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Environmental Monitoring Using Satellite Remote Sensing (Second Edition)
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Environmental Monitoring Using Satellite Remote Sensing (Second Edition)

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

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 2577

Special Issue Editors

Dr. Emanuele Mandanici

E-Mail Website
Guest Editor
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Bologna, Italy
Interests: geomatics; remote sensing; change detection; thermography; radiometric calibration; environmental monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Sara Kasmaeeyazdi

E-Mail Website
Guest Editor
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Bologna, Italy
Interests: remote sensing and geostatistical tools in geoscience; multispectral and hyperspectral remote sensing; geostatistical mapping; mining residues
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The sustainable management of the environment is one of the major challenges of the modern era, with the goal of a wise use of the resources, while preserving ecosystems integrity. A deep understanding of the status of the environment and an accurate monitoring of its dynamics, especially in response to anthropogenic actions, are crucial to develop a correct management strategy. In this context, Remote Sensing techniques can provide a major contribution. Indeed, the increasing number of satellite platforms and the enhanced performances of the imaging sensors have been making available an unprecedented amount of information about land and ocean surfaces.

In this perspective, research efforts are needed to develop methods and tools for the integration of platforms and sensors with different spectral, spatial and temporal resolutions. This integration is essential to expand the capabilities of a multi-temporal and multi-scale monitoring of the environment and enlarge the number of applications that may benefit from remote sensing data. Furthermore, the development of best practices to validate the results and predict the accuracy of the proposed approaches is another crucial aspect.

The previous volume of ‘Environmental Monitoring Using Satellite Remote Sensing’, collected valuable applications for eco-environment, water management, urbanization monitoring and land cover, vegetation and ecological quality assessments.  This second Special Issue aims to continue collecting high quality contribution to the advancement of satellite remote sensing technology and solutions for environmental monitoring applications.

Dr. Emanuele Mandanici
Dr. Sara Kasmaeeyazdi
Guest Editors

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

  • environmental monitoring
  • sustainability and resilience
  • LULC mapping
  • change-detection
  • hazard assessment
  • image classification
  • multi and hyperspectral remote sensing
  • multi-sensor integration
  • optical and SAR integration
  • multi-scale analysis
  • time-series analysis
  • satellite imagery calibration
  • validation strategies
  • geostatistical analysis for RS

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Related Special Issue

Published Papers (3 papers)

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Research

18 pages, 7713 KiB  
Article
Water Quality Inversion Framework for Taihu Lake Based on Multilayer Denoising Autoencoder and Ensemble Learning
by Zhihao Sun, Liang Guo, Zhe Tao, Yana Li, Yang Zhan, Shuling Li and Ying Zhao
Remote Sens. 2024, 16(24), 4793; https://doi.org/10.3390/rs16244793 - 23 Dec 2024
Viewed by 489
Abstract
In river and lake ecosystem management, comprehensive water quality monitoring is crucial. Traditional in situ water quality monitoring is costly, and it is challenging to cover entire water bodies. Remote sensing imagery offers the possibility of efficient monitoring of water quality over large [...] Read more.
In river and lake ecosystem management, comprehensive water quality monitoring is crucial. Traditional in situ water quality monitoring is costly, and it is challenging to cover entire water bodies. Remote sensing imagery offers the possibility of efficient monitoring of water quality over large areas. However, remote sensing data typically contain a large amount of noise and redundant information, making it difficult for models to capture the effective spectral information and the relationships in the water quality in the remote sensing data. Consequently, this hinders the achievement of high-precision water quality inversion performance. Therefore, this study proposes a comprehensive water quality inversion framework based on a multilayer denoising autoencoder that automatically extracts effective spectral features, utilizing a multilayer denoising autoencoder to extract effective features from Sentinel-2 remote sensing data, thereby reducing noise in the subsequent model input data and mitigating the overfitting problem in subsequent models. A bagging ensemble learning model was established to invert the total phosphorus concentration in Taihu Lake. This model reduces the prediction bias generated by a single machine learning model and was compared with decision tree, random forest, and linear regression models. The research results indicate that compared to a single model, the bagging ensemble learning model achieved better water quality retrieval results, with a coefficient of determination of 0.9 and an MAE of 0.014, while the linear regression model performed the worst, with a coefficient of determination of 0.42. Additionally, models trained using spectral effective information extracted by multilayer denoising autoencoders showed improved water quality retrieval accuracy compared to those trained with raw data, with the coefficient of determination for the bagging model increasing from 0.62 to 0.9. This study provides a rapid and accurate method for large-scale watershed water quality monitoring using remote sensing data, offering technical support for applying remote sensing data to watershed environmental management and water resource protection. Full article
(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing (Second Edition))
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31 pages, 12950 KiB  
Article
Exploring Trends and Variability of Water Quality over Lake Titicaca Using Global Remote Sensing Products
by Vann Harvey Maligaya, Analy Baltodano, Afnan Agramont and Ann van Griensven
Remote Sens. 2024, 16(24), 4785; https://doi.org/10.3390/rs16244785 - 22 Dec 2024
Viewed by 767
Abstract
Understanding the current water quality dynamics is necessary to ensure that ecological and sociocultural services are provided to the population and the natural environment. Water quality monitoring of lakes is usually performed with in situ measurements; however, these are costly, time consuming, laborious, [...] Read more.
Understanding the current water quality dynamics is necessary to ensure that ecological and sociocultural services are provided to the population and the natural environment. Water quality monitoring of lakes is usually performed with in situ measurements; however, these are costly, time consuming, laborious, and can have limited spatial coverage. Nowadays, remote sensing offers an alternative source of data to be used in water quality monitoring; by applying appropriate algorithms to satellite imagery, it is possible to retrieve water quality parameters. The use of global remote sensing water quality products increased in the last decade, and there are a multitude of products available from various databases. However, in Latin America, studies on the inter-comparison of the applicability of these products for water quality monitoring is rather scarce. Therefore, in this study, global remote sensing products estimating various water quality parameters were explored on Lake Titicaca and compared with each other and sources of data. Two products, the Copernicus Global Land Service (CGLS) and the European Space Agency Lakes Climate Change Initiative (ESA-CCI), were evaluated through a comparison with in situ measurements and with each other for analysis of the spatiotemporal variability of lake surface water temperature (LSWT), turbidity, and chlorophyll-a. The results of this study showed that the two products had limited accuracy when compared to in situ data; however, remarkable performance was observed in terms of exhibiting spatiotemporal variability of the WQ parameters. The ESA-CCI LSWT product performed better than the CGLS product in estimating LSWT, while the two products were on par with each other in terms of demonstrating the spatiotemporal patterns of the WQ parameters. Overall, these two global remote sensing water quality products can be used to monitor Lake Titicaca, currently with limited accuracy, but they can be improved with precise pixel identification, accurate optical water type definition, and better algorithms for atmospheric correction and retrieval. This highlights the need for the improvement of global WQ products to fit local conditions and make the products more useful for decision-making at the appropriate scale. Full article
(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing (Second Edition))
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19 pages, 44681 KiB  
Article
Spatial–Temporal Assessment of Eco-Environment Quality with a New Comprehensive Remote Sensing Ecological Index (CRSEI) Based on Quaternion Copula Function
by Zongmin Wang, Longfei Hou, Haibo Yang, Yong Zhao, Fei Chen, Qizhao Li and Zheng Duan
Remote Sens. 2024, 16(19), 3580; https://doi.org/10.3390/rs16193580 - 26 Sep 2024
Cited by 1 | Viewed by 966
Abstract
The traditional remote sensing ecological index (RSEI), based on principal component analysis (PCA) to integrate four evaluation indexes: greenness (NDVI), humidity (WET), dryness (NDBSI), and heat (LST), is insufficient to comprehensively consider the influence of each eco-environment evaluation index on eco-environment quality (EEQ). [...] Read more.
The traditional remote sensing ecological index (RSEI), based on principal component analysis (PCA) to integrate four evaluation indexes: greenness (NDVI), humidity (WET), dryness (NDBSI), and heat (LST), is insufficient to comprehensively consider the influence of each eco-environment evaluation index on eco-environment quality (EEQ). In this research, a new comprehensive remote sensing ecological index (CRSEI) based on the quaternion Copula function is proposed to comprehensively characterize EEQ responded by integrating four eco-environment evaluation indexes. Additionally, the spatiotemporal variation of EEQ in Henan Province is evaluated using monthly CRSEI data from 2001 to 2020. The results show that: (1) The applicability and monitoring accuracy of CRSEI are better than that of RSEI, which can be used to assess the EEQ. (2) The EEQ of Henan Province declined between 2001 and 2010 but significantly improved and rebounded from 2011 to 2020. During this period, CRSEI values were higher in West and South Henan and lowest in central Henan, with West Henan consistently showing the highest values across all seasons. (3) The EEQ in Henan Province exhibited a tendency of deterioration from the central cities outward, followed by improvement from the outer areas back towards the central cities. In 2010, regions with poor EEQ made up 68.3% of the total area, whereas by 2020, regions with excellent EEQ accounted for 74% of the total area. (4) The EEQ was significantly negatively correlated with human activities, while it was positively correlated with precipitation. The research provides a reference and guidance for the scientific assessment of the regional eco-environment. Full article
(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing (Second Edition))
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