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Remote Sensing
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Environmental Monitoring Based on Remote Sensing, Earth Observation and Geoinformation
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Environmental Monitoring Based on Remote Sensing, Earth Observation and Geoinformation

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

Deadline for manuscript submissions: 29 July 2025 | Viewed by 1938

Special Issue Editors

Dr. George P. Petropoulos

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Guest Editor
Department of Geography, Harokopio University of Athens, Eleftheriou Venizelou 70, Kallithea, 17676 Athens, Greece
Interests: earth observation; GIS; geospatial data analysis; operational products; land surface models; geoinformation; software tools
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Daniela Silva-Fuzzo

E-Mail Website
Guest Editor
Department of Agricultural and Biological Sciences, State University of Minas Gerais—Avenida Scotland, 1001, CEP: 38202-436, Frutal, Minas Gerais, Brazil
Interests: earth observation; GIS; land use; climate analysis of geospatial data; land surface models; geoinformation; agrometeorology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nikos Koutsias

E-Mail Website
Guest Editor
Department of Sustainable Agriculture, University of Patras, 2 Seferi, Agrinio, GR-30100, Greece
Interests: remote sensing; GIS; spatial analysis; wildland fires; natural disasters; landscape ecology; phenology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yansong Bao

E-Mail Website
Guest Editor
School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: satellite remote sensing; data assimilation; weather forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, titled “Environmental Monitoring Based on Remote Sensing, Earth Observation and Geoinformation”, aims to explore the comprehensive utilization of geoinformation technologies for environmental mapping and monitoring across a wide spectrum of applications.

In recent decades, Earth Observation (EO) technology has become indispensable for determining a multitude of parameters that characterize Earth’s systems. Its integration with Geographic Information Systems (GIS) and various simulation models has significantly amplified its impact, leading to the proliferation of operational products and services available globally and showcasing the rapid advancements in these fields. The evolution of Remote Sensing (RS) and GIS technologies has also underscored the critical need for the efficient manipulation and analysis of EO data and has opened up new opportunities for using this technologies in a range of applications. To address this, numerous open-source programs for EO and GIS analysis and image processing have also emerged. This Special Issue invites contributions that focus on the development and application of innovative, open-source software tools designed for displaying, processing, and analyzing EO data obtained from handheld, airborne, or spaceborne sensors. Furthermore, it welcomes submissions that emphasize the use of new, open-source software tools and web-GIS platforms for GIS data analysis across a wide range of environmental applications. These contributions will reflect the cutting-edge progress made in the field of geoinformation technologies for environmental monitoring.

Specific topics for this Special Issue include, but are not limited to, the following:

  • Applicability of Active and Passive EO Sensors: synthetic aperture radar (SAR), optical, and thermal sensors.
  • Multi-Sensor Synergies: exploring the combined use of various sensors to enhance data accuracy and application.
  • Applications at different scales of Proximal and Remote Sensing: phenotyping platforms, drones, and satellite-borne data.
  • Phenology, Time Series, and Gap-filling: analysis of seasonal patterns and methods for addressing data gaps.
  • Synergies of Remote Sensing, GIS, and Simulation Process Models: integrating different technologies to improve environmental monitoring and modeling.
  • Downscaling and upscaling of biophysical parameters: Methods for translating data between different spatial scales.
  • New and emerging applications of geoinformation technologies: innovative uses of geoinformation in various environmental contexts.
  • Uncertainty assessment of remotely sensed data and approaches for evaluating and improving the reliability of operational products.

This Special Issue aims to highlight and disseminate advancements in the field, promoting the development of accessible, efficient, and innovative solutions for environmental monitoring through the use of geoinformation technologies.

Dr. George P. Petropoulos
Prof. Dr. Daniela Silva-Fuzzo
Prof. Dr. Nikos Koutsias
Prof. Dr. Yansong Bao
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

  • earth observation
  • remote sensing
  • geoinformation
  • geographical information systems
  • geospatial software tools
  • geoinformatics
  • operational products validation
  • natural disasters
  • time series analysis
  • multispectral and hyperspectral remote sensing sensors

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

22 pages, 2804 KiB  
Article
Semi-Automatic Extraction of Hedgerows from High-Resolution Satellite Imagery
by Anna Lilian Gardossi, Antonio Tomao, MD Abdul Mueed Choudhury, Ernesto Marcheggiani and Maurizia Sigura
Remote Sens. 2025, 17(9), 1506; https://doi.org/10.3390/rs17091506 - 24 Apr 2025
Viewed by 353
Abstract
Small landscape elements are critical in ecological systems, encompassing vegetated and non-vegetated features. As vegetated elements, hedgerows contribute significantly to biodiversity conservation, erosion protection, and wind speed reduction within agroecosystems. This study focuses on the semi-automatic extraction of hedgerows by applying the Object-Based [...] Read more.
Small landscape elements are critical in ecological systems, encompassing vegetated and non-vegetated features. As vegetated elements, hedgerows contribute significantly to biodiversity conservation, erosion protection, and wind speed reduction within agroecosystems. This study focuses on the semi-automatic extraction of hedgerows by applying the Object-Based Image Analysis (OBIA) approach to two multispectral satellite datasets. Multitemporal image data from PlanetScope and Copernicus Sentinel-2 have been used to test the applicability of the proposed approach for detailed land cover mapping, with an emphasis on extracting Small Woody Elements. This study demonstrates significant results in classifying and extracting hedgerows, a smaller landscape element, from both Sentinel-2 and PlanetScope images. A good overall accuracy (OA) was obtained using PlanetScope data (OA = 95%) and Sentinel-2 data (OA = 85%), despite the coarser resolution of the latter. This will undoubtedly demonstrate the effectiveness of the OBIA approach in leveraging freely available image data for detailed land cover mapping, particularly in identifying and classifying hedgerows, thus supporting biodiversity conservation and ecological infrastructure enhancement. Full article
(This article belongs to the Special Issue Environmental Monitoring Based on Remote Sensing, Earth Observation and Geoinformation)
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24 pages, 9347 KiB  
Article
RDAU-Net: A U-Shaped Semantic Segmentation Network for Buildings near Rivers and Lakes Based on a Fusion Approach
by Yipeng Wang, Dongmei Wang, Teng Xu, Yifan Shi, Wenguang Liang, Yihong Wang, George P. Petropoulos and Yansong Bao
Remote Sens. 2025, 17(1), 2; https://doi.org/10.3390/rs17010002 - 24 Dec 2024
Viewed by 727
Abstract
The encroachment of buildings into the waters of rivers and lakes can lead to increased safety hazards, but current semantic segmentation algorithms have difficulty accurately segmenting buildings in such environments. The specular reflection of the water and boats with similar features to the [...] Read more.
The encroachment of buildings into the waters of rivers and lakes can lead to increased safety hazards, but current semantic segmentation algorithms have difficulty accurately segmenting buildings in such environments. The specular reflection of the water and boats with similar features to the buildings in the environment can greatly affect the performance of the algorithm. Effectively eliminating their influence on the model and further improving the segmentation accuracy of buildings near water will be of great help to the management of river and lake waters. To address the above issues, the present study proposes the design of a U-shaped segmentation network of buildings called RDAU-Net that works through extraction and fuses a convolutional neural network and a transformer to segment buildings. First, we designed a residual dynamic short-cut down-sampling (RDSC) module to minimize the interference of complex building shapes and building scale differences on the segmentation results; second, we reduced the semantic and resolution gaps between multi-scale features using a multi-channel cross fusion transformer module (MCCT); finally, a double-feature channel-wise fusion attention (DCF) was designed to improve the model’s ability to depict building edge details and to reduce the influence of similar features on the model. Additionally, an HRI Building dataset was constructed, comprising water-edge buildings situated in a riverine and lacustrine regulatory context. This dataset encompasses a plethora of water-edge building sample scenarios, offering a comprehensive representation of the subject matter. The experimental results indicated that the statistical metrics achieved by RDAU-Net using the HRI and WHU Building datasets are better than those of others, and that it can effectively solve the building segmentation problems in the management of river and lake waters. Full article
(This article belongs to the Special Issue Environmental Monitoring Based on Remote Sensing, Earth Observation and Geoinformation)
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