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.

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

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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.