sensors-logo

Journal Browser

Journal Browser

Laser Based Remote Sensors for Environmental Science: Apparatus, Measurements and Analysis Techniques

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 14985

Special Issue Editor

Department of Industrial Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
Interests: laser sensors for application and diagnostics in environmental science; detection and identification of chemical/biological agents in CBRNe events; diagnostics for Fusion Reactors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Laser-based remote sensing techniques are very promising methodologies. They have become important, sometimes primary, devices in industrial, urban, environmental, safety, and security applications. Concerning the safety and security field, remote sensing monitoring plays a crucial role in providing fast and preventive alarms in the case of intentional (terrorism, war, etc.) or accidental (natural, accident) diffusions of dangerous substances, such chemicals or pathogens. Moreover, remote sensing approaches may avoid people working directly in threatening areas and it may help understand the dangers and to take appropriate countermeasures.

Pollution monitoring is also fundamental to preserve and guarantee a good quality of life, especially in industrial and high-traffic urban areas.

Although many remote laser-based techniques have been developed, such as Lidar, Dial, Laser-Induced Fluorescence (LIF), and Laser-Induced Breakdown Spectroscopy (LIBS), these instruments are usually large, heavy, and expensive, leading to underutilization of these devices. Moreover, these techniques usually require complicated data analysis, since they work in very variable and unpredictable environments.

This Special Issue refers to any research in the field of laser-based remote sensing applied to environmental, safety, and security fields. It will accept both original research and review articles about not only the techniques, but also innovative experimental apparatus or devices, and new data analysis techniques.

Dr. Pasqualino Gaudio
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • Lidar
  • Dial
  • LIBS
  • optical techniques for environmental science
  • standoff detection of chemical and biological agents
  • laser-based detection systems
  • laser identification chemical and biological agent

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Other

20 pages, 6727 KiB  
Article
Study of the Vertical Structure of the Coastal Boundary Layer Integrating Surface Measurements and Ground-Based Remote Sensing
by Teresa Lo Feudo, Claudia Roberta Calidonna, Elenio Avolio and Anna Maria Sempreviva
Sensors 2020, 20(22), 6516; https://doi.org/10.3390/s20226516 - 14 Nov 2020
Cited by 10 | Viewed by 1896
Abstract
The understanding of the atmospheric processes in coastal areas requires the availability of quality datasets describing the vertical and horizontal spatial structure of the Atmospheric Boundary Layer (ABL) on either side of the coastline. High-resolution Numerical Weather Prediction (NWP) models can provide this [...] Read more.
The understanding of the atmospheric processes in coastal areas requires the availability of quality datasets describing the vertical and horizontal spatial structure of the Atmospheric Boundary Layer (ABL) on either side of the coastline. High-resolution Numerical Weather Prediction (NWP) models can provide this information and the main ingredients for good simulations are: an accurate description of the coastline and a correct subgrid process parametrization permitting coastline discontinuities to be caught. To provide an as comprehensive as possible dataset on Mediterranean coastal area, an intensive experimental campaign was realized at a near-shore Italian site, using optical and acoustic ground-based remote sensing and surface instruments, under different weather characteristic and stability conditions; the campaign is also fully simulated by a NWP model. Integrating information from instruments responding to different atmospheric properties allowed for an explanation of the development of various patterns in the vertical structure of the atmosphere. Wind LiDAR measurements provided information of the internal boundary layer from the value of maximum height reached by the wind profile; a height between 80 and 130 m is often detected as an interface between two different layers. The NWP model was able to simulate the vertical wind profiles and the eight of the ABL. Full article
Show Figures

Figure 1

16 pages, 5897 KiB  
Article
Vision-Based Safety-Related Sensors in Low Visibility by Fog
by Bong Keun Kim and Yasushi Sumi
Sensors 2020, 20(10), 2812; https://doi.org/10.3390/s20102812 - 15 May 2020
Cited by 7 | Viewed by 3030
Abstract
Mobile service robots are expanding their use to outdoor areas affected by various weather conditions, but the outdoor environment directly affects the functional safety of robots implemented by vision-based safety-related sensors (SRSs). Therefore, this paper aims to set the fog as the environmental [...] Read more.
Mobile service robots are expanding their use to outdoor areas affected by various weather conditions, but the outdoor environment directly affects the functional safety of robots implemented by vision-based safety-related sensors (SRSs). Therefore, this paper aims to set the fog as the environmental condition of the robot and to understand the relationship between the quantified value of the environmental conditions and the functional safety performance of the robot. To this end, the safety functions of the robot built using SRS and the requirements for the outdoor environment affecting them are described first. The method of controlling visibility for evaluating the safety function of SRS is described through the measurement and control of visibility, a quantitative means of expressing the concentration of fog, and wavelength analysis of various SRS light sources. Finally, object recognition experiments using vision-based SRS for robots are conducted at low visibility. Through this, it is verified that the proposed method is a specific and effective method for verifying the functional safety of the robot using the vision-based SRS, for low visibility environmental requirements. Full article
Show Figures

Figure 1

10 pages, 354 KiB  
Article
Multispectral LIF-Based Standoff Detection System for the Classification of CBE Hazards by Spectral and Temporal Features
by Lea Fellner, Marian Kraus, Florian Gebert, Arne Walter and Frank Duschek
Sensors 2020, 20(9), 2524; https://doi.org/10.3390/s20092524 - 29 Apr 2020
Cited by 7 | Viewed by 3149
Abstract
Laser-induced fluorescence (LIF) is a well-established technique for monitoring chemical processes and for the standoff detection of biological substances because of its simple technical implementation and high sensitivity. Frequently, standoff LIF spectra from large molecules and bio-agents are only slightly structured and a [...] Read more.
Laser-induced fluorescence (LIF) is a well-established technique for monitoring chemical processes and for the standoff detection of biological substances because of its simple technical implementation and high sensitivity. Frequently, standoff LIF spectra from large molecules and bio-agents are only slightly structured and a gain of deeper information, such as classification, let alone identification, might become challenging. Improving the LIF technology by recording spectral and additionally time-resolved fluorescence emission, a significant gain of information can be achieved. This work presents results from a LIF based detection system and an analysis of the influence of time-resolved data on the classification accuracy. A multi-wavelength sub-nanosecond laser source is used to acquire spectral and time-resolved data from a standoff distance of 3.5 m. The data set contains data from seven different bacterial species and six types of oil. Classification is performed with a decision tree algorithm separately for spectral data, time-resolved data and the combination of both. The first findings show a valuable contribution of time-resolved fluorescence data to the classification of the investigated chemical and biological agents to their species level. Temporal and spectral data have been proven as partly complementary. The classification accuracy is increased from 86% for spectral data only to more than 92%. Full article
Show Figures

Figure 1

12 pages, 3730 KiB  
Article
High Repetition Rate Mid-Infrared Differential Absorption Lidar for Atmospheric Pollution Detection
by Yu Gong, Lingbing Bu, Bin Yang and Farhan Mustafa
Sensors 2020, 20(8), 2211; https://doi.org/10.3390/s20082211 - 14 Apr 2020
Cited by 35 | Viewed by 3849
Abstract
Developments in mid-infrared Differential Absorption Lidar (DIAL), for gas remote sensing, have received a significant amount of research in recent years. In this paper, a high repetition rate tunable mid-infrared DIAL, mounted on a mobile platform, has been built for long range remote [...] Read more.
Developments in mid-infrared Differential Absorption Lidar (DIAL), for gas remote sensing, have received a significant amount of research in recent years. In this paper, a high repetition rate tunable mid-infrared DIAL, mounted on a mobile platform, has been built for long range remote detection of gas plumes. The lidar uses a solid-state tunable optical parametric oscillator laser, which can emit laser pulse with repetition rate of 500 Hz and between the band from 2.5 μm to 4 μm. A monitoring channel has been used to record the laser energy in real-time and correct signals. Convolution correction technology has also been incorporated to choose the laser wavelengths. Taking NO2 and SO2 as examples, lidar system calibration experiment and open field observation experiment have been carried out. The observation results show that the minimum detection sensitivity of NO2 and SO2 can reach 0.07 mg/m3, and 0.31 mg/m3, respectively. The effective temporal resolution can reach second level for the high repetition rate of the laser, which demonstrates that the system can be used for the real-time remote sensing of atmospheric pollution gas. Full article
Show Figures

Figure 1

Other

Jump to: Research

11 pages, 2337 KiB  
Letter
Adaptive Quasi-Unsupervised Detection of Smoke Plume by LiDAR
by Riccardo Rossi, Michela Gelfusa, Andrea Malizia and Pasqualino Gaudio
Sensors 2020, 20(22), 6602; https://doi.org/10.3390/s20226602 - 18 Nov 2020
Cited by 4 | Viewed by 2507
Abstract
The early detection of fire is one of the possible applications of LiDAR techniques. The smoke generated by a fire is mainly compounded of CO2, H2O, particulate, and other combustion products, which involve the local variation of the scattering [...] Read more.
The early detection of fire is one of the possible applications of LiDAR techniques. The smoke generated by a fire is mainly compounded of CO2, H2O, particulate, and other combustion products, which involve the local variation of the scattering of the electromagnetic wave at specific wavelengths. The increases of the backscattering coefficient are transduced in peaks on the signal of the backscattering power recorded by the LiDAR system, located exactly where the smoke plume is, allowing not only the detection of a fire but also its localization. The signal processing of the LiDAR signals is critical in the determination of the performances of the fire detection. It is important that the sensitivity of the apparatus is high enough but also that the number of false alarms is small, in order to avoid the trigger of useless and expensive countermeasures. In this work, a new analysis method, based on an adaptive quasi-unsupervised approach was used to ensure that the algorithm is continuously updated to the boundary conditions of the system, such as the weather and experimental apparatus issues. The method has been tested on an experimental campaign of 227 pulses and the performances have been analyzed in terms of sensitivity and specificity. Full article
Show Figures

Figure 1

Back to TopTop