Typical mountain Search and Rescue (SaR) operations require the localization of the persons involved in accidents in harsh environments. ARVA and RECCO are the current standards for the localization in snowy environments although their radio range is limited to some tens of meters. In this paper, we prove by experimental results that the Long Range (LoRa) Low-Power Wide-Area Network (LPWAN) technology is very promising for SaR applications due to its extended radio range. A LoRa-based system for SaR operations is presented and analyzed. The localization of the persons is obtained through an algorithm based on path loss measurements. Radio path loss models of body-worn LoRa devices in harsh mountain environments are derived by measurements. We observed that, although the communication range of LoRa decreases from kilometres to hundreds of meters in the tested environments, at least 50% of the transmitted packets can be received at distances about five times greater than those achievable with golden standard technologies such as ARVA. The performances of the considered localization algorithm are analyzed on the basis of the collected data. The achievable accuracy is in the order of meters around the true position for a relatively large number of available path loss measurements. Lastly, we propose and detail a LoRa-based system for SaR operations. .

Response to medical incidents in mountainous areas is delayed due to the remote and challenging terrain. Drones could assist in a quicker search for patients and can facilitate earlier treatment through delivery of medical equipment. We aim to assess the effects of using drones in search and rescue (SAR) operations in challenging terrain. We hypothesize that drones can reduce the search time and treatment-free interval of patients by delivering an emergency kit and telemedical support. In this randomized controlled trial with a cross-over design two methods of searching for and initiating treatment of a patient were compared. The primary outcome was a comparison of the times for locating a patient through visual contact and starting treatment on-site between the drone-assisted intervention arm and the conventional ground-rescue control arm. A linear mixed model (LMM) was used to evaluate the effect of using a drone on search and start of treatment times. Twenty-four SAR missions, performed by six SAR teams each with four team members, were analyzed. The mean time to locate the patient was 14.6 min (95% CI 11.3-17.9) in the drone-assisted intervention arm and 20.6 min (95% CI 17.3-23.9) in the control arm. The mean time to start treatment was 15.7 min (95% CI 12.4-19.0) in the drone-assisted arm and 22.4 min (95% CI 19.1-25.7) in the control arm (p < 0.01 for both comparisons). The successful use of drones in SAR operations leads to a reduction in search time and treatment-free interval of patients in challenging terrain, which could improve outcomes in patients suffering from traumatic injuries, the most commonly occurring incident requiring mountain rescue operations.

The Copernicus Sentinel-1 mission provides synthetic aperture radar (SAR) acquisitions over large areas with high temporal and spatial resolution. This new generation of satellites providing open-data products has enhanced the capabilities for continuously studying Earth surface changes. Over the past two decades, several studies have demonstrated the potential of differential synthetic aperture radar interferometry (DInSAR) for detecting and quantifying land surface deformation. DInSAR limitations and challenges are linked to the SAR properties and the field conditions (especially in mountainous environments) leading to spatial and temporal decorrelation of the SAR signal. High temporal decorrelation can be caused by changes in vegetation (particularly in nonurban areas), atmospheric conditions, or high ground surface velocity. In this study, the kinematics of the complex and vegetated Corvara landslide, situated in Val Badia (South Tyrol, Italy), are monitored by a network of three permanent and 13 monthly measured benchmark points measured with the differential global navigation satellite system (DGNSS) technique. The slope displacement rates are found to be highly unsteady and reach several meters a year. This paper focuses firstly on evaluating the performance of DInSAR changing unwrapping and coherence parameters with Sentinel-1 imagery, and secondly, on applying DInSAR with DGNSS measurements to monitor an active and complex landslide. To this end, 41 particular SAR images, coherence thresholds, and 2D and 3D unwrapping processes give various results in terms of reliability and accuracy, supporting the understanding of the landslide velocity field. Evolutions of phase changes are analysed according to the coherence, the changing field conditions, and the monitored ground-based displacements.

A general framework for the integration of multi-sensor data for dry and fresh biomass retrieval is proposed and tested in Alpine meadows and pastures. To this purpose, hyperspectral spectroradiometer (as simulation of hyperspectral imagery) and biomass samples were collected in field campaigns and Copernicus Sentinel-1 Interferometric Wide (IW) swath SAR backscattering coefficients were used. First, a genetic algorithm feature selection was performed on hyperspectral data, and afterwards the resulting most sensitive bands where combined with SAR data within a support vector regression (SVR) model. The most sensitive hyperspectral bands were mainly located in different regions of the SWIR range for both fresh and dry biomass, and in the red and near-infrared regions mainly for dry biomass, but with less influence for fresh biomass. The R² correlation values between the sampled and the estimated biomass range from 0.24 to 0.71. The relatively low performances are mainly related to the saturation effect in the optical bands, as well as to the paucity of points for high values of biomass. The methodology allows a better understanding of the interaction between grassland systems and the electromagnetic spectrum by offering a model with a reduced number of narrow bands in the context of a multi-sensor integration.

Introduction: Out-of-hospital cardiac arrest (OHCA) has a high global incidence and mortality rate, with early defibrillation significantly improving survival. Our aim was to assess the feasibility of autonomous drone delivery of automated external defibrillators (AED) in a non-urban area with physical barriers and compare the time to defibrillate (TTD) with bystander retrieval from a public access defibrillator (PAD) point and helicopter emergency medical services (HEMS) physician performed defibrillation. Methods: This randomized simulation-based trial with a cross-over design included bystanders performing AED retrievals either delivered by automated drone flight or on foot from a PAD point, and simulated HEMS interventions. The primary outcome was the time to defibrillation, with secondary outcomes comparing workload, perceived physical effort, and ease of use. Results: Thirty-six simulations were performed. Drone-delivered AED intervention had a significantly shorter TTD [2.2 (95 % CI 2.0–2.3) min] compared to PAD retrieval [12.4 (95 % CI 10.4–14.4) min] and HEMS [18.2 (95 % CI 17.1–19.2) min]. The self-reported physical effort on a visual analogue scale for drone-delivered AED was significantly lower versus PAD [2.5 (1 – 22) mm vs. 81 (65–99) mm, p = 0.02]. The overall mean workload measured by NASA-TLX was also significantly lower for drone delivery compared to PAD [4.3 (1.2–11.7) vs. 11.9 (5.5–14.5), p = 0.018]. Conclusion: The use of drones for automated AED delivery in a non-urban area with physical barriers is feasible and leads to a shorter time to defibrillation. Drone-delivered AEDs also involve a lower workload and perceived physical effort than AED retrieval on foot.

The data set contains information on aboveground vegetation traits of > 100 georeferenced locations within ten temperate pre-Alpine grassland plots in southern Germany. The grasslands were sampled in April 2018 for the following traits: bulk canopy height; weight of fresh and dry biomass; dry weight percentage of the plant functional types (PFT) non-green vegetation, legumes, non-leguminous forbs, and graminoids; total green area index (GAI) and PFT-specific GAI; plant water content; plant carbon and nitrogen content (community values and PFT-specific values); as well as leaf mass per area (LMA) of PFT. In addition, a species specific inventory of the plots was conducted in June 2020 and provides plot-level information on grassland type and plant species composition. The data set was obtained within the framework of the SUSALPS project (“Sustainable use of alpine and pre-alpine grassland soils in a changing climate”;https://www.susalps.de/) to providein-situdata for the calibration and validation of remote sensing based models to estimate grassland traits.