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
Innovative UAV and Satellite Technologies and Applications for Spatiotemporal Analysis
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

Innovative UAV and Satellite Technologies and Applications for Spatiotemporal Analysis

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2842

Special Issue Editors

Dr. Apostolos Papakonstantinou

E-Mail Website
Guest Editor
Department of Civil Engineering and Geomatics, School of Engineering & Technology, Cyprus University of Technology, Saripolou 2-8, 3036 Achilleos 1 Building, 3rd Floor, P.O. Box 50329, Lemesos 3603, Cyprus
Interests: cartography; geoinformatics; UAV in spatiotemporal mapping; UAV data for environmental assessment; spatiotemporal mapping; geovisualization
Special Issues, Collections and Topics in MDPI journals
Prof. Eufemia Tarantino

E-Mail Website1 Website2
Guest Editor
Department of Civil, Environmental, Land, Construction and Chemistry (DICATECh), Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy
Interests: geomatics; optical remote sensing; pixel-based and geographic object-based image analysis (GEOBIA); UAV applications; digital photogrammetry and spatial analysis for water resource management
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandra Capolupo

E-Mail Website1 Website2
Guest Editor
Department of Civil, Environmental, Land, Construction and Chemistry (DICATECh), Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy
Interests: remote sensing; photogrammetry; vegetation mapping; geoinformation; satellite image processing; spatial analysis for heavy metal detection
Special Issues, Collections and Topics in MDPI journals
Dr. Athos Agapiou

E-Mail Website
Guest Editor
Department of Civil Engineering and Geomatics, School of Engineering & Technology, Cyprus University of Technology, Saripolou 2-8, 3036 Achilleos 1 Building, 2nd Floor, P.O Box. 50329, Lemesos 3603, Cyprus
Interests: remote sensing for cultural heritage; optical image processing analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing from UAVs to spaceborne sensors offers a unique opportunity to measure, analyze, quantify, map, and explore spatiotemporal phenomena at high temporal frequencies. By leveraging these technologies, researchers gain unprecedented insights into the dynamic changes occurring in coastal areas and other environments, facilitating better monitoring, management, and conservation efforts.

This Special Issue aims to collect innovative and high-quality research articles related to current trends and challenges in the field of UAV and satellite mapping for Dynamic Environmental Monitoring. The integration of UAV and satellite technologies with GIS and GeoAI has opened new avenues for the mapping, analysis, and assessment of environmental spatiotemporal phenomena. We invite contributions that explore the latest advancements, methodologies, and applications in this dynamic field.

In this Special Issue, original research articles and reviews are welcome. Research areas may include, but are not limited to, the following:

  • Monitoring and mapping changes;
  • UAV and satellite imagery within GIS are used for emergency planning and recovery;
  • UAV and satellite-based GIS applications for environmental monitoring;
  • The mapping and quantification of emerging environmental phenomena;
  • The use of geo AI for mapping spatial relationships and patterns;
  • Machine learning techniques for analyzing spatiotemporal data from UAVs and satellites;
  • Machine learning strategies for environmental monitoring and analyzing ecosystem dynamics;
  • Fusion techniques for UAV and satellite data;
  • The assessment of emerged phenomena using UAS remote sensing;
  • Techniques for monitoring environmental changes using UAV and satellite data;
  • Rapid assessment and response strategies for natural disasters;
  • Damage assessment in coastal areas using remote sensing;
  • UAV and remote sensing applications in archeology;
  • Temporal analysis of land use and land cover changes using UAV, satellite, and GIS technologies;
  • Decision making using remote sensing in coastal environments;
  • Applications in forest management, water resource monitoring, and pollution tracking.

These topics cover various applications and innovations at the intersection of UAV, satellite technologies, GeoAI, and GIS in mapping dynamic environmental phenomena, providing a comprehensive scope for research articles and systematic literature reviews on any of the aforementioned topics.

Dr. Apostolos Papakonstantinou
Prof. Eufemia Tarantino
Dr. Alessandra Capolupo
Dr. Athos Agapiou
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

  • UAV and satellite mapping
  • spatiotemporal phenomena
  • geospatial technologies
  • geospatial artificial intelligence (GeoAI)
  • aerial and satellite remote sensing
  • environmental change detection
  • multi-source data integration
  • high-resolution imagery
  • machine learning in geodata
  • natural disaster assessment

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 policies can be found here.

Published Papers (3 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

28 pages, 23880 KiB  
Article
Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground”
by Wu Nile, Su Rina, Na Mula, Cha Ersi, Yulong Bao, Jiquan Zhang, Zhijun Tong, Xingpeng Liu and Chunli Zhao
Remote Sens. 2025, 17(4), 572; https://doi.org/10.3390/rs17040572 - 8 Feb 2025
Viewed by 469
Abstract
Leaf chlorophyll content (LCC) is a key indicator of crop growth condition. Real-time, non-destructive, rapid, and accurate LCC monitoring is of paramount importance for precision agriculture management. This study proposes an improved method based on multi-source data, combining the Sentinel-2A spectral response function [...] Read more.
Leaf chlorophyll content (LCC) is a key indicator of crop growth condition. Real-time, non-destructive, rapid, and accurate LCC monitoring is of paramount importance for precision agriculture management. This study proposes an improved method based on multi-source data, combining the Sentinel-2A spectral response function (SRF) and computer algorithms, to overcome the limitations of traditional methods. First, the equivalent remote sensing reflectance of Sentinel-2A was simulated by combining UAV hyperspectral images with ground experimental data. Then, using grey relational analysis (GRA) and the maximum information coefficient (MIC) algorithm, we explored the complex relationship between the vegetation indices (VIs) and LCC, and further selected feature variables. Meanwhile, we utilized three spectral indices (DSI, NDSI, RSI) to identify sensitive band combinations for LCC and further analyzed the response relationship of the original bands to LCC. On this basis, we selected three nonlinear machine learning models (XGBoost, RFR, SVR) and one multiple linear regression model (PLSR) to construct the LCC inversion model, and we chose the optimal model to generate spatial distribution maps of maize LCC at the regional scale. The results indicate that there is a significant nonlinear correlation between the VIs and LCC, with the XGBoost, RFR, and SVR models outperforming the PLSR model. Among them, the XGBoost_MIC model achieved the best LCC inversion results during the tasseling stage (VT) of maize growth. In the UAV hyperspectral data, the model achieved an R2 = 0.962 and an RMSE = 5.590 mg/m2 in the training set, and an R2 = 0.582 and an RMSE = 6.019 mg/m2 in the test set. For the Sentinel-2A-simulated spectral data, the training set had an R2 = 0.923 and an RMSE = 8.097 mg/m2, while the test set showed an R2 = 0.837 and an RMSE = 3.250 mg/m2, which indicates an improvement in test set accuracy. On a regional scale, the LCC inversion model also yielded good results (train R2 = 0.76, test R2 = 0.88, RMSE = 18.83 mg/m2). In conclusion, the method proposed in this study not only significantly improves the accuracy of traditional methods but also, with its outstanding versatility, can achieve rapid, non-destructive, and precise crop growth monitoring in different regions and for various crop types, demonstrating broad application prospects and significant practical value in precision agriculture. Full article
(This article belongs to the Special Issue Innovative UAV and Satellite Technologies and Applications for Spatiotemporal Analysis)
►▼ Show Figures

Graphical abstract

21 pages, 10149 KiB  
Article
Minimizing Seam Lines in UAV Multispectral Image Mosaics Utilizing Irradiance, Vignette, and BRDF
by Hoyong Ahn, Chansol Kim, Seungchan Lim, Cheonggil Jin, Jinsu Kim and Chuluong Choi
Remote Sens. 2025, 17(1), 151; https://doi.org/10.3390/rs17010151 - 4 Jan 2025
Viewed by 716
Abstract
Unmanned aerial vehicle (UAV) imaging provides the ability to obtain high-resolution images at a lower cost than satellite imagery and aerial photography. However, multiple UAV images need to be mosaicked to obtain images of large areas, and the resulting UAV multispectral image mosaics [...] Read more.
Unmanned aerial vehicle (UAV) imaging provides the ability to obtain high-resolution images at a lower cost than satellite imagery and aerial photography. However, multiple UAV images need to be mosaicked to obtain images of large areas, and the resulting UAV multispectral image mosaics typically contain seam lines. To address this problem, we applied irradiance, vignette, and bidirectional reflectance distribution function (BRDF) filters and performed field work using a DJI Mavic 3 Multispectral (M3M) camera to collect data. We installed a calibrated reference tarp (CRT) in the center of the collection area and conducted three types of flights (BRDF, vignette, and validation) to measure the irradiance, radiance, and reflectance—which are essential for irradiance correction—using a custom reflectance box (ROX). A vignette filter was generated from the vignette parameter, and the anisotropy factor (ANIF) was calculated by measuring the radiance at the nadir, following which the BRDF model parameters were calculated. The calibration approaches were divided into the following categories: a vignette-only process, which solely applied vignette and irradiance corrections, and the full process, which included irradiance, vignette, and BRDF. The accuracy was verified through a validation flight. The radiance uncertainty at the seam line ranged from 3.00 to 5.26% in the 80% lap mode when using nine images around the CRT, and from 4.06 to 6.93% in the 50% lap mode when using all images with the CRT. The term ‘lap’ in ‘lap mode’ refers to both overlap and sidelap. The images that were subjected to the vignette-only process had a radiance difference of 4.48–6.98%, while that of the full process images was 1.44–2.40%, indicating that the seam lines were difficult to find with the naked eye and that the process was successful. Full article
(This article belongs to the Special Issue Innovative UAV and Satellite Technologies and Applications for Spatiotemporal Analysis)
►▼ Show Figures

Figure 1

32 pages, 4670 KiB  
Article
Mapping River Flow from Thermal Images in Approximately Real Time: Proof of Concept on the Sacramento River, California, USA
by Carl J. Legleiter, Paul J. Kinzel, Michael Dille, Massimo Vespignani, Uland Wong, Isaac Anderson, Elizabeth Hyde, Chris Gazoorian and Jennifer M. Cramer
Remote Sens. 2024, 16(24), 4746; https://doi.org/10.3390/rs16244746 - 19 Dec 2024
Cited by 1 | Viewed by 1017
Abstract
Image velocimetry has become an effective method of mapping flow conditions in rivers, but this analysis is typically performed in a post-processing mode after data collection is complete. In this study, we evaluated the potential to infer flow velocities in approximately real time [...] Read more.
Image velocimetry has become an effective method of mapping flow conditions in rivers, but this analysis is typically performed in a post-processing mode after data collection is complete. In this study, we evaluated the potential to infer flow velocities in approximately real time as thermal images are being acquired from an uncrewed aircraft system (UAS). The sensitivity of thermal image velocimetry to environmental conditions was quantified by conducting 20 flights over four days and assessing the accuracy of image-derived velocity estimates via comparison to direct field measurements made with an acoustic Doppler current profiler (ADCP). This analysis indicated that velocity mapping was most reliable when the air was cooler than the water. We also introduced a workflow for River Velocity Measurement in Approximately Real Time (RiVMART) that involved transferring brief image sequences from the UAS to a ground station as distinct data packets. The resulting velocity fields were as accurate as those generated via post-processing. A new particle image velocimetry (PIV) algorithm based on staggered image sequences increased the number of image pairs available for a given image sequence duration and slightly improved accuracy relative to a standard PIV implementation. Direct, automated geo-referencing of image-derived velocity vectors based on information on the position and orientation of the UAS acquired during flight led to poor alignment with vectors that were geo-referenced manually by selecting ground control points from an orthophoto. This initial proof-of-concept investigation suggests that our workflow could enable highly efficient characterization of flow fields in rivers and might help support applications that require rapid response to changing conditions. Full article
(This article belongs to the Special Issue Innovative UAV and Satellite Technologies and Applications for Spatiotemporal Analysis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop