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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 10591

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Special Issue Editor

Dr. Ann Morrison

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Guest Editor

Associate Professor, School of Future Environments, Auckland University of Technology, Auckland, New Zealand
Interests: animal computer interaction; tangible computing; assistive computing; nature; conservation; animals; futures thinking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Extreme environment research is critical for understanding the impacts of climate change, assessing ecosystem resilience, ensuring human safety and adaptability, and advancing planetary exploration. This research provides valuable insights into the effects of climate change on extreme environments, facilitating the development of effective mitigation and adaptation strategies. By studying these environments, researchers gain a deeper understanding of unique ecosystems’ resilience and can develop strategies to conserve and manage them. Additionally, investigating extreme environments helps ensure the safety of humans in these challenging conditions and informs the development of technologies and guidelines for protection.

This Special Issue aims to collate original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the deployment of sensors in extreme environments.

Potential topics include, but are not limited to:

Climate change impacts in polar regions and sensors for temperature, ice dynamics, and glacial retreat.
Desert adaptations and sensors for temperature, humidity, and soil moisture.
High-altitude physiology and sensors for oxygen levels and atmospheric pressure.
Deep sea biodiversity and sensors for temperature, pressure, salinity, and chemical composition.
Underwater monitoring and sensors for currents, pH levels, and dissolved oxygen.
Extraterrestrial exploration and sensors for atmospheric composition, radiation levels, and geological properties.
Sensor development for extreme temperature resistance, corrosion resistance, and pressure tolerance.
Sensor networks and data transmission systems for remote monitoring in extreme environments.
Autonomous sensors and robotics for data collection and exploration feasibility in extreme environments.
Sensor data integration for understanding extreme environment dynamics.

Dr. Ann Morrison
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

extreme environments
harsh conditions
challenging terrains
severe climates
unforgiving settings
sensor resilience
extreme sensing

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Published Papers (6 papers)

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Research

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17 pages, 1391 KiB

Open AccessArticle

Optimizing Sensor Data Interpretation via Hybrid Parametric Bootstrapping

by Victor V. Golovko

Sensors 2025, 25(4), 1183; https://doi.org/10.3390/s25041183 - 14 Feb 2025

Viewed by 436

Abstract

The Chalk River Laboratories (CRL) site in Ontario, Canada, has long been a hub for nuclear research, which has resulted in the accumulation of legacy nuclear waste, including radioactive materials such as uranium, plutonium, and other radionuclides. Effective management of this legacy requires [...] Read more.

{}^{235}U

. These assessments are essential for maintaining nuclear criticality safety. This study estimates the upper bounds of

{}^{235}U

concentrations. We investigated the use of a hybrid parametric bootstrapping method and robust statistical techniques to analyze datasets with outliers, then compared these outcomes with those derived from nonparametric bootstrapping. This study underscores the significance of measuring

{}^{235}U

for ensuring safety, conducting environmental monitoring, and adhering to regulatory compliance requirements at nuclear legacy sites. We used publicly accessible

{}^{235}U

data from the Eastern Desert of Egypt to demonstrate the application of these statistical methods to small datasets, providing reliable upper limit estimates that are vital for remediation and decommissioning efforts. This method seeks to enhance the interpretation of sensor data, ultimately supporting safer nuclear waste management practices at legacy sites such as CRL. Full article

(This article belongs to the Special Issue Sensors and Extreme Environments)

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17 pages, 1852 KiB

Open AccessArticle

Sông Sài Gòn: Extreme Plastic Pollution Pathways in Riparian Waterways

by Peter Cleveland and Ann Morrison

Sensors 2025, 25(3), 937; https://doi.org/10.3390/s25030937 - 4 Feb 2025

Viewed by 841

Abstract

Plastic pollution in waterways poses a significant global challenge, largely stemming from land-based sources and subsequently transported by rivers to marine environments. With a substantial percentage of marine plastic waste originating from land-based sources, comprehending the trajectory and temporal experience of single-use plastic bottles assumes paramount importance. This project designed, developed, and released a plastic pollution tracking device, coinciding with Vietnam’s annual Plastic Awareness Month. By mapping the plastic tracker’s journey through the Saigon River, this study generated high-fidelity data for comprehensive analysis and bolstered public awareness through regular updates on the Re-Think Plastics Vietnam website. The device, equipped with technologies such as drone flight controller, open-source software, embedded computing, and cellular networking effectively captured GPS position, track, and localized conditions experienced by the plastic bottle tracker on its journey. This amalgamation of data contributes to the understanding of plastic pollution behaviors and serves as a data set for future initiatives aimed at plastic prevention in the ecologically sensitive Mekong Delta. By illuminating the transportation of single-use plastic bottles in the riparian waterways of Ho Chi Minh City and beyond, this study plays a role in collective efforts to understand plastic pollution and preserve aquatic ecosystems. By deploying a GPS-enabled plastic tracker, this study provides novel, high-resolution empirical data on plastic transport in urban tidal systems. These findings contribute to improving waste interception strategies and informing environmental policies aimed at reducing plastic accumulation in critical retention zones. Full article

(This article belongs to the Special Issue Sensors and Extreme Environments)

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26 pages, 3176 KiB

Open AccessArticle

Exploring the Influence of Tropical Cyclones on Regional Air Quality Using Multimodal Deep Learning Techniques

by Muhammad Waqar Younis, Saritha, Bhavya Kallapu, Rama Moorthy Hejamadi, Jeny Jijo, Raghunandan Kemmannu Ramesh , Muhammad Aslam and Syeda Fizzah Jilani

Sensors 2024, 24(21), 6983; https://doi.org/10.3390/s24216983 - 30 Oct 2024

Viewed by 1104

Abstract

Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involvement of TCs in the air quality index (AQI), focusing on aspects related to the air quality before, during and after cyclones. This research employs multimodal methods, which include meteorological data and different satellite observations. Deep learning approaches, i.e., ConvLSTM, CNN and Real-ESRGAN models, are combined with a regression model to analyze the temporal variability in the air quality associated with tropical cyclones. Deep learning models are deployed to uncover complex patterns and non-linear interdependencies between cyclones’ features and the AQI to give predictive insights into the air quality fluctuations throughout the different stages of tropical cyclones. Furthermore, this study explores the aftermaths of TCs in terms of the air quality with respect to post-cyclone recovery. The findings offer an enhanced view of the role of TCs in the regional or global air quality, which will be useful for policymakers, meteorologists and environmental researchers. Utilizing a CNN for tropical cyclone (TC) classification and the extra trees regressor (ETR) for AQI prediction results in accuracy of 92.02% for the CNN and an

R^{2}

of 83.33% for the ETR. Hence, this work adds to our knowledge and enlightens us on the complex interactions between TCs and the air quality, highlighting wider public health concerns regarding climate adaptation and urban renewal. Full article

(This article belongs to the Special Issue Sensors and Extreme Environments)

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30 pages, 74562 KiB

Open AccessArticle

Monitoring of Antarctica’s Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI

by Damini Raniga, Narmilan Amarasingam, Juan Sandino, Ashray Doshi, Johan Barthelemy, Krystal Randall, Sharon A. Robinson, Felipe Gonzalez and Barbara Bollard

Sensors 2024, 24(4), 1063; https://doi.org/10.3390/s24041063 - 6 Feb 2024

Cited by 6 | Viewed by 3533

Abstract

Vegetation in East Antarctica, such as moss and lichen, vulnerable to the effects of climate change and ozone depletion, requires robust non-invasive methods to monitor its health condition. Despite the increasing use of unmanned aerial vehicles (UAVs) to acquire high-resolution data for vegetation analysis in Antarctic regions through artificial intelligence (AI) techniques, the use of multispectral imagery and deep learning (DL) is quite limited. This study addresses this gap with two pivotal contributions: (1) it underscores the potential of deep learning (DL) in a field with notably limited implementations for these datasets; and (2) it introduces an innovative workflow that compares the performance between two supervised machine learning (ML) classifiers: Extreme Gradient Boosting (XGBoost) and U-Net. The proposed workflow is validated by detecting and mapping moss and lichen using data collected in the highly biodiverse Antarctic Specially Protected Area (ASPA) 135, situated near Casey Station, between January and February 2023. The implemented ML models were trained against five classes: Healthy Moss, Stressed Moss, Moribund Moss, Lichen, and Non-vegetated. In the development of the U-Net model, two methods were applied: Method (1) which utilised the original labelled data as those used for XGBoost; and Method (2) which incorporated XGBoost predictions as additional input to that version of U-Net. Results indicate that XGBoost demonstrated robust performance, exceeding 85% in key metrics such as precision, recall, and F1-score. The workflow suggested enhanced accuracy in the classification outputs for U-Net, as Method 2 demonstrated a substantial increase in precision, recall and F1-score compared to Method 1, with notable improvements such as precision for Healthy Moss (Method 2: 94% vs. Method 1: 74%) and recall for Stressed Moss (Method 2: 86% vs. Method 1: 69%). These findings contribute to advancing non-invasive monitoring techniques for the delicate Antarctic ecosystems, showcasing the potential of UAVs, high-resolution multispectral imagery, and ML models in remote sensing applications. Full article

(This article belongs to the Special Issue Sensors and Extreme Environments)

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19 pages, 39258 KiB

Open AccessArticle

Simulation and Experimental Verification of Magnetic Field Diffusion at the Launch Load during Electromagnetic Launch

by Yuxin Yang, Qiang Yin, Changsheng Li, Haojie Li and He Zhang

Sensors 2023, 23(18), 8007; https://doi.org/10.3390/s23188007 - 21 Sep 2023

Cited by 2 | Viewed by 1577

Abstract

The unique magnetic field environment during electromagnetic launch imposes higher requirements on the design and protection of the internal electronic system within the launch load. This low-frequency, Tesla-level extreme magnetic field environment is fundamentally distinct from the Earth’s geomagnetic field. The excessive change rate of magnetic flux can readily induce voltage within the circuit, thus disrupting the normal operation of intelligent microchips. Existing simulation methods primarily focus on the physical environments of rails and armatures, making it challenging to precisely compute the magnetic field environment at the load’s location. In this paper, we propose a computational rail model based on the magneto–mechanical coupling model of a railgun. This model accounts for the dynamic current distribution during the launch process and simulates the magnetic flux density distribution at the load location. To validate the model’s accuracy, three-axis magnetic sensors were placed in front of the armature, and the dynamic magnetic field distribution during the launch process was obtained using the projectile-borne-storage testing method. The results indicate that compared to the previous literature methods, the approach proposed in this paper achieves higher accuracy and is closer to experimental results, providing valuable support for the design and optimization of the launch load. Full article

(This article belongs to the Special Issue Sensors and Extreme Environments)

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Review

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21 pages, 3500 KiB

Open AccessReview

Advancements in Detection and Mitigation Strategies for Petroleum-Derived Contaminants in Aquatic Environments: A Comprehensive Review

by Hugo Duarte, María José Aliaño-González, Anabela Romano and Bruno Medronho

Sensors 2024, 24(11), 3284; https://doi.org/10.3390/s24113284 - 21 May 2024

Cited by 2 | Viewed by 1654

Abstract

The exponential increase in the production and transportation of petroleum-derived products observed in recent years has been driven by the escalating demand for energy, textiles, plastic-based materials, and other goods derived from petroleum. Consequently, there has been a corresponding rise in spills of these petroleum derivatives, particularly in water sources utilized for transportation or, occasionally, illegally utilized for tank cleaning or industrial equipment maintenance. Numerous researchers have proposed highly effective techniques for detecting these products, aiming to facilitate their cleanup or containment and thereby minimize environmental pollution. However, many of these techniques rely on the identification of individual compounds, which presents significant drawbacks, including complexity of handling, subjectivity, lengthy analysis times, infeasibility for in situ analysis, and high costs. In response, there has been a notable surge in the utilization of sensors or generalized profiling techniques serving as sensors to generate characteristic fingerprints of these products, thereby circumventing the aforementioned disadvantages. This review comprehensively examines the evolution of techniques employed for detecting petroleum-derived products in water samples, along with their associated advantages and disadvantages. Furthermore, the review examines current perspectives on methods for the removal and/or containment of these products from water sources, to minimize their environmental impact and the associated health repercussions on living organisms and ecosystems. Full article

(This article belongs to the Special Issue Sensors and Extreme Environments)

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