Abstract. The Italian legislation on Remote Pilot Aircraft Systems (SAPR: Sistema Areomobile a Pilotaggio Remoto), as in 2012, regulates the use of drones in controlled and uncontrolled airspace. In 2016, the regulation introduced simplified procedures for the use of ultra-light drones. These instruments are particularly widespread in the field of Cultural Heritage survey. In fact, according to the Italian regulations currently in force it is possible to pilot a drone of less than 300 grams without the need of a flight license and without a specific training course and medical examination and it is not required a special permit to fly in populates areas (although without flying over groups of people). Another possible explanation is the limited cost of these aircrafts and their easy availability on the market, both on the shelves of all electronic shops and in online stores.Following the boom of drones under 300 g, and considering the new European regulations also pay particular attention to a similar segment of Unmanned Aerial Vehicle (UAV) (in the future for aircraft under 250 g), it is important to evaluate the results that can be obtained through these small instruments and above all to evaluate which are the fields of application compatible with the technical limitations imposed by the need to lighten the components onboard (think in particular of the sensors of digital cameras).The purpose of the study is linked to the documentation of Cultural Heritage, in particular, we want to investigate the quality and metric reliability of photogrammetric surveys carried out through ultra-light drone images. Some application of UAV photogrammetry by ultra-light drones are showed in this paper and they deal with archaeological and architectural survey.

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Compositional data are commonly present in many disciplines. Nevertheless, it is often improperly incorporated into statistical modelling and a misleading interpretation of the results is given. This paper explains how partial least squares for discrimination is an adequate technique for compositional data when a dimensional reduction of original variables is needed and difining the variables that more influence the discrimination between the observations is the goal.

Water quality monitoring data typically consist of J parameters and constituents measured at I number of static locations at K sets of seasonal occurrences. The resulting I x J x K three-way array can be difficult to interpret. Additionally, the constituent portion of the dataset (e.g., major ion and trace element concentration, pH, etc.) is compositional in that it sums to a constant (e.g., 1 kg/L) and is mathematically confined to the simplex, the sample space for compositional data. Here we apply a Tucker3 model on centered log-ratio data to find low dimensional representation of latent variables as a means to simplify data processing\ud and interpretation of three years of seasonal compositional groundwater chemistry data for 14 wells at a study\ud site in Wyoming, USA. The study site has been amended with treated coalbed methane produced water, using a subsurface drip irrigation system, to allow for irrigation of forage crops. Results from three-way\ud compositional data analysis indicate that primary controls on water quality at the study site include: solutes concentration by evapotranspiration, cation exchange, and dissolution of native salts. These findings agree well with results from more detailed investigations of the site. In addition, the model identified Ba uptake during gypsum precipitation in some portions of the site during the final 6–9 months of investigation, a process for which the timing and extent had not previously been identified. These results suggest that multi-way compositional analyses hold promise as a means to more easily interpret water quality monitoring data

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