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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.

This paper presents an unsupervised workflow to assess the extension of the burned areas due to wildfire events over green vegetated areas, in particular woods and forests, based on open access and high spatial resolution multispectral images from the Landsat-8 and Sentinel-2 satellites, together with open access high temporal resolution products (MODIS and VIIRS). The idea is to use jointly these kinds of data to combine the advantages linked to the use of high spatial resolution data with those ones derived from the exploitation of high temporal resolution data. Experimental results, carried on different case studies, show the effectiveness of the proposed method on forest fires, by reaching a performance in terms of the F-measure parameter, that ranges from 0.69 to 0.95, depending on the specific case study.

This paper presents an approach to process open access medium and high resolution multispectral images for both active fire monitoring and post-fire damage assessment. The performance of a recently introduced spectral index for active fire detection (AFD3) is assessed and a processing workflows for burning area estimation is applied to a reference case study, using only data provided by the NASA Landsat-8 and ESA Sentinel-2 missions. The reported results shows satisfactory performance, that can be further improved integrating such products in exiting services based on other missions like MODIS and VIIRS.

This paper deals with ground displacement measurements with Differential Synthetic Aperture Radar Interferometry (DInSAR) technique. These ground modifications often occur as a consequence of an earthquake. The island of Ischia (Southern Italy) has been chosen as case study since it was hit by a severe earthquake on the 21st of August 2017. The National Institute of Geophysics and Volcanology (INGV) and the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council of Italy (CNR) provided the displacement maps considering interferometric pairs close to the main shock event. In this work, a further interferometric pair, which also includes some aftershocks, has been used to calculate ground modifications. The results confirmed a ground subsidence up to 4 centimeters in the epicenter area, in agreement with the reference data. This study rely on open access data as well as on open software, both provided by the European Space Agency (ESA) under the Copernicus program. In particular, data from Sentinel-1 radar satellite mission and SNAP software have been used. The availability of such data and software is relevant for public institutions that can produce valuable information at almost no charge.

This study rely on open access data as well as open software, both provided by the European Space Agency (ESA) under the Copernicus program. In particular, data from Sentinel-1 radar satellite mission and SNAP software have been used. The availability of such data and software is relevant for public institutions that can produce valuable information at almost no charge. Main aim of the authors has been to produce a review paper, a kind of tutorial, where main characteristics of Sentinel-1 data and SNAP processing tools are presented and discussed. It's important to underline that SNAP is not an easy tool and needs a deep knowledge of electromagnetic aspects to result into reliable results. Together with Sentinel-1 data, interferometry techniques have been employed to get from phase difference information a measure of ground displacements. The Ischia island has been chosen as Case Study because it was affected by a severe earthquake in August 2017. In the days following the earthquake, the area was subject to many topographic surveys. Some of them, carried out by CNR-IREA researchers [1], working in collaboration with INGV, have used DInSAR technique applied to Sentinel-1 and COSMO-SkyMed images, showing a lowering of the soil around the epicenter up to 4 cm [2], [3]. These data have been used in our processing for comparison and validation as explained later in the paper.

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.

A flood is a relatively high flow of water that overtops the natural and artificial banks in any of the reaches of a stream. When banks are overtopped, water spreads over flood plain and generally causes problem for inhabitants, crops and vegetation. During extreme flood events, it is important to determine quickly the extent of flooding and land use under water. Flood maps can be applied to develop comprehensive relief effort immediately after flooding. There are varieties of issues and uncertainties involved in flood mapping. Remotely sensed data can be used to develop flood map in an efficient and effective way. In this paper different techniques of flood mapping using active and passive remote sensing systems are applied, so that a final flood inundation map has been prepared by combining all data within a Geographical Information System (GIS) environment. The flood inundation maps can be further used for quick identification of areas of potential flood hazard to minimize the flood losses.

Speckle reduction is a key step in several SAR image processing procedures. In this paper, a new despeckling technique based on the “nonlocal” denoising filter BM3D [1] is presented. The filter has been modified in order to take into account SAR image characteristics. The experimental results, conducted on both synthetic and real SAR images, confirm the potential of the proposed approach.

This paper presents a new low-complexity H.264 encoder for Unmanned Aerial Vehicles (UAV) applications. Standard video coding systems currently employed in UAV applications do not rely on some peculiarities in terms of scene 3D model and correlation among successive frames. In particular, the observed scene is static, i.e. the camera movement is dominant, and it can often be well approximated with a plane. Moreover, camera position and orientation can be obtained from the navigation system. Therefore, correspondent points on two video frames are linked by a simple homography. The encoder employs a new motion estimation scheme which make use of the global motion information provided by the onboard navigation system. The homography is used in order to initialize the block matching algorithm allowing a more robust motion estimation and a smaller search window, and hence reducing the complexity. Experimental results show that the proposed scheme ouperforms standard H.264 in terms of PSNR and throughput. The results are relevant in low frame rate video coding, which is a typical scenario in UAV behind line-of-sight (BLOS) missions. Experiments open new drections in developing new sensor aided video coding standards.

International audience; In this paper we address the image restoration problem in the variational framework. Classical approaches minimize the Lp norm of the residual and rely on parametric assumptions on the noise statistical model. We relax this parametric hypothesis and we formulate the problem on the basis of nonparametric density estimates. The proposed approach minimizes the residual differential entropy. Experimental results with non gaussian distributions show the interest of such a nonparametric approach. Images quality is evaluated by means of the PSNR measure and SSIM index, more adapted to the human visual system.