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

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

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 20610

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Special Issue Editors

Dr. Pasqualino Gaudio

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Guest 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

Dr. Riccardo Rossi

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Guest Editor

Department of Industrial Engineering, University of Rome “Tor Vergata”, Via Del Politecnico 1, 00133 Roma, Italy
Interests: nuclear fusion; plasma diagnostics; machine learning; causality detection; laser based diagnostics

Special Issue Information

Dear Colleagues,

Laser-based remote sensing techniques are very promising methodologies, having 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 (or natural) diffusions of dangerous substances, such as chemicals or pathogens. Moreover, remote sensing approaches may prevent people working directly in threatening areas, help understand the dangers involved and take appropriate countermeasures.

Pollution monitoring is also fundamental to the preservation and guarantee of 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 the underutilization of these devices. Moreover, these techniques usually require a 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, accepting both original research and review articles regarding not only the techniques, but also innovative experimental apparatus or devices and new data analysis techniques.

Dr. Pasqualino Gaudio
Dr. Riccardo Rossi
Guest Editors

Manuscript Submission Information

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Keywords

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

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Published Papers (7 papers)

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Research

12 pages, 6304 KiB

Open AccessCommunication

Real-Time Walk Error Compensation Method Using Echo Signal Magnitude Measurement in ToF Laser Scanners

by Bartosz Sędek, Marek Zygmunt, Marcin Jakubaszek, Tadeusz Drozd and Jacek Wojtanowski

Sensors 2024, 24(3), 733; https://doi.org/10.3390/s24030733 - 23 Jan 2024

Cited by 1 | Viewed by 1545

Abstract

The rapid advancement of mobile laser scanner technology used for terrain mapping, among other things, imposes increasing requirements for scanning frequency and distance measurement accuracy. To meet these requirements, rangefinder modules are expected to operate with high echo signal dynamics and to allow accurate distance measurement even based on single-laser-pulse echo detection. Such performance can be potentially achieved using pulsed time-of-flight (ToF) laser rangefinders (LRF). In conventional ToF modules, however, the STOP signal (for time counter interruption) is generated using a straightforward fixed-threshold comparator method. Unfortunately, it corresponds to the so-called walk error, i.e., the dependence of the measured time of flight on the magnitude of the echo signal. In most ranging applications, however, the LRF detection channel can be exposed to an extremely large span of received echo power levels, which depend on the distance measured, type of target surface, atmospheric transmission, etc. Thus, the walk error is an inseparable element of the conventional ToF technique and creates a fundamental limit for its precision. This article presents a novel method of walk error compensation in real time. By using our authorial electronic circuit for measuring the magnitude of the echo signal, it is possible to effectively compensate for the walk error even when the echo signal brings the detection channel amplifiers into saturation. In addition, the paper presents a laboratory method for calibrating the walk error compensation curve. Full article

(This article belongs to the Special Issue Laser Based Remote Sensors for Environmental Science: Measurements and Analysis Techniques)

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21 pages, 9568 KiB

Open AccessArticle

Signal Photon Extraction Method for ICESat-2 Data Using Slope and Elevation Information Provided by Stereo Images

by Linyu Gu, Dazhao Fan, Song Ji, Zhihui Gong, Dongzi Li and Yang Dong

Sensors 2023, 23(21), 8752; https://doi.org/10.3390/s23218752 - 27 Oct 2023

Cited by 3 | Viewed by 1307

Abstract

Laser altimetry data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) contain a lot of noise, which necessitates the requirement for a signal photon extraction method. In this study, we propose a density clustering method, which combines slope and elevation information from optical stereo images and adaptively adjusts the neighborhood search direction in the along-track direction. The local classification density threshold was calculated adaptively according to the uneven spatial distribution of noise and signal density, and reliable surface signal points were extracted. The performance of the algorithm was validated for strong and weak beam laser altimetry data using optical stereo images with different resolutions and positioning accuracies. The results were compared qualitatively and quantitatively with those obtained using the ATL08 algorithm. The signal extraction quality was better than that of the ATL08 algorithm for steep slope and low signal-to-noise ratio (SNR) regions. The proposed method can better balance the relationship between recall and precision, and its F₁-score was higher than that of the ATL08 algorithm. The method can accurately extract continuous and reliable surface signals for both strong and weak beams among different terrains and land cover types. Full article

(This article belongs to the Special Issue Laser Based Remote Sensors for Environmental Science: Measurements and Analysis Techniques)

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16 pages, 11094 KiB

Open AccessArticle

ICESat-2 for Canopy Cover Estimation at Large-Scale on a Cloud-Based Platform

by Emre Akturk, Sorin C. Popescu and Lonesome Malambo

Sensors 2023, 23(7), 3394; https://doi.org/10.3390/s23073394 - 23 Mar 2023

Cited by 7 | Viewed by 3365

Abstract

Forest canopy cover is an essential biophysical parameter of ecological significance, especially for characterizing woodlands and forests. This research focused on using data from the ICESat-2/ATLAS spaceborne lidar sensor, a photon-counting altimetry system, to map the forest canopy cover over a large country extent. The study proposed a novel approach to compute categorized canopy cover using photon-counting data and available ancillary Landsat images to build the canopy cover model. In addition, this research tested a cloud-mapping platform, the Google Earth Engine (GEE), as an example of a large-scale study. The canopy cover map of the Republic of Türkiye produced from this study has an average accuracy of over 70%. Even though the results were promising, it has been determined that the issues caused by the auxiliary data negatively affect the overall success. Moreover, while GEE offered many benefits, such as user-friendliness and convenience, it had processing limits that posed challenges for large-scale studies. Using weak or strong beams’ segments separately did not show a significant difference in estimating canopy cover. Briefly, this study demonstrates the potential of using photon-counting data and GEE for mapping forest canopy cover at a large scale. Full article

(This article belongs to the Special Issue Laser Based Remote Sensors for Environmental Science: Measurements and Analysis Techniques)

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16 pages, 6263 KiB

Open AccessArticle

Retrieval of Suspended Sediment Concentration from Bathymetric Bias of Airborne LiDAR

by Xinglei Zhao, Jianfei Gao, Hui Xia and Fengnian Zhou

Sensors 2022, 22(24), 10005; https://doi.org/10.3390/s222410005 - 19 Dec 2022

Cited by 2 | Viewed by 2126

Abstract

In addition to depth measurements, airborne LiDAR bathymetry (ALB) has shown usefulness in suspended sediment concentration (SSC) inversion. However, SSC retrieval using ALB based on waveform decomposition or near-water-surface penetration by green lasers requires access to full-waveform data or infrared laser data, which are not always available for users. Thus, in this study we propose a new SSC inversion method based on the depth bias of ALB. Artificial neural networks were used to build an empirical inversion model by connecting the depth bias and SSC. The proposed method was verified using an ALB dataset collected through Optech coastal zone mapping and imaging LiDAR systems. The results showed that the mean square error of the predicted SSC based on the empirical model of ALB depth bias was less than 2.564 mg/L in the experimental area. The proposed method was compared with the waveform decomposition and regression methods. The advantages and limits of the proposed method were analyzed and summarized. The proposed method can effectively retrieve SSC and only requires ALB-derived and sonar-derived water bottom points, eliminating the dependence on the use of green full-waveforms and infrared lasers. This study provides an alternative means of conducting SSC inversion using ALB. Full article

(This article belongs to the Special Issue Laser Based Remote Sensors for Environmental Science: Measurements and Analysis Techniques)

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17 pages, 4962 KiB

Open AccessArticle

Fast Detection of Different Water Contaminants by Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy

by Salvatore Almaviva, Florinda Artuso, Isabella Giardina, Antonia Lai and Alessandra Pasquo

Sensors 2022, 22(21), 8338; https://doi.org/10.3390/s22218338 - 30 Oct 2022

Cited by 22 | Viewed by 6838

Abstract

Fast monitoring of water quality is a fundamental part of environmental management and protection, in particular, the possibility of qualitatively and quantitatively determining its contamination at levels that are dangerous for human health, fauna and flora. Among the techniques currently available, Raman spectroscopy and its variant, Surface-Enhanced Raman Spectroscopy (SERS), have several advantages, including no need for sample preparation, quick and easy operation and the ability to operate on the field. This article describes the application of the Raman and SERS technique to liquid samples contaminated with different classes of substances, including nitrates, phosphates, pesticides and their metabolites. The technique was also used for the detection of the air pollutant polycyclic aromatic hydrocarbons and, in particular, benzo(a)pyrene, considered as a reference for the carcinogenicity of the whole class of these compounds. To pre-concentrate the analytes, we applied a methodology based on the well-known coffee-ring effect, which ensures preconcentration of the analytes without any pretreatment of the sample, providing a versatile approach for fast and in-situ detection of water pollutants. The obtained results allowed us to reveal these analytes at low concentrations, close to or lower than their regulatory limits. Full article

(This article belongs to the Special Issue Laser Based Remote Sensors for Environmental Science: Measurements and Analysis Techniques)

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14 pages, 6830 KiB

Open AccessArticle

Setup and Analysis of a Mid-Infrared Stand-Off System to Detect Traces of Explosives on Fabrics

by Lisa B. Dreier, Christoph Kölbl, Vincent Jeuk, Claudia Beleites, Anja Köhntopp and Frank Duschek

Sensors 2022, 22(20), 7839; https://doi.org/10.3390/s22207839 - 15 Oct 2022

Cited by 2 | Viewed by 2233

Abstract

The increasing number of terrorist attacks within the last decade has demonstrated that taking preventive protective measures is highly important. In addition to existing measures, automated detection systems for fast and reliable explosive detection are required. A sensitive spectroscopic system based on mid-infrared spectroscopy has been developed and applied to explosive samples on different types of fabric under various geometric conditions. Using this system, traces of TNT, RDX, PETN and ammonium nitrate can be detected in less than a second. Various approaches for data pretreatment (wavelength calibration) and subsequent analysis (normalization, removal of atmospheric water absorption lines) are presented and the remaining challenges on the road to a fully automated system, including a robust classification algorithm, are discussed. Full article

(This article belongs to the Special Issue Laser Based Remote Sensors for Environmental Science: Measurements and Analysis Techniques)

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16 pages, 7526 KiB

Open AccessArticle

A Weighted-LSM Method to Improve Classification and Concentration Evaluation from Laser-Induced Fluorescence Spectra

by Valentina Gabbarini, Alessandro Puleio, Riccardo Rossi, Andrea Malizia and Pasqualino Gaudio

Sensors 2022, 22(20), 7721; https://doi.org/10.3390/s22207721 - 11 Oct 2022

Cited by 1 | Viewed by 1947

Abstract

The detection of biological agents using optical systems is an open field of research. Currently, different spectroscopic techniques allow to detect and classify chemical agents while a fast and accurate technique able to identify biological agents is still under investigation. Some optical techniques, such as Laser-Induced Breakdown Spectroscopy (LIBS) or Laser-Induced Fluorescence (LIF), are already used as classification methods. However, the presence of background, spectrum similarities and other confounders make these techniques not very specific. This work shows a new method to achieve better performances in terms of classification and concentration evaluations. The method is based on the Weighted Least Square Minimization method. In fact, by using ad hoc weights, the LSM looks at specific features of the spectra, resulting in higher accuracy. In order to make a systematic analysis, numerical tests have been conducted. With these tests, the authors were able to highlight the various advantages and drawbacks of the new methodology proposed. Then, the method was applied to some LIF measurements to investigate the applicability of the method to preliminary experimental cases. The results show that, by using this new weighted LSM, it is possible to achieve better classification and concentration evaluation performances. Finally, the possible application of the new method is discussed. Full article

(This article belongs to the Special Issue Laser Based Remote Sensors for Environmental Science: Measurements and Analysis Techniques)

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