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Remote Sensing
Special Issues
State-of-the-Art on Satellite and UAV Remote Sensing in Geoscience Research
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State-of-the-Art on Satellite and UAV Remote Sensing in Geoscience Research

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

Deadline for manuscript submissions: closed (26 April 2024) | Viewed by 3331

Special Issue Editors

Dr. Francesca Ardizzone

E-Mail Website
Guest Editor
National Research Council of Italy, Research Institute of Geo-Hydrological Protection (CNR IRPI), Via della Madonna Alta 126, 06128 Perugia, Italy
Interests: landslide; landslide hazard; landslide risk; remote sensing; geodatabase
Special Issues, Collections and Topics in MDPI journals
Dr. Gabriella Caroti

E-Mail Website
Guest Editor
Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
Interests: geomatics; photogrammetry; terrestrial laser scanning, cultural heritage surveying
Special Issues, Collections and Topics in MDPI journals
Dr. Yuankun Xu

E-Mail Website
Guest Editor
Department of Earth and Planetary Science, University of California, Berkeley, 201 McCone Hall, Berkeley, CA 94720, USA
Interests: radar remote sensing; InSAR; hydromechanics; flow dynamics; natural hazards

Special Issue Information

Dear Colleagues,

Satellite and UAV (Unmanned Aerial Vehicle) remote sensing provide essential observations for geoscientists to better understand the earth system and the interaction between human activities and our living environment. The advancement of remote sensing in geosciences encompasses efforts spanning from the development of remote sensing sensors and platforms, the processing, interpretation, and dissemination of observational data, to the physical modeling of earth system dynamics. We welcome studies that utilize state-of-the-art remote sensing techniques to advance our knowledge in geosciences across a broad range of spectrums. These topics include yet are not limited to (1) development, calibration, and validation of satellite and airborne instruments, such as optical, radar, and lidar sensors; (2) advanced and novel data processing methodology and algorithms, including photogrammetry, structure from motion, and point cloud processing, InSAR time series analysis; (3) geoscience research using remotely sensed datasets from individual cases to regional-scale characterization, such as landcover change, geological mapping, groundwater and soil moisture assessment, geodetic deformation survey, natural hazard monitoring, and archaeology; (4) mechanistic characterization of surface and subsurface processes using remote sensing and numerical modeling, and (5) the integration of multiple components above.

Dr. Francesca Ardizzone
Dr. Gabriella Caroti
Dr. Yuankun Xu
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
  • photogrammetry
  • lidar point cloud
  • UAV
  • InSAR
  • natural hazards
  • landcover change
  • soil moisture
  • geologic mapping
  • geomorphology

Published Papers (3 papers)

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Research

32 pages, 32412 KiB  
Article
Monitoring and Quantifying Soil Erosion and Sedimentation Rates in Centimeter Accuracy Using UAV-Photogrammetry, GNSS, and t-LiDAR in a Post-Fire Setting
by Simoni Alexiou, Ioannis Papanikolaou, Sascha Schneiderwind, Valerie Kehrle and Klaus Reicherter
Remote Sens. 2024, 16(5), 802; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16050802 - 25 Feb 2024
Viewed by 1415
Abstract
Remote sensing techniques, namely Unmanned Aerial Vehicle (UAV) photogrammetry and t-LiDAR (terrestrial Light Detection and Ranging), two well-established techniques, were applied for seven years in a mountainous Mediterranean catchment in Greece (Ilioupoli test site, Athens), following a wildfire event in 2015. The goal [...] Read more.
Remote sensing techniques, namely Unmanned Aerial Vehicle (UAV) photogrammetry and t-LiDAR (terrestrial Light Detection and Ranging), two well-established techniques, were applied for seven years in a mountainous Mediterranean catchment in Greece (Ilioupoli test site, Athens), following a wildfire event in 2015. The goal was to monitor and quantify soil erosion and sedimentation rates with cm accuracy. As the frequency of wildfires in the Mediterranean has increased, this study aims to present a methodological approach for monitoring and quantifying soil erosion and sedimentation rates in post-fire conditions, through high spatial resolution field measurements acquired using a UAV survey and a t-LiDAR (or TLS—Terrestrial Laser Scanning), in combination with georadar profiles (Ground Penetration Radar—GPR) and GNSS. This test site revealed that 40 m3 of sediment was deposited following the first intense autumn rainfall events, a value that was decreased by 50% over the next six months (20 m3). The UAV–SfM technique revealed only 2 m3 of sediment deposition during the 2018–2019 analysis, highlighting the decrease in soil erosion rates three years after the wildfire event. In the following years (2017–2021), erosion and sedimentation decreased further, confirming the theoretical pattern, whereas sedimentation over the first year after the fire was very high and then sharply lessened as vegetation regenerated. The methodology proposed in this research can serve as a valuable guide for achieving high-precision sediment yield deposition measurements based on a detailed analysis of 3D modeling and a point cloud comparison, specifically leveraging the dense data collection facilitated by UAV–SfM and TLS technology. The resulting point clouds effectively replicate the fine details of the topsoil microtopography within the upland dam basin, as highlighted by the profile analysis. Overall, this research clearly demonstrates that after monitoring the upland area in post-fire conditions, the UAV–SfM method and LiDAR cm-scale data offer a realistic assessment of the retention dam’s life expectancy and management planning. These observations are especially crucial for assessing the impacts in the wildfire-affected areas, the implementation of mitigation strategies, and the construction and maintenance of retention dams. Full article
(This article belongs to the Special Issue State-of-the-Art on Satellite and UAV Remote Sensing in Geoscience Research)
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15 pages, 9026 KiB  
Article
Non-Destructive Estimation of Deciduous Forest Metrics: Comparisons between UAV-LiDAR, UAV-DAP, and Terrestrial LiDAR Leaf-Off Point Clouds Using Two QSMs
by Yi Gan, Quan Wang and Guangman Song
Remote Sens. 2024, 16(4), 697; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16040697 - 16 Feb 2024
Viewed by 594
Abstract
Timely acquisition of forest structure is crucial for understanding the dynamics of ecosystem functions. Despite the fact that the combination of different quantitative structure models (QSMs) and point cloud sources (ALS and DAP) has shown great potential to characterize tree structure, few studies [...] Read more.
Timely acquisition of forest structure is crucial for understanding the dynamics of ecosystem functions. Despite the fact that the combination of different quantitative structure models (QSMs) and point cloud sources (ALS and DAP) has shown great potential to characterize tree structure, few studies have addressed their pros and cons in alpine temperate deciduous forests. In this study, different point clouds from UAV-mounted LiDAR and DAP under leaf-off conditions were first processed into individual tree point clouds, and then explicit 3D tree models of the forest were reconstructed using the TreeQSM and AdQSM methods. Structural metrics obtained from the two QSMs were evaluated based on terrestrial LiDAR (TLS)-based surveys. The results showed that ALS-based predictions of forest structure outperformed DAP-based predictions at both plot and tree levels. TreeQSM performed with comparable accuracy to AdQSM for estimating tree height, regardless of ALS (plot level: 0.93 vs. 0.94; tree level: 0.92 vs. 0.92) and DAP (plot level: 0.86 vs. 0.86; tree level: 0.89 vs. 0.90) point clouds. These results provide a robust and efficient workflow that takes advantage of UAV monitoring for estimating forest structural metrics and suggest the effectiveness of LiDAR in temperate deciduous forests. Full article
(This article belongs to the Special Issue State-of-the-Art on Satellite and UAV Remote Sensing in Geoscience Research)
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18 pages, 2670 KiB  
Article
Absolute Calibration of a UAV-Mounted Ultra-Wideband Software-Defined Radar Using an External Target in the Near-Field
by Asem Melebari, Piril Nergis, Sepehr Eskandari, Pedro Ramos Costa and Mahta Moghaddam
Remote Sens. 2024, 16(2), 231; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16020231 - 06 Jan 2024
Viewed by 659
Abstract
We describe a method to calibrate a Software-Defined Radar (SDRadar) system mounted on an uncrewed aerial vehicle (UAV) with an ultra-wideband (UWB) waveform operated in the near-field region. Radar calibration is a prerequisite for using the full capabilities of the radar system to [...] Read more.
We describe a method to calibrate a Software-Defined Radar (SDRadar) system mounted on an uncrewed aerial vehicle (UAV) with an ultra-wideband (UWB) waveform operated in the near-field region. Radar calibration is a prerequisite for using the full capabilities of the radar system to retrieve geophysical parameters accurately. We introduce a framework and process to calibrate the SDRadar with the UWB waveform in the 675 MHz–3 GHz range in the near-field region. Furthermore, we present the framework for computing the near-field radar cross section (RCS) of an external passive calibration target, a trihedral corner reflector (CR), using HFSS software and with consideration for specific antennas. The calibration performance was evaluated with various distances between the calibration target and radar antennas. The necessity for the knowledge of the near-field RCS to calibrate SDRadar was demonstrated, which sets this work apart from the standard method of using a trihedral CR for backscatter radar calibration. We were able to achieve approximately 0.5 dB accuracy when calibrating the SDRadar in the anechoic chamber using a trihedral CR. In outdoor field conditions, where the ground rough surface scattering effects are present, the calibration performance was lower, approximately 1.5 dB. A solution is proposed to overcome the ground effect by elevating the CR above the ground level, which enables applying time-gating around the CR echo, excluding the reflection from the ground. Full article
(This article belongs to the Special Issue State-of-the-Art on Satellite and UAV Remote Sensing in Geoscience Research)
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