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In the last decades, Mediterranean rural landscapes have undergone significant changes, with relevant considerable environmental and socio-economic impacts. These phenomena are often triggered by agricultural abandonment, especially in environmentally-sensitive areas, which are usually located in marginal and less profitable regions, and which could indeed irremediably compromise the identity and role of these Mediterranean landscapes. On the other hand, the progressive increase of available multi-source geodata allows to reconstruct the landscape original structure, providing new tools able to prevent negative impacts on environment. Hence, thanks to the development of increasingly advanced and open-source GIS tools, it is possible to implement several geodata typologies that can be mutually integrated in an increasingly efficient approach. In this paper the process of landscape reshaping pattern is analyzed in a study area of Basilicata region (Southern Italy) using remote sensing. In particular, the vegetation component of a landscape has been assessed by means of SAR images by using an artificial intelligence approach, that is machine learning to understand landscape dynamics in two different time periods. In this way, it has been possible to integrate data of different source and composition into landscape analysis methodologies, hence developing a suitable tool for planning and managing the rural landscape.

A built environment, that also includes infrastructures, needs to be taken under control to prevent unexpected modifications, otherwise it could react as a loose cannon. Sensing techniques and technologies can come to the rescue of built environments thanks to their capabilities to monitor appropriately. This article illustrates findings related to monitoring a channel hydrodynamic behavior by means of sensors based on imaging and ultrasound. The ultrasound approach is used here to monitor the height of the water with respect to a maximum limit. Imaging treatment is here proposed to understand the flow velocity under the area to be considered. Since these areas can be covered by trash, an enhanced version of the particle image velocimetry technique has been implemented, allowing the discrimination of trash from water flow. Even in the presence of the total area occupied by trash, it is able to detect the velocity of particles underneath. Rainfall and hydraulic levels have been included and processed to strengthen the study.

Rural pipelines dedicated to water distribution, that is, waterworks, are essential for agriculture, notably plantations and greenhouse cultivation. Water is a primary resource for agriculture, and its optimized management is a key aspect. Saving water dispersion is not only an economic problem but also an environmental one. Spectral estimation of leakage is based on processing signals captured from sensors and/or transducers generally mounted on pipelines. There are different techniques capable of processing signals and displaying the actual position of leaks. Not all algorithms are suitable for all signals. That means, for pipelines located underground, for example, external vibrations affect the spectral response quality, then, depending on external vibrations/noises and flow velocity within pipeline, one should choose a suitable algorithm that fits better with the expected results in terms of leak position on the pipeline and expected time for localizing the leak. This paper presents findings related to the application of a decimated linear prediction (DLP) algorithm for agriculture and rural environments. In a certain manner, the application also detects the hydrodynamics of the water transportation. A general statement on the issue, DLP illustration, a real application and results are also included.

Pipelines conveying fluids are considered strategic infrastructures to be protected and maintained. They generally serve for transportation of important fluids such as drinkable water, waste water, oil, gas, chemicals, etc. Monitoring and continuous testing, especially on-line, are necessary to assess the condition of pipelines. The paper presents findings related to a comparison between two spectral response algorithms based on the decimated signal diagonalization (DSD) and decimated Pad&eacute, approximant (DPA) techniques that allow to one to process signals delivered by pressure sensors mounted on an experimental pipeline.