Special Issue "State-of-the-Art Remote Sensing Technologies for Environmental Monitoring"

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

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Dr. Monica Rivas Casado
Website
Guest Editor
School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire, MK430AL, UK
Interests: unmanned aerial vehicles; monitoring; ecological modelling; freshwater ecosystems; statistics; environmental engineering; robotics and autonomous systems
Special Issues and Collections in MDPI journals
Dr. Marco Palma
Website
Co-Guest Editor
1) Dipartimento di Scienze della Vita e dell’Ambiente, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, 60131, Italy
2) Habitats Edge Ltd 39 High Street, MK416AG, Bedford, UK
Interests: underwater photogrammetry; marine habitat monitoring and restoration; environmental accounting; taxonomy; innovative technologies
Prof. Dr. Paul Leinster
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 and Collections 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 papers will be 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 2400 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 (4 papers)

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

Research

Jump to: Other

Open AccessArticle
Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method
Remote Sens. 2020, 12(12), 1937; https://doi.org/10.3390/rs12121937 - 15 Jun 2020
Cited by 1 | Viewed by 803
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 [...] Read more.
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
Show Figures

Figure 1

Open AccessFeature PaperArticle
Combining Unmanned Aircraft Systems and Image Processing for Wastewater Treatment Plant Asset Inspection
Remote Sens. 2020, 12(9), 1461; https://doi.org/10.3390/rs12091461 - 05 May 2020
Viewed by 943
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 [...] Read more.
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
Show Figures

Graphical abstract

Open AccessArticle
Quantifying Coral Reef Composition of Recreational Diving Sites: A Structure from Motion Approach at Seascape Scale
Remote Sens. 2019, 11(24), 3027; https://doi.org/10.3390/rs11243027 - 16 Dec 2019
Cited by 3 | Viewed by 1029
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 [...] Read more.
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
Show Figures

Graphical abstract

Other

Jump to: Research

Open AccessLetter
Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data
Remote Sens. 2020, 12(6), 1033; https://doi.org/10.3390/rs12061033 - 23 Mar 2020
Viewed by 631
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 [...] Read more.
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
Show Figures

Graphical abstract

Back to TopTop