Plucker forms and the theta map

In this paper we introduce the elementary notion of Pl\"ucker form of a pair $(E,S)$, where $E$ is a vector bundle of rank $r$ on a smooth, irreducible, complex projective variety $X$ and $S \subset H^0(E)$ is a subspace of dimension $rm$. We apply this notion to the study of theta map $\theta_r$ on the moduli space $SU_X(r,0)$ of semistable vector bundles of rank $r$ and trivial determinant on a curve $X$ of genus $g$. We prove that $\theta_r$ is generically injective if $X$ is general and $g >> r$.Comment: 20 pages to appear on "American Journal of Mathematics

Seasonality of MODIS LST over Southern Italy and correlation with land cover, topography and solar radiation

AbstractLand Surface Temperature (LST) is a key variable in the interactions and energy fluxes between the Earth surface and the atmosphere. Satellite data provide consistent, continuous and spatially distributed information on the Earth's surface conditions among which LST. Ten years of NASA-MODIS day-time and night-time 1 km LST data over Southern Italy have been analyzed to quantify the influence of factors such as topography and the land cover on LST spatio-temporal variations. Results show that topography significantly influence LST variability as a function of the land cover and to a different extent for day-time and night-time data. Moreover, the relation between LST and the influential factors varies with the season during the year. This study contributes to a further understanding of the complex relationship between the spatio-temporal variability of the surface thermal conditions and its driving factors highlighting how these relationships might change within the year

Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High-Resolution Satellite Images

Abstract This article presents a new method for burned area mapping using high-resolution satellite images in the Mediterranean ecosystem. In such a complex environment, high-resolution satellite images represent an appropriate data source for identifying fire-affected areas, and single postfire data are often the only available source of information. The method proposed here integrates several spectral indices into a fuzzy synthetic indicator of likelihood of burn. The indices are interpreted through fuzzy membership functions that have been derived with a partially data-driven approach exploiting training data and expert knowledge. The final map of fire-affected areas is produced by applying a region growing algorithm on the basis of seed pixels selected on a conservative threshold of the synthetic fuzzy score. The algorithm has been developed and tested on a set of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes acquired over Southern Italy. Validation showed that the accuracy of the burned area maps is comparable or even better [overall accuracy (OA) > 90%, K > 0.76] than that obtained with approaches based on single index thresholds adapted to each image. The method described here provides an automatic approach for mapping fire-affected areas with very few false alarms (low commission error), whereas omission errors are mainly related to undetected small burned areas and are located in heterogeneous sparse vegetation cover

Towards an automated approach to map flooded areas from Sentinel-2 MSI data and soft integration of water spectral features

Abstract In this work we propose an approach for mapping flooded areas from Sentinel-2 MSI (Multispectral Instrument) data based on soft fuzzy integration of evidence scores derived from both band combinations (i.e. Spectral Indices - SIs) and components of the Hue, Saturation and Value (HSV) colour transformation. Evidence scores are integrated with Ordered Weighted Averaging (OWA) operators, which model user's decision attitude varying smoothly between optimistic and pessimistic approach. Output is a map of global evidence degree showing the plausibility of being flooded for each pixel of the input Sentinel-2 (S2) image. Algorithm set up and validation were carried out with data over three sites in Italy where water surfaces are extracted from stable water bodies (lakes and rivers), natural hazard flooding, and irrigated paddy rice fields. Validation showed more than satisfactory accuracy for the OR-like OWA operators (F-score > 0.90) with performance slightly decreased (F-scor

Assessing in-season crop classification performance using satellite data: a test case in Northern Italy

AbstractThis study investigated the feasibility of delivering a crop type map early during the growing season. Landsat 8 OLI multi-temporal data acquired in 2013 season were used to classify seven crop types in Northern Italy. The accuracy achieved with four supervised algorithms, fed with multi-temporal spectral indices (EVI, NDFI, RGRI), was assessed as a function of the crop map delivery time during the season. Overall accuracy (Kappa) exceeds 85% (0.83) starting from mid-July, five months before the end of the season, when maximum accuracy is reached (OA=92%, Kappa=0.91). Among crop types, rice is the most accurately classified, followed by forages, maize and arboriculture, while soybean or double crops can be confused with other classes

The contribution of hyperspectral remote sensing to identify vegetation characteristics necessary to assess the fate of Persistent Organic Pollutants (POPs) in the environment

During recent years hyperspectral remote sensing data were successfully used to characterise the state and properties of vegetation. The information on vegetation cover and status is useful for a range of environmental modelling studies. Recent works devoted to the understanding of the fate of Persistent Organic Pollutants (POPs) in the environment showed that forests and vegetation in general act as a «sponge» for chemicals present in air and the intensity of this «capture» effect depends on some vegetation parameters such as surface area, leaf composition, turnover etc. In the framework of the DARFEM experiment conducted in late June 2001, different airborne hyperspectral images were acquired and analysed to derive some vegetation parameters of relevance for multimedia models, such as the spatial distribution of plant species and their relative foliage biomass. The study area, south west of Milan, encompasses a range of land cover types typical of Northern Italy, including intensive poplar plantations and natural broad-leaf forest. An intensive field campaign was accomplished during the aerial survey to collect vegetation parameters and radiometric measurements. Results obtained from the analysis of hyperspectral images, map of vegetation species, Leaf Area Index (LAI) and foliage biomass are presented and discussed

Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at √s = 13 TeV

Abstract The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (d̂ t) and chromomagnetic (μ̂ t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb−1. The linearized variable AFB(1) is used to approximate the asymmetry. Candidate t t ¯ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for t t ¯ final states. The values found for the parameters are AFB(1)=0.048−0.087+0.095(stat)−0.029+0.020(syst),μ̂t=−0.024−0.009+0.013(stat)−0.011+0.016(syst), and a limit is placed on the magnitude of | d̂ t| < 0.03 at 95% confidence level. [Figure not available: see fulltext.