Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Remote Sensing in Geomatics (Second Edition)
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing in Geomatics (Second Edition)

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

Deadline for manuscript submissions: 15 June 2025 | Viewed by 4205

Special Issue Editors

Prof. Dr. Gino Dardanelli

E-Mail Website
Guest Editor
Department of Engineering, Università degli Studi di Palermo, 90100 Palermo, Italy
Interests: Galileo; GLONASS; GPS; GNSS; CORS; remote sensing; geomatics; dam displacements
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paolo Dabove

E-Mail Website
Guest Editor
Department of Environment, Land, and Infrastructure Engineering (DIATI), Politecnico di Torino, 10129 Torino, Italy
Interests: GNSS; GIS; open data; visual odometry
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Günther Retscher

E-Mail Website
Guest Editor
Engineering Geodesy, Institute of Geodesy and Geophysics, Vienna University of Technology, 1040 Vienna, Austria
Interests: positioning and navigation with GNSS; location based services (LBS); indoor and pedestrian navigation; applications of multi-sensor systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geomatics can be used in all domains in which the positioning of a simple or complex element plays a central role; this problem is solved by implementing algorithms of analytical modelling and rigorous statistical methods to various types of data to estimation, in addition to finding position, precision, and accuracy, depending on the scale of its representation. The research activity research also takes technological development into account, with consequent theoretical–analytical disciplinary development. The geomatic results obtained have implications within the discipline itself (such as geodesy, global reference systems, and networks of permanent stations) or constitute crucial support for many multi-disciplinary analyses. Geomatic surveying is carried out with terrestrial, marine, airborne, and space-based sensors using GNSS, inertial, topographic laser scanning, photogrammetric, and remote sensing sensors. 

After the excellent results of the first edition of the Special Issue “Remote Sensing in Geomatics” (with 20 papers submitted, 12 of which were accepted and published, constituting a 60% acceptance), a new version is proposed.

Prof. Dr. Gino Dardanelli
Prof. Dr. Paolo Dabove
Prof. Dr. Günther Retscher
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

  • remote sensing
  • radar, thermal, optical, interferometry, hyperspectral
  • geostatistics
  • geodesy
  • cartography
  • GIS, WebGIS, DSS
  • GNSS
  • LIDAR
  • geometric and radiometric accuracy
  • photogrammetry
  • UAV

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Related Special Issue

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

20 pages, 4062 KiB  
Article
A CNN-Based Framework for Automatic Extraction of High-Resolution River Bankfull Width
by Wenqi Li, Chendi Zhang, David Puhl, Xiao Pan, Marwan A. Hassan, Stephen Bird, Kejun Yang and Yang Zhao
Remote Sens. 2024, 16(23), 4614; https://doi.org/10.3390/rs16234614 - 9 Dec 2024
Viewed by 526
Abstract
River width is a crucial parameter that correlates and reflects the hydrological, geomorphological, and ecological characteristics of the channel. However, the width data with high spatial resolution is limited owing to the difficulties in extracting channel width under complex and variable riverine surroundings. [...] Read more.
River width is a crucial parameter that correlates and reflects the hydrological, geomorphological, and ecological characteristics of the channel. However, the width data with high spatial resolution is limited owing to the difficulties in extracting channel width under complex and variable riverine surroundings. To address this issue, we aimed to develop an automatic framework specifically for delineating river channels and measuring the bankfull widths at small spatial intervals along the channel. The DeepLabV3+ Convolutional Neural Network (CNN) model was employed to accurately delineate channel boundaries and a Voronoi Diagram approach was complemented as the river width algorithm (RWA) to calculate river bankfull widths. The CNN model was trained by images across four river types and performed well with all the evaluating metrics (mIoU, Accuracy, F1-score, and Recall) higher than 0.97, referring to the accuracy over 97% in prediction. The RWA outperformed other existing river width calculation methods by showing lower errors. The application of the framework in the Lillooet River, Canada, presented the capacity of this methodology to obtain detailed distributions of hydraulic and hydrological parameters, including flow resistance, flow energy, and sediment transport capacity, based on high-resolution channel widths. Our work highlights the significant potential of the newly developed framework in acquiring high-resolution channel width information and characterizing fluvial dynamics based on these widths along river channels, which contributes to facilitating cost-effective integrated river management. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics (Second Edition))
►▼ Show Figures

Figure 1

Review

Jump to: Research

26 pages, 2185 KiB  
Review
Rural Land Degradation Assessment through Remote Sensing: Current Technologies, Models, and Applications
by Federica D’Acunto, Francesco Marinello and Andrea Pezzuolo
Remote Sens. 2024, 16(16), 3059; https://doi.org/10.3390/rs16163059 - 20 Aug 2024
Cited by 1 | Viewed by 3070
Abstract
Degradation and desertification represent serious threats, as they present severe environmental and socio-economic consequences, demanding immediate action. Although a recognized methodology for assessing degradation and desertification is missing, remote sensing has been recognized as a powerful support for its accessibility and efficacy. The [...] Read more.
Degradation and desertification represent serious threats, as they present severe environmental and socio-economic consequences, demanding immediate action. Although a recognized methodology for assessing degradation and desertification is missing, remote sensing has been recognized as a powerful support for its accessibility and efficacy. The aim of this study is to examine the application of remote sensing for assessing land and soil degradation and desertification. A total of 278 research papers retrieved from Scopus/Web of Science database and published over the past decade have been analyzed. From the analysis of scientific publications, a rising interest for these topics and a dominance of research from China has been registered. Established satellite data, Landsat, and MODIS, despite limitations in accuracy and resolution, remain popular due to easy access. This restricts research to broader scales and limits practical applications like land management. The prevalent use of vegetation indexes, while convenient, can be misleading due to their indirect connection to soil health. Consequently, vegetation-based models may not fully capture the complexities involved. To improve understanding, the study suggests a shift towards multi-indexes models and a move away from relying solely on readily available data products. Moreover, the application of data fusion methods could provide a more holistic view. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics (Second Edition))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop