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State-of-the-Art Remote Sensing Technologies for Environmental Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 20395

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Special Issue Editors

Dr. Monica Rivas Casado

E-Mail Website
Guest Editor

School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK430AL, UK
Interests: surface water flooding; standardised monitoring approaches; systems engineering; disruptive technologies; climate change; extreme events
Special Issues, Collections and Topics in MDPI journals

Dr. Marco Palma

E-Mail Website
Co-Guest Editor

1. Dipartimento di Scienze della Vita e dell’Ambiente, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy
2. Habitats Edge Ltd, 39 High Street, Bedford MK416AG, UK
Interests: underwater photogrammetry; marine habitat monitoring and restoration; environmental accounting; taxonomy; innovative technologies

Prof. Dr. Paul Leinster

E-Mail Website
Co-Guest Editor

School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK430AL, UK
Interests: environmental policy; environmental regulation; sustainability; governance; monitoring; natural capital; ecosystem services; risk assessment; emergency response; systems-based approaches; operationalizing research findings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Current technological advances in remote sensing are proving to be key engineering tools for environmental surveying tasks. The range of available technologies is wide and varied, and includes unmanned aerial systems, semi-autonomous and autonomous boats, autonomous underwater vehicles and remotely operated vehicles, amongst others. Similarly, their applications have expanded across different environmental domains, from atmospheric measurements to coral reef characterization. The uptake of these technologies has enabled increased data quality (accuracy) and quantity (coverage), which necessitates the use and development of advanced mathematical and statistical methods for data analysis and interpretation. This Special Issue aims to collate manuscripts showcasing recent applications of novel remote sensing technological advances within the context of environmental monitoring. Manuscripts can be related to any aspects of remote sensing techniques used for environmental assessment, characterization, and protection. Of special interest are those manuscripts covering the integrated use of state-of-the art remote sensing technology for environmental data capture and advanced statistical methods for data analysis and interpretation. The following topics will be considered for this Special Issue:

Subtopics:

Robots and autonomous systems for environmental remote sensing;
Emerging technologies for environmental remote sensing;
Holistic and integrated approaches for remote sensing data collection;
Novel advances in remote sensing for the collection of collocated spatio-temporal data;
Technological solutions for high-resolution wide-area data collection;
Industrial- and regulatory-based applications of monitoring environmental processes
Remote sensing solutions to unbiased environmental monitoring;
Uncertainty and accuracy of remote sensing techniques for environmental assessment;
Comparison of novel and traditional remote sensing methods for environmental monitoring;
Data fusion solutions for enhanced environmental characterization;
Optimization of monitoring/sampling programs for environmental mapping, assessment, and characterization;
Technological tools and solutions to map extreme environmental events and their impact;
Increased environmental change detection through novel remote sensing technologies;
Identification of advantages and limitations of novel remote sensing methods via applied environmental examples.

Dr. Monica Rivas Casado
Dr. Marco Palma
Professor Paul Leinster CBE
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. Remote Sensing 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 2700 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

emerging technologies
robots
autonomous systems
environmental assessment
advanced statistics
data analysis
unmanned aerial systems
autonomous underwater vehicles
remotely operated vehicles

Published Papers (6 papers)

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Research

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28 pages, 19785 KiB

Open AccessArticle

Development of a UAV Based Framework for CH₄ Monitoring in Sludge Treatment Centres

by Hiniduma Gamage Kavindi Abeywickrama, Yadira Bajón-Fernández, Bharanitharan Srinamasivayam, Duncan Turner and Mónica Rivas Casado

Remote Sens. 2023, 15(15), 3704; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15153704 - 25 Jul 2023

Viewed by 1797

Abstract

With the increasing trend in the global average temperature, the UK’s water industry has committed to achieve Net Zero by 2030 and part of this includes cutting CH₄ emissions from sludge treatment facilities. Currently, emissions are estimated following the carbon accounting workbook guidelines and using default emission factors. However, this method might not be a true representation of emissions as these vary depending on many factors. The use of unmanned aerial vehicles (UAVs) has proved cost effective for environmental monitoring tasks requiring high spatial resolution information. Within the context of CH₄ emissions and in the last decade, the technology has been curtailed by sensor weight and size. Recent advances in sensor technology have enabled the development of a fit-for purpose UAV CH₄ sensor (U10) which uses Tuneable Diode Laser Absorption Spectroscopy. This study intends to develop a framework for CH₄ data collection strategies from sludge treatment centres using UAV-U10 technology and asset level CH₄ enhancement estimations based on geostatistical interpolation techniques and the mass balance approach. The framework presented here enables the characterization of spatial and temporal variations in CH₄ concentrations. It promotes asset level CH₄ enhancement estimation based on on-site measurements. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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Graphical abstract

20 pages, 4886 KiB

Open AccessEditor’s ChoiceArticle

Accuracy Assessment of Surveying Strategies for the Characterization of Microtopographic Features That Influence Surface Water Flooding

by Rakhee Ramachandran, Yadira Bajón Fernández, Ian Truckell, Carlos Constantino, Richard Casselden, Paul Leinster and Mónica Rivas Casado

Remote Sens. 2023, 15(7), 1912; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15071912 - 02 Apr 2023

Cited by 1 | Viewed by 2052

Abstract

With the increase in rainfall intensity, population, and urbanised areas, surface water flooding (SWF) is an increasing concern impacting properties, businesses, and human lives. Previous studies have shown that microtopography significantly influences flow paths, flow direction, and velocity, impacting flood extent and depth, particularly for the shallow flow associated with urban SWF. This study compares two survey strategies commonly used by flood practitioners, S1 (using Unmanned Aerial Systems-based RGB data) and S2 (using manned aircraft with LiDAR scanners), to develop guidelines on where to use each strategy to better characterise microtopography for a range of flood features. The difference between S1 and S2 in elevation and their accuracies were assessed using both traditional and robust statistical measures. The results showed that the difference in elevation between S1 and S2 varies between 11 cm and 37 cm on different land use and microtopographic flood features. Similarly, the accuracy of S1 ranges between 3 cm and 70 cm, and the accuracy of S2 ranges between 3.8 cm and 30.3 cm on different microtopographic flood features. Thus, this study suggests that the flood features of interest in any given flood study would be key to select the most suitable survey strategy. A decision framework was developed to inform data collection and integration of the two surveying strategies to better characterise microtopographic features. The findings from this study will help improve the microtopographic representation of flood features in flood models and, thus, increase the ability to identify high flood-risk prompt areas accurately. It would also help manage and maintain drainage assets, spatial planning of sustainable drainage systems, and property level flood resilience and insurance to better adapt to the effects of climate change. This study is another step towards standardising flood extent and impact surveying strategies. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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Graphical abstract

19 pages, 7174 KiB

Open AccessArticle

Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method

by Mengjiao Qin, Linshu Hu, Zhenhong Du, Yi Gao, Lianjie Qin, Feng Zhang and Renyi Liu

Remote Sens. 2020, 12(12), 1937; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12121937 - 15 Jun 2020

Cited by 11 | Viewed by 3176

Abstract

Lakes have been identified as an important indicator of climate change and a finer lake area can better reflect the changes. In this paper, we propose an effective unsupervised deep gradient network (UDGN) to generate a higher resolution lake area from remote sensing images. By exploiting the power of deep learning, UDGN models the internal recurrence of information inside the single image and its corresponding gradient map to generate images with higher spatial resolution. The gradient map is derived from the input image to provide important geographical information. Since the training samples are only extracted from the input image, UDGN can adapt to different settings per image. Based on the superior adaptability of the UDGN model, two strategies are proposed for super-resolution (SR) mapping of lakes from multispectral remote sensing images. Finally, Landsat 8 and MODIS (moderate-resolution imaging spectroradiometer) images from two study areas on the Tibetan Plateau in China were used to evaluate the performance of UDGN. Compared with four unsupervised SR methods, UDGN obtained the best SR results as well as lake extraction results in terms of both quantitative and visual aspects. The experiments prove that our approach provides a promising way to break through the limitations of median-low resolution remote sensing images in lake change monitoring, and ultimately support finer lake applications. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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14 pages, 2707 KiB

Open AccessFeature PaperArticle

Combining Unmanned Aircraft Systems and Image Processing for Wastewater Treatment Plant Asset Inspection

by Jorge Sancho Martínez, Yadira Bajón Fernández, Paul Leinster and Mónica Rivas Casado

Remote Sens. 2020, 12(9), 1461; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091461 - 05 May 2020

Cited by 4 | Viewed by 3962

Abstract

Wastewater treatment plants are essential for preserving the water quality of freshwater and marine ecosystems. It is estimated that, in the UK, as much as 11 billion liters of wastewater are treated on a daily basis. Effective and efficient treatment of wastewater requires treatment plants to be maintained in good condition. Recent studies have highlighted the potential of unmanned aircraft systems (UASs) and image processing to be used in autonomous and automated monitoring systems. However, the combined use of UASs and image processing for wastewater treatment plant inspections has not yet been tested. This paper presents a novel image processing-UAS framework for the identification of failures in trickling filters and activated sludge facilities. The results show that the proposed framework has an accuracy of 95% in the detection of failures in activated sludge assets, with the accuracy ranging between 55% and 81% for trickling filters. These results are promising and they highlight the potential use of the technology for the inspection of wastewater treatment plants. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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Graphical abstract

21 pages, 3193 KiB

Open AccessArticle

Quantifying Coral Reef Composition of Recreational Diving Sites: A Structure from Motion Approach at Seascape Scale

by Marco Palma, Chiara Magliozzi, Monica Rivas Casado, Ubaldo Pantaleo, João Fernandes, Gianpaolo Coro, Carlo Cerrano and Paul Leinster

Remote Sens. 2019, 11(24), 3027; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11243027 - 16 Dec 2019

Cited by 12 | Viewed by 4637

Abstract

Recreational diving is known to have both direct and indirect impacts on coral habitats. Direct impacts include increasing sedimentation, breaks and diseases that lead to a decrease in the richness and abundances of hard corals. Indirect impacts include urban development, land management and sewage disposal. The ecological effects of scuba diving on the spatial composition metrics of reef benthic communities are less well studied, and they have not been investigated at seascape scale. In this study, we combine orthomosaics derived from Structure from Motion (SfM) photogrammetry and data-mining techniques to study the spatial composition of reef benthic communities of recreational diving sites at seascape scale (>25 m

^{2}

). The study focuses on the case study area of Ponta do Ouro Partial Marine Reserve (Mozambique). Results showed that scuba-diving resistant taxa (i.e., sponges and algae) were abundant at small (>850 m

^{2}

) and highly dived sites (>3000 dives yr

^{- 1}

), characterized by low diversity and density, and big organisms with complex shapes. Fragile taxa (i.e., Acropora spp.) were abundant at low (365 dives yr

^{- 1}

) and moderately dived sites (1000–3000 dives yr

^{- 1}

) where the greater depth and wider coral reef surfaces attenuate the abrasive effect of waves and re-suspended sediments. Highest taxa diversity and density, and lowest abundance of resistant taxa were recorded at large (>2000 m

^{2}

) and rarely dived sites. This study highlights the potential applications for a photogrammetric approach to support monitoring programs at Ponta do Ouro Partial Marine Reserve (Mozambique), and provides some insight to understand the influence of scuba diving on benthic communities. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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Other

Jump to: Research

12 pages, 2925 KiB

Open AccessLetter

Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data

by Yasuhiro Tanaka

Remote Sens. 2020, 12(6), 1033; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12061033 - 23 Mar 2020

Cited by 2 | Viewed by 2395

Abstract

Meltwater drainage onset (DO) timing and drainage duration (DD) related to snowmelt-water redistribution are both important for understanding not only the Arctic energy and heat budgets but also the salt/heat balance of the mixed layer in the ocean and sea-ice ecosystem. We present DO and DD as determined from the time series of Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) melt pond fraction (MPF) estimates in an area with Canadian landfast ice. To address the lack of evaluation on a day-by-day basis for the AMSR-E MPF estimate, we first compared AMSR-E MPF with the daily Medium Resolution Imaging Spectrometer (MERIS) MPF. The AMSR-E MPF estimate correlates significantly with the MERIS MPF (r = 0.73–0.83). The estimate has a product quality similar to the MERIS MPF only when the albedo is around 0.5–0.7 and a positive bias of up to 10% in areas with an albedo of 0.7–0.9, including melting snow. The DO/DD estimates are determined by using a polynomial regression curve fitted on the time series of the AMSR-E MPF. The DOs/DDs from time series of the AMSR-E and MERIS MPFs are compared, revealing consistency in both DD and DO. The DO timing from 2006 to 2011 is correlated with melt onset timing. To the best of our knowledge, our study provides the first large-scale information on both DO timing and DD. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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