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Satellite remote sensing allows for large scale monitoring with low cost and high revisit time. However, in some applications, it does not provide all the information needed by the analyst for the full characterization of the problem due to, as an example, insufficient resolution or lack of specific measurements. This can lead to inaccuracies in classification and/or detection tasks. Nevertheless, satellite data can be used to guide subsequent discovery, recognition and characterization actions, defining potential areas of interest of limited extension that can be furtherly investigated with on-site strategies. This work presents an innovative framework showing how to use incomplete and inaccurate information extracted by satellite images in order to address on-ground discovery actions aimed to the mapping and the characterization of illegal micro-dumps in Campania Region (Italy). In particular, high-resolution images up to 50 cm resolution are exploited to detect potential micro-dumps by means of a statistical learning method based on spatial features. The detection map is then used to create a priority map based on environmental risk considerations, such as the extension of the area interested by the dump and its proximity to urban settlements, and previous risk mitigation actions. This information is ingested by a planning system in order to allocate and calculate patrolling routes based on the available manpower and vehicles for on-site surveying. The survey is implemented by means of drones equipped with payloads and software allowing for real-time three-dimensional reconstruction of the scene and volumetric estimations. This provides further data to assess the real dangerousness of the site giving to decision makers essential information to plan remediation actions. The system is demonstrated through a case study showing all the stages of the decision process.

In some environmental applications, satellite acquisitions could not be able to provide all the information necessary to characterize the problem at hand due, for example, to limited spatial resolution or inadequate revisit time. However, in these cases, they can be used for preliminary investigation of the area of interest with the purpose to guide subsequent acquisitions with higher spatial resolution made by means of aerial sensing. This work presents an innovative application combining both satellite acquisitions and aerial close-range sensing implemented via drones in autonomous and coordinated flight. The case study concerns the discovery of illegal micro-dumps and other environmental hazards in Campania Region (Italy). The envisioned workflow includes the detection of target environmental criticalities in very high-resolution optical satellite images and a methodology to plan and adaptively re-plan a survey mission of a team of drones aimed at confirming the presence of a micro-dump and at its characterization. The processing of satellite images is validated on real data in a significative application context, while the performance of the acquisition strategy performed by the drone team are characterized trough simulations on a pre-analysed geographical area.

Dam monitoring represents a crucial issue in order to avoid catastrophic failures due to infrastructure aging or earthquake damages. Differential SAR Interferometry (DInSAR) is a technique suitable for critical infrastructure monitoring, also for the availability of free data and tools, that can be used by experts in SAR remote sensing and also by geologists and civil engineers, after having acquired the right confidence and experience in these data processing and tool use. In order to apply the DInSAR technique, in its basic and simple version, to critical infrastructure monitoring, it is very important to assess its performance. Nevertheless, validation results are not largely available in literature, because heterogeneous technical competencies are required to this aim and in situ measurements must be collected and made available. In this paper, we propose a highly reproducible DInSAR workflow that can be effectively used for dam monitoring, by validating its results with in situ measurements on some significant case studies in Italy.

In case of emergency, a rapid first picture of the damaged area is required in order to have a better comprehension of the event and to deploy first aids. The continuous monitoring of the area affected by a disaster can also help to follow its evolution and to evaluate the consequences on population and man-made structures. The continuous monitoring is usually defined as persistence. In this paper, the deployment of HAPS (High Altitude Pseudo-Satellite) is proposed to monitor an emergency area right during the phase of disaster response, offering images in proximity and time persistence. Indeed, thanks to their proximity to the Earth surface (18–20 km of altitude) HAPS could provide images with greater spatial resolution than satellites and greater time resolution because of station-keeping or loiter capability on the desired area. Feasibility and effectiveness of this proposal is assessed by a simulation of a hypothetic HAPS usage in the realistic condition represented by the Central Italy earthquake in 2016.