IDENTIFICAÇÃO DE SÍTIOS DE REPRODUÇÃO DE AEDES AEGYPTI COM AERONAVE REMOTAMENTE PILOTADA (ARP)

Um drone e seus complementos de voo são denominados Sistema de Aeronave Remotamente Pilotada (RPAS - Remotely Piloted Aircraft System), sendo uma ferramenta com ampla gama de aplicações em diversas áreas. A pesquisa prospectou novas possibilidades de uso de RPAS com enfoque no diagnóstico e monitoramento de locais de reprodução de Aedes aegypti. Para isso, objetos considerados como potenciais criadouros de larvas de mosquito foram distribuídos em ambientes que permitiam maior ou menor detecção visual dos alvos (embalagens/recipientes) em quatro ambientes: solo coberto com gramínea seca, solo exposto, solo coberto com gramínea de porte baixo e solo coberto com gramínea de porte alto. Foi utilizado RPAS, Phantom 4 Pro com dispositivo móvel e o programa nativo da RPA para os voos. Sobrevoamos alvos para registro fotográfico em quatro alturas do solo (20m, 30m, 60m e 80m). A detecção visual dos alvos foi realizada por um grupo de 10 pessoas denominado júri. O Júri aferiu a maior ou menor probabilidade de detecção de alvos, em função de três variáveis: tipo de alvo, tipo de ambiente e altura de tomada da fotografia aérea. Fotografias obtidas a 30 metros de altura representaram o maior número de alvos identificados (30% dos alvos). Os alvos mais identificados foram pneu, garrafa PET, latas de cerveja e latas de tinta. Os menos identificados foram vasilhas plásticas coloridas e garrafas de cerveja. A pesquisa colaborou para o aperfeiçoamento de procedimentos operacionais de controle e combate a endemias e epidemias, que poderão identificar possíveis criadouros do mosquito por meio de RPA, monitorando áreas de difícil acesso que ofereçam risco a integridade física das pessoas. Palavras-chave: drone; geotecnologias; arboviroses; dengue. Identification of reproduction sites of Aedes aegypti with remote pilot aircraft (ARP) ABSTRACT: A drone and its flight accessories are called Remotely Piloted Aircraft System (RPAS - Remotely Piloted Aircraft System), being a tool with a wide range of applications in several areas. The research explored new possibilities for the use of RPAS with a focus on the diagnosis and monitoring of breeding sites for Aedes aegypti. For this, objects considered as potential breeding grounds for mosquito larvae were distributed in environments that allowed greater or lesser visual detection of targets (packages / containers) in four environments: soil covered with dry grass, exposed soil, soil covered with low grass. and soil covered with tall grass. Was used RPAS, Phantom 4 Pro with an Ipad Mini 4 mobile device and the DJI GO program for flights. We fly over targets for photographic recording at four heights from the ground (20m, 30m, 60m and 80m). The visual detection of the targets was carried out by a group of 10 people called a jury. The Jury assessed the greater or lesser probability of target detection, depending on three variables: type of target, type of environment and height of aerial photography. Photographs taken at a height of 30 meters represented the largest number of targets identified (30% of the targets). The most identified targets were tires, pet bottles, cans of beer and cans of paint. The least identified were colored plastic canisters and beer bottles. The research helped to improve operational procedures for controlling and combating endemics and epidemics, which may identify possible mosquito breeding sites through RPA, monitoring areas of difficult access that pose a risk to people's physical integrity. Keywords: drone; geotecnologies; arbovírus; dengue.

Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield

The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. To date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM (tha−1)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry provided measures of pasture biomass from many overlapping high-resolution images. These measurements were taken throughout a growing season and were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM (tha−1). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base.

Determining Antecedent Pasture State for Climate Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite Based Deep-Learning Method for Estimating Time-Integrated Measures of Pasture Yield

The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. Up to date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM(tha−1)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry, provided measures of pasture biomass from many overlapping high-resolution images. Repeated remotely piloted aircraft system measures throughout a growing season, were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM(tha−1). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base.

Airspace design for integrating RPAS into terminal airspace

Purpose This paper aims to analyse remotely piloted aircraft system (RPAS) integration in non-segregated terminal airspace. This work aims to identify the potential airspace volumes where a free operation of RPAS can be developed by analysing the airspace design of the terminal airspace. Design/methodology/approach The methodology considers five crucial elements of the airspace design: obstacles, prohibited, restricted and dangerous zones, aerodrome zones, departing and arriving procedures and visual corridors. Free operation of RPAS is performed in those airspace volumes that no interaction with instrumental flight rules (IFR) flights is expected. Free RPAS airspace volumes are separated through current IFR separation minima. Findings The results show there is a significant amount of available airspace that RPAS can operate without interaction with conventional aircraft. The more significant risks are allocated by the limitations imposed by departing and arriving procedures in the terminal airspace. Research limitations/implications The methodology is applied to medium-dense terminal airspace. This work assumes RPAS can perform visual or instrumental flights. Originality/value RPAS is a capital issue for the majority of aviation actors. This work underlay the further development of a methodology regarding airspace design for RPAS in a terminal control area.

The Existing Technologies on Anti-Drone Systems

The paper presents the technologies currently available on the market in the field of anti-drone systems (C-RPAS -Counter Remotely Piloted Aircraft System). These include technologies with the help of radar, audio interception systems or via infrared and electro-optical devices, which are limited in remote sensing. The purpose of this paper was to highlight the multitude of factors that can influence the main mission of C-RPAS systems, the detection. Without detection the other features of a C-RPAS system could not be applied. I used specialized documents and studies, but also comparative analysis as research methods. The results of the study confirmed to me the hypothesis that anti-drone systems use in combination, one or more of the technologies (to detect, to recognize, to identify, to locate, to block, to capture or to destroy the drone). The first four (the detection, the recognition, the identification, the localization) are in the basic configuration for any C-RPAS system. In the future, there will be a challenge (for the producers of C-RPAS systems), the capture of the RPAS, especially the military ones. It is also important to prepare the operators / beneficiaries for such systems. They can influence the effectiveness of drone combat missions.

The Threat that is Represented by the Unauthorized Flight of the Rpas and the Anti-Drone Systems

The unauthorized flights of the RPAS (Remotely Piloted Aircraft System) represent serious threats to the security of the aircraft with human crew on board, to the security of the troops in the theatre of operations as well as to the critical infrastructures and why not to the security of the civilian population. The purpose of the study was to identify which events have occurred recently related to RPAS and which of these represent real challenges to security and defense. Specific regulations and standards are not enough to control the RPAS phenomenon. I used specialized documents and studies, information and conclusions presented by the media, comparative analysis as research methods. Also, I highlighted the risks represented by RPAS. I have presented in chronological order some of the events that have taken place lately related to the use of RPAS, to demonstrate their wide spectrum of use. Even if several C-RPAS systems are installed (for anti-drone protection), an important area cannot be secured if you do not have enough systems capable of covering the entire restricted airspace. The development of anti-drone systems (C-RPAS) is rapidly required.

Team Situation Awareness and Conflict: A Study of Human–Machine Teaming

This article focuses on two fundamental human–human teamwork behaviors and seeks to understand them better in human–machine teams. Specifically, team situation awareness (TSA) and team conflict are examined in human–machine teams. There is a significant need to identify how TSA and team conflict occur during human–machine teaming, in addition to how they impact each other. In this work, we present an experiment aimed at understanding TSA and team conflict in the context of human–machine teaming in a remotely piloted aircraft system (RPAS). Three conditions were tested: (1) control: teams consisted of all humans; (2) synthetic: teams consisted of the pilot role being occupied by a computational agent based on ACT-R architecture that employed AI capabilities, with all other team roles being humans; and (3) experimenter: an experimenter playing the role of the pilot as a highly effective computational agent, with the other roles being humans. The results indicate that TSA improved over time in synthetic teams, improved and then stabilized over time in experimenter teams, and did not improve in control teams. In addition, results show that control teams had the most team conflict. Finally, in the control condition, team conflict negatively impacts TSA.