studi Livornesi" n. I/II 2019, ISBN:1591-777

Migration and global environmental change: methodological lessons from mountain areas of the global South

The relationship between migration and environmental and climatic changes is a crucial yet understudied factor influencing mountain livelihoods in the global South. These livelihoods are often characterized by high prevalence of family farming, widespread dependence on natural resources, and high sensitivity to climatic changes. Except for a limited number of empirical case studies, the literature on migration and global environmental change has not yet moved beyond case study results to address and explain global patterns and specificities of migration in mountain areas of the global South. After an introduction to the topic, the authors present a new synthesis of three field studies combining household surveys, participatory research approach (PRA) tools and key informant interviews in Pakistan, Peru, and Tanzania. This article suggests that the systematic use of transdisciplinary approaches, with a combination of quantitative and qualitative empirical methods, is the key to understanding global migration patterns in rural mountain areas of the global South. The results of our synthesis suggests that survey data should be triangulated with PRA results as well as secondary data in order to build household profiles connecting vulnerability (measured through a multidimensional index) with human mobility patterns. Such profiles can be conducive to better understand the feedback processes between livelihoods and mobility patterns both within each case study and across case studies, helping researchers to draw general lessons