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Remote Sensing and UAV Technologies for Environmental Monitoring
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Remote Sensing and UAV Technologies for Environmental Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 25 December 2025 | Viewed by 1942

Special Issue Editor

Prof. Dr. Alfred Colpaert

E-Mail Website
Guest Editor
Department of Geographical and Historical Studies, University of Eastern Finland, Yliopistokatu 7, 80100 Joensuu, Finland
Interests: geoinformatics; spatial analysis; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors is a journal dedicated to a wide scope of sensor and imaging technologies and related topics. UAV technologies for environmental imaging and UAV sensor development and image processing therefore fall within the journal’s scope.

UAV (drone) hardware and software are rapidly evolving, making the technology available to large communities of researchers and professionals. The use of UAV technology has come to provide fast and cost-effective tools for the monitoring of natural and manmade environments. The need to provide instant aerial data to assess environmental accidents and damage is evident. Apart from basic RGB camera drones, there is a growing need for cheap specialized drones carrying, for example, thermal or multi-spectral cameras, just as there is an emerging requirement to fly drone swarms that can map large areas in search and rescue missions. The development of weather resistant drones has also become a clear issue. This Special Issue intends to provide a platform for those actively developing UAV sensor products, flight automation, high-precision positioning, integration, image processing, and other related fields.

Prof. Dr. Alfred Colpaert
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 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 250 words) can be sent to the Editorial Office for assessment.

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. Sensors 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 2600 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

  • UAV sensors
  • flight automation
  • drone swarms
  • UAV high-precision positioning
  • RTK
  • environmental monitoring
  • damage assessment
  • long-endurance flight technology
  • weather resistant drones

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Published Papers (2 papers)

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Research

21 pages, 11077 KB  
Article
An Investigation into the Registration of Unmanned Surface Vehicle (USV)–Unmanned Aerial Vehicle (UAV) and UAV–UAV Point Cloud Models
by Yu-Shen Hsiao, Yu-Hsuan Cho and Yu-Sian Yan
Sensors 2025, 25(22), 6992; https://doi.org/10.3390/s25226992 - 15 Nov 2025
Viewed by 566
Abstract
This study explores the integration of point cloud data obtained from unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) to address limitations in photogrammetry and to create comprehensive models of aquatic environments. The UAV platform (AUTEL EVO II) employs structure-from-motion (SfM) photogrammetry [...] Read more.
This study explores the integration of point cloud data obtained from unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) to address limitations in photogrammetry and to create comprehensive models of aquatic environments. The UAV platform (AUTEL EVO II) employs structure-from-motion (SfM) photogrammetry using optical imagery, while the USV (equipped with a NORBIT iWBMS multibeam sonar system) collects underwater bathymetric data. UAVs commonly face constraints in battery life and image-processing capacity, making it necessary to merge smaller UAV point clouds into larger, more complete models. The USV-derived bathymetric data are integrated with UAV-derived surface data to construct unified terrain models that include both above-water and underwater features. This study evaluates three coordinate transformation (CT) methods—4-parameter, 6-parameter, and 7-parameter—across three study areas in Taiwan to assess their effectiveness in registering USV–UAV and UAV–UAV point clouds. For USV–UAV integration, all CT methods improved alignment accuracy compared with results without CT, achieving decimeter-level precision. For UAV–UAV integrations, the 7-parameter method provided the best accuracy, especially in areas with low terrain roughness such as rooftops and pavements, while improvements were less pronounced in areas with high roughness such as tree canopies. These findings demonstrate that the 7-parameter CT method offers an effective and straightforward approach for accurate point cloud integration from different platforms and sensors. Full article
(This article belongs to the Special Issue Remote Sensing and UAV Technologies for Environmental Monitoring)
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19 pages, 6293 KB  
Article
Restoring Anomalous Water Surface in DOM Product of UAV Remote Sensing Using Local Image Replacement
by Chunjie Wang, Ti Zhang, Liang Tao and Jiayuan Lin
Sensors 2025, 25(13), 4225; https://doi.org/10.3390/s25134225 - 7 Jul 2025
Viewed by 757
Abstract
In the production of a digital orthophoto map (DOM) from unmanned aerial vehicle (UAV)-acquired overlapping images, some anomalies such as texture stretching or data holes frequently occur in water areas due to the lack of significant textural features. These anomalies seriously affect the [...] Read more.
In the production of a digital orthophoto map (DOM) from unmanned aerial vehicle (UAV)-acquired overlapping images, some anomalies such as texture stretching or data holes frequently occur in water areas due to the lack of significant textural features. These anomalies seriously affect the visual quality and data integrity of the resulting DOMs. In this study, we attempted to eliminate the water surface anomalies in an example DOM via replacing the entire water area with an intact one that was clipped out from one single UAV image. The water surface scope and boundary in the image was first precisely achieved using the multisource seed filling algorithm and contour-finding algorithm. Next, the tie points were selected from the boundaries of the normal and anomalous water surfaces, and employed to realize their spatial alignment using affine plane coordinate transformation. Finally, the normal water surface was overlaid onto the DOM to replace the corresponding anomalous water surface. The restored water area had good visual effect in terms of spectral consistency, and the texture transition with the surrounding environment was also sufficiently natural. According to the standard deviations and mean values of RGB pixels, the quality of the restored DOM was greatly improved in comparison with the original one. These demonstrated that the proposed method had a sound performance in restoring abnormal water surfaces in a DOM, especially for scenarios where the water surface area is relatively small and can be contained in a single UAV image. Full article
(This article belongs to the Special Issue Remote Sensing and UAV Technologies for Environmental Monitoring)
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