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Remote Sensing
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Remote Sensing in Geomatics (Second Edition)
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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 5515

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
Department of Geodesy and Geoinformation, Technische Universitat Wien, 1040 Vienna, Austria
Interests: sitioning and navigation with GNSS; location-based services; indoor and pedestrian navigation; applications of multi-sensor systems; smartphone positioning and sensor fusion
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

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Related Special Issue

Published Papers (3 papers)

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Research

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29 pages, 5358 KiB  
Article
An Approach for Spatial Statistical Modelling Remote Sensing Data of Land Cover by Fusing Data of Different Types
by Antonella Belmonte, Carmela Riefolo, Gabriele Buttafuoco and Annamaria Castrignanò
Remote Sens. 2025, 17(1), 123; https://doi.org/10.3390/rs17010123 - 2 Jan 2025
Viewed by 540
Abstract
Remote sensing technologies continue to expand their role in environmental monitoring, providing invaluable advances in soil assessing and mapping. This study aimed to prove the need to apply spatial statistical models for processing data in remote sensing (RS), which appears to be an [...] Read more.
Remote sensing technologies continue to expand their role in environmental monitoring, providing invaluable advances in soil assessing and mapping. This study aimed to prove the need to apply spatial statistical models for processing data in remote sensing (RS), which appears to be an important source of spatial data at multiple scales. A crucial problem facing us is the fusion of multi-source spatial data of different natures and characteristics, among which there is the support size of measurement that unfortunately is little considered in RS. A data fusion approach of both sample (point) and grid (areal) data is proposed that explicitly takes into account spatial correlation and change of support in both increasing support (upscaling) and decreasing support (downscaling). The techniques of block cokriging and kriging downscaling were employed for the implementation of such an approach, respectively. The method is applied to soil sample data, jointly analysed with hyperspectral data measured in the laboratory, UAV, and satellite data (Planet and Sentinel 2) of an olive grove after filtering soil pixels. Each data type had its own support that was transformed to the same support as the soil sample data so that the data fusion approach could be applied. To demonstrate the statistical, as well as practical, effectiveness of such a method, it was compared by a cross-validation test with a univariate approach for predicting each soil property. The positive results obtained should stimulate advanced statistical techniques to be applied more and more widely to RS data. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics (Second Edition))
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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 836
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))
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Review

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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 3507
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))
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