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.

Illegal dumping is one of the major issues to be addressed by public managers in both developed and developing countries. The adequate tackling and enforcement of such unlawful activities require continuous territory monitoring, the lack of which is the principal cause of the failure of traditional systems based on patrolling, eventually triggered by sparse crowdsourced data. This work proposes the digitalization of the entire illegal waste management monitoring process through an innovative decision support system based on multiscale remote sensing data. These data enable the assessment of the risk level of specific areas, thus determining inspection priorities in line with the available patrolling resources. The proposed methodology provides a tool to assess the impact of diverse monitoring system parameters on overall the performance in light of the expected operating costs and to understand whether remote sensing can help a better management of illegal waste in a specific operational scenario, thus helping in the determination of the best cost–benefit trade-off. Simulation results over a demonstration scenario, based on realistic dumping/restoration dynamics and the typical performance of satellite detection systems, show that the adoption of remote sensing technologies in the monitoring process can increase the restoration capacity by about 10% compared to traditional patrolling.

In this paper a set of spectral indices, some of them novel, is suggested in order to effectively process multispec-tral images, from the NASA and ESA missions Landsat-8 and Sentinel-2, for fire monitoring. The processing aims at extract common products, useful to detect active fires and evaluate the position and extension of the burned areas. The use of open data in comparison with other paid solutions is to remark because of great interest to public and private end users. The thresholding of the proposed indices produces satisfactory results in terms of classification accuracy. Moreover, the joint use of images from Landsat-8 and Sentinel-2, by increasing the overall availability of data, allows also to overcome some limitations on time delivery and high frequency observation, requirements that instead are goodly matched through the use of lower resolution multispectral images such as those from MODIS and VIIRS satellites. The proposed indices have been successfully tested on Vesuvius, that was devastated by terrible fires on the of 11th July 2017. Respect to other works of the same authors on this topic, the performance of the suggested indices are here more deeply investigated, comparing them to alternative products.

This paper presents an innovative approach for detecting illegal microdumps using very high-resolution optical satellite imagery, addressing a significant environmental monitoring challenge in Campania, Italy. Due to the regional vulnerability to illegal dumping, exacerbated by the waste management crisis, there is a pressing need for enhanced surveillance and accurate identification of microdump locations. This paper uses deep learning techniques to introduce an effective technology for detecting microdumps in high-resolution optical satellite images from Pleiades and GeoEye-1 satellites in an end-to-end solution, from images to detection. Its primary aim is to preliminarily assess dumping sites within specific target areas of interest (patrolling cells) for subsequent on-ground confirmation and characterization. The proposed system comprises two neural networks: the first, based on RetinaNet, identifies regions containing microdumps, while the second, utilizing InceptionV3, enhances the detection through pixel-wise classification. A fusion rule is then applied to combine the decisions of these networks. This technology addresses an environmental issue and is part of a progressive monitoring process. Validation was performed through a significant case study focusing on an extensive area between Naples and Caserta in the Campania region in Italy, particularly affected by illegal microdumps. A model was trained and validated using the pansharpened version of Pleiades multispectral images. This model exhibits potential for detecting microdumps in images from other satellite missions, as confirmed by validating it with GeoEye-1 imagery without further fine-tuning or training. The performance of the proposed detection system, evaluated for the reference application, achieves a detection rate of approximately 90% and a false discovery rate of about 40%. Notably, this is attained using a fully automatic processing chain without geospatial integration with additional information sources. In conclusion, despite satellite images having limited ground sampling distance and subsequent lower accuracy of image understanding algorithms, they remain suitable for environmental monitoring applications from an end-user perspective.

Proximal gamma-ray spectroscopy is a consolidated technology for a continuous and real-time tracing of soil water content at field scale. New developments have shown that this method can also act as an unbiased tool for remotely distinguishing rainwater from irrigation without any meteorological support information. Given a single detector, the simultaneous observation in a gamma spectrum of a transient increase in the ²¹⁴Pb signal, coupled with a decrease in the ⁴⁰K signal, acts as an effective proxy for rainfall. A decrease in both ²¹⁴Pb and ⁴⁰K signals is, instead, a reliable fingerprint for irrigation. We successfully proved this rationale in two data-taking campaigns performed on an agricultural test field with different crop types (tomato and maize). The soil moisture levels were assessed via the ⁴⁰K gamma signal on the basis of a one-time setup calibration. The validation against a set of gravimetric measurements showed excellent results on both bare and vegetated soil conditions. Simultaneously, the observed rain-induced increase in the ²¹⁴Pb signal permitted to identify accurately the rain and irrigation events occurred in the 8852 h of data taking.