The aim of this paper is to highlight how the employment of Light Detection and Ranging (LiDAR) technique can enhance greatly the performance and reliability of many monitoring systems applied to the Earth Observation (EO) and Environmental Monitoring. A short presentation of LiDAR systems, underlying their peculiarities, is first given. References to some review papers are highlighted, as they can be regarded as useful guidelines for researchers interested in using LiDARs. Two case studies are then presented and discussed, based on the use of 2D and 3D LiDAR data. Some considerations are done on the performance achieved through the use of LiDAR data combined with data from other sources. The case studies show how the LiDAR-based systems, combined with optical Very High Resolution (VHR) data, succeed in improving the analysis and monitoring of specific areas of interest, specifically how LiDAR data help in exploring external environment and extracting building features from urban areas. Moreover the discussed Case Studies demonstrate that the use of the LiDAR data, even with a low density of points, allows the development of an automatic procedure for accurate building features extraction, through object-oriented classification techniques, therefore by underlying the importance that even simple LiDAR-based systems play in EO and Environmental Monitoring.

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.

In recent decades, floods have caused significant loss of human life as well as interruptions in economic and social activities in affected areas. In order to identify effective flood mitigation measures and to suggest actions to be taken before and during flooding, microscale risk estimation methods are increasingly applied. In this context, an implemented methodology for microscale flood risk evaluation is presented, which considers direct and tangible damage as a function of hydrometric height and allows for quick estimates of the damage level caused by alluvial events. The method has been applied and tested on businesses and residential buildings of the town of Benevento (southern Italy), which has been hit by destructive floods several times in the past; the most recent flooding occurred in October 2015. The simplified methodology tries to overcome the limitation of the original method—the huge amounts of input data—by applying a simplified procedure in defining the data of the physical features of buildings (e.g., the number of floors, typology, and presence of a basement). Data collection for each building feature was initially carried out through careful field surveys (FAM, field analysis method) and subsequently obtained through generalization of data (DGM, data generalization method). The basic method (FAM) allows for estimating in great detail the potential losses for representative building categories in an urban context and involves a higher degree of resolution, but it is time-consuming; the simplified method (DGM) produces a damage value in a shorter time. By comparison, the two criteria show very similar results and minimal differences, making generalized data acquisition most efficient.

This paper presents a case study about the detection of illegal dumps from optical satellite images in a large territory falling in the provinces of Naples and Caserta, Southern Italy. This location is also known with the term "Terra dei Fouchi" because in this area is particularly widespread the phenomenon of waste burning and, over the past decades, there have been many landfills of hazardous waste of industrial origin. In order to contrast this phenomenon, the government of the Campania Region organized some prevention, monitoring and repression activities. In particular, the monitoring activities are employed by periodic inspection of sites, which are often object of illegal deposits (former quarries, illegal dumps, as well as city and country roads). The periodic inspection is usually performed by patrols of the company SMA Campania (the in-house regional company, specialized in environmental protection), and law enforcement agencies. Since early warning of illegal and the quick characterization of the detected sites is a key factor for effective resource allocation in the fight against illegal waste dumping, as part of a project, the periodic monitoring inspections of the local environmental agency have been supported with multi-temporal satellite images. The detection has been performed via expert photo-interpretation in order to achieve a high level of accuracy and reliable maps. However, in order to reduce this time consuming task, a multi-features detection algorithm has been implemented. Detected sites have been then classified according to 4 major characteristics (type, state, location and activity) of the dumps. Moreover, a multi-temporal analysis has been performed and it allowed following the evolution of the phenomenon. This approach was effective in both finding new illegal spills (with associated macro classification) and to follow the evolution (in terms of extension and persistence) of landfills already found in the past.

In this paper, an in-depth study is presented regarding an innovative methodology on which the authors have invested several months of research, as evidenced by their recent references on similar topics. A semi-automatic classification of the buildings in urban area is analyzed, when Light Detection And Ranging (LiDAR) data are combined to Very High Resolution (VHR) WorldView-2 (WV-2) images and an Object-Based Image Analysis (OBIA) technique is used. The aim of the data fusion from different sensor types is to realize an effective and limited-cost product for protection and management of environmental and natural resources, to meet the needs of many institutions, such as municipalities, provinces and regions, and for applications in the context of risk analysis, territorial planning and local development. This procedure may be extended to large areas of the territory, resulting into a reduction of the processing time for the building detection, with respect to a human visual or manual photointerpretation. Interesting results and further implications will be presented and discussed.

The international experiment EAQUATE (European AQUA Thermodynamic Experiment) was held in September 2004 in Italy and in the United Kingdom. The Italian phase, performed in the period 6-10 September 2004, was mainly devoted to assessment and validation of performances of new IR hyperspectral sensors and benefits from data and results of measurements of AQUA and in particular of AIRS. It is also connected with the preparatory actions of MetOp mission with particular attention to calibration and validation of IASI products (as water vapour and temperature profiles), characterization of semitransparent clouds and study of radiative balance, demonstrating the role of ground-based and airborne systems in validation operations. The Italian phase of the campaign was carried out within a cooperation between NASA Langley Research Center, University of Wisconsin, the Istituto di Metodologie per l'Analisi Ambientale (CNR-IMAA), the Mediterranean Agency for Remote Sensing (MARS) and the Universities of Basilicata, Bologna and Napoli. It involved the participation of the Scaled Composites Proteus aircraft (with NAST thermal infrared interferometer and microwave radiometer, the Scanning HIS infrared interferometer, the FIRSC far-IR interferometer), an Earth Observing System-Direct Readout Station and several ground based instruments: four lidar systems, a microwave radiometer, two infrared spectrometers, and a ceilometer. Radiosonde launches for measurements of PTU and wind velocity and direction were also performed as ancillary observations. Four flights were successfully completed with two different AQUA overpasses. The aircraft flew over the Napoli, Potenza and Tito Scalo ground stations several times allowing the collection of coincident aircraft and in- situ observations.