The optimization of forest management in roadsides is a necessary task in terms of wildfire prevention in order to mitigate their effects. Forest fire risk assessment identifies high-risk locations, while providing a decision-making support about vegetation management for firefighting. In this study, nine relevant parameters: elevation, slope, aspect, road distance, settlement distance, fuel model types, normalized difference vegetation index (NDVI), fire weather index (FWI), and historical fire regimes, were considered as indicators of the likelihood of a forest fire occurrence. The parameters were grouped in five categories: topography, vegetation, FWI, historical fire regimes, and anthropogenic issues. This paper presents a novel approach to forest fire risk mapping the classification of vegetation in fuel model types based on the analysis of light detection and ranging (LiDAR) was incorporated. The criteria weights that lead to fire risk were computed by the analytic hierarchy process (AHP) and applied to two datasets located in NW Spain. Results show that approximately 50% of the study area A and 65% of the study area B are characterized as a 3-moderate fire risk zone. The methodology presented in this study will allow road managers to determine appropriate vegetation measures with regards to fire risk. The automation of this methodology is transferable to other regions for forest prevention planning and fire mitigation.

The COVID-19 pandemic has shown the need to maximize the cleanliness of outside public services and the need to disinfect these areas to reduce the virus transmission. This work evaluates the possibilities of using unmanned aircraft systems for disinfection tasks in these aeras. The operational study focuses on evaluating the static and dynamic behavior, as well as the influence of the flying height, mission speed and flow of spraying. The most recommended height for correct spraying with the drone system under study is 3.0 m. The dynamic test shows that the lower height, 3.0 m, also provides the most adequate spraying footprint, achieving 2.2 m for a speed of 0.5 m/s. The operational behavior is evaluated on three different scenarios, a skatepark with an area around 882.7 m², an outdoor gym with an area around 545.0 m² and a multisport court with an area around 2025.7 m². The cleaning time evaluates the flying duration, battery change and tank refill and results in 41 min for the skatepark (5 tank refills and 2 battery changes), 28.6 min for the outdoor gym (3 tank refills and 2 battery changes) and 96.4 min for the multisport court (11 tank refills and 5 battery changes). Each battery change and each tank refill are estimated to take 4 min each, with a drone autonomy of 7 min. The technology appears competitive compared to other forms of cleaning based, for example, on human operators.

The optimization of forest management in the surroundings of roads is a necessary task in term of wildfire prevention and the mitigation of their effects. One of the reasons why a forest fire spreads is the presence of contiguous flammable material, both horizontally and vertically and, thus, vegetation management becomes essential in preventive actions. This work presents a methodology to detect the continuity of vegetation based on aerial Light Detection and Ranging (LiDAR) point clouds, in combination with point cloud processing techniques. Horizontal continuity is determined by calculating Cover Canopy Fraction (CCF). The results obtained show 50% of shrubs presence and 33% of trees presence in the selected case of study, with an error of 5.71%. Regarding vertical continuity, a forest structure composed of a single stratum represents 81% of the zone. In addition, the vegetation located in areas around the roads were mapped, taking into consideration the distances established in the applicable law. Analyses show that risky areas range from a total of 0.12 ha in a 2 m buffer and 0.48 ha in a 10 m buffer, representing a 2.4% and 9.5% of the total study area, respectively.

The use of drones is very extended for multiple applications. Some of them impose attitude and navigation requirements that need appropriate measurements. Pixhawk is an open-source autopilot used on board drones that includes a magnetometer as part of its inertial measurement unit. A testing facility based on a Helmholtz cage is used to evaluate the metrological features of different magnetometer units. The first test induces a magnetic field rotation around six different Pixhawk magnetometers, simulating changes in attitude and course for a drone. The data show a standard deviation higher than 250 mG, in comparison with the standard deviation of 30 mG in the standard gaussmeter model HMR2300. The second test is focused on the stability of the magnetometer, where the Helmholtz cage simulates a certain flight altitude and course. Values obtained from Pixhawk magnetometers range between 25 mG and 116 mG for the highest peak-to-peak magnetic field amplitude, while the standard gaussmeter range is only between 1.3 mG to 5.4 mG. Considering these results, it is possible to determine the limitations of the Pixhawk autopilot magnetometer, both in terms of manufacturing tolerances and measuring stability. Therefore, users should be very careful when using this type of autopilot in professional applications that require precision and safety.