Bistatic LIDAR System for the Characterisation of Aviation-Related Pollutant Column Densities

et al. 2020

Sensors

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In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.

Section: Remote Sensingmentioning

confidence: 99%

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

Fahey

Pham

et al. 2020

Sensors

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“…With respect to the modelling and assessment of the environmental impacts of aviation, researchers are now extending the investigation in the complex interdependencies of thermochemical processes associated with the aviation pollutant emissions, and in the development of measurement and estimation techniques to more accurately characterise aircraft emissions. In particular, new measurement techniques are required to characterise aircraft emissions at higher spatial and time resolutions, thus allowing research in trajectory optimisation and more generally on aviation sustainability to more accurately formulate the mathematical problem and deliver more effective solutions [25][26][27] studies, these models show limited accuracy in estimating the emissions, as anomalies in the engine or fuel conditions can void key assumptions and compromise the validity of these modelbased estimations. For more detail on the assessment of environmental impacts of aviation, the reader is referred to [28][29][30][31][32][33][34].…”

Section: Environmental Sustainability Of Aviationmentioning

confidence: 99%

Multi-objective optimisation of aircraft flight trajectories in the ATM and avionics context

Progress in Aerospace Sciences

Sabatini

Ramasamy

2016

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114

The continuous increase of air transport demand worldwide and the push for a more economically viable and environmentally sustainable aviation are driving significant evolutions of aircraft, airspace and airport systems design and operations. Although extensive research has been performed on the optimisation of aircraft trajectories and very efficient algorithms were widely adopted for the optimisation of vertical flight profiles, it is only in the last few years that higher levels of integration were proposed for automated flight planning and rerouting functionalities of innovative Communication Navigation and Surveillance/Air Traffic Management (CNS/ATM) and Avionics (CNS+A) systems. In this context, the implementation of additional environmental targets and of multiple operational constraints introduces the need to efficiently deal with multiple objectives as part of the trajectory optimisation algorithm. This article provides a comprehensive review of Multi-Objective Trajectory Optimisation (MOTO) techniques for transport aircraft flight operations, with a special focus on the recent advances introduced in the CNS+A research context. In the first section, a brief introduction is given, together with an overview of the main international research initiatives where this topic has been studied, and the problem statement is given. The second section introduces the mathematical formulation and the third section reviews the numerical solution techniques, including discretisation and optimisation methods for the specific problem formulated. The fourth section summarises the strategies to articulate the preferences and to select optimal trajectories when multiple conflicting objectives are introduced. The fifth section introduces a number of models defining the optimality criteria and constraints typically adopted in MOTO studies, including fuel consumption, air pollutant and noise emissions, operational costs, condensation trails, current airspace and airport operations. A brief overview of atmospheric and weather modelling is also included. Key equations describing the optimality criteria are presented, with a focus on the latest advancements in the respective application areas. In the sixth section, a number of MOTO implementations in the CNS+A systems context are mentioned with relevant simulation case studies addressing different operational tasks. The final section draws some conclusions and outlines guidelines for future research on MOTO and associated CNS+A system implementations.

“…A stand-off measurement system based on bistatic LIDAR techniques was conceptually investigated in [14][15][16], based on previous research [6,[17][18][19]. The principle of operation of the aerial monitoring by an Unmanned Aircraft (UA) employing bistatic LIDAR measurement techniques is depicted in Fig.…”

Section: Conceptual Designmentioning

confidence: 99%

“…The propagation of laser radiation in atmosphere is affected by a number of linear and nonlinear effects. In [15] we described the following expression for the peak irradiance I P , accounting for absorption, scattering, diffraction, jitter, atmospheric turbulence and thermal blooming effects assuming a Gaussian profile of the laser beam at the source and an average focused irradiance [19,20]:…”

Section: Atmospheric Laser Beam Propagationmentioning

confidence: 99%

“…Errors were introduced on the distance, , and on the differential energy measurement, which is translated into by means of the Bidirectional Reflectance Distribution Function (BRDF) of the target surface [18]. Assuming the operational conditions summarised in Table 4 and injecting source errors detailed in Table 5, the resulting relative error for the CO 2 volume density was calculated as ̃ ̃ = 6.77 % [15]. These preliminary results associated with the very low error figures from the monostatic Integral Path Differential Absorption (IPDA) LIDAR experimental campaigns [24] and with the estimated performance of the in-situ calibration technique, contribute to supporting the validity of the proposed bistatic DIAL measurement technique for high accuracy sensing of aviation-related pollutant concentrations.…”

Section: Lidar Error Estimationmentioning

confidence: 99%

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Stand-off measurement of industrial air pollutant emissions from unmanned aircraft

2016 International Conference on Unmanned Aircraft Systems (ICUAS)

Sabatini

Ramasamy

2016

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This paper investigates the stand-off measurement of atmospheric pollutant concentrations and air quality parameters around industrial complexes. The theoretical investigation considers a robust, accurate and inexpensive measurement system based on tuneable Light Detection and Ranging (LIDAR), calibrated reflectors and imaging systems. The equipment is deployed in two non-collocated components. The source component is installed on board an unmanned aircraft. The sensor component is constituted by a reflector calibrated for reflectance, a rail-mounted infrared camera calibrated for radiance and highly wavelength-selective optics. The system is conceived to perform Differential Absorption LIDAR (DIAL) measurements of selected molecular pollutants and a model-based estimation of aerosol pollutant concentrations by means of suitably developed inversion algorithm. The relevant opportunities and challenges, and the viability of the system in the intended operational environments are discussed. Numerical simulation results show promising performances in term of estimated error budget even in degraded meteorological conditions, which are comparable to the more complex and relatively costly monostatic LIDAR techniques currently available. I.