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Special Issue "Unmanned Aerial Vehicles in Earth and Environmental Sciences"

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

Deadline for manuscript submissions: closed (30 September 2019).

Special Issue Editors

Prof. Tomasz Niedzielski
E-Mail Website
Guest Editor
University of Wrocław, Faculty of Earth Sciences and Environmental Management, Wrocław, Poland
Interests: geoinformatics; unmanned aerial vehicles; hydrology; hydroinformatics
Prof. Jakub Langhammer
E-Mail Website
Guest Editor
Charles University, Faculty of Science, Prague, Czech Republic
Interests: hydrology; fluvial geomorphology; physical geography; geoinformatics; UAVs; remote sensing
Dr. Daniele Giordan
E-Mail Website
Guest Editor
Research Institute for Geo-Hydrological Protection, National Research Council, 10135 Torino, Italy
Interests: geometric and radiometric sensors; sensor fusion; mission planning; navigation and position/orientation; geosciences; natural hazards; monitoring/change detection
Special Issues and Collections in MDPI journals
Dr. Waldemar Spallek
E-Mail Website
Guest Editor
University of Wrocław, Faculty of Earth Science and Environmental Management, Wrocław, Poland
Interests: cartography; geovisualisation; unmanned aerial vehicles

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs), also known as drones, have become powerful tools for acquiring geoscientific data on the Earth’s surface and its subsurface, as well as the hydrosphere and atmosphere. The vast majority of UAV applications include aerial observations of the Earth surface using cameras sensitive to different parts of the electromagnetic spectrum. However, different sensors (e.g., light detection and ranging (LIDAR) devices, magnetometers, ground-penetrating radars (GPRs), and meteorological instruments) are also installed on-board drones. The objective of this Special Issue is to present recent advances and challenges of UAV applications to solve research problems in the field of Earth and environmental sciences. Original research articles as well as review papers on the diverse use of drones in geosciences, equipped with various cameras and sensors, are welcome.

Prof. Tomasz Niedzielski
Prof. Jakub Langhammer
Dr. Daniele Giordan
Dr. Waldemar Spallek
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 papers will be 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. 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 1800 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

  • unmanned aerial vehicle 
  • Earth 
  • environment 
  • cameras 
  • sensors 
  • mapping 
  • spatial analysis

Published Papers (2 papers)

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Research

Open AccessArticle
Structure from Motion Multisource Application for Landslide Characterization and Monitoring: The Champlas du Col Case Study, Sestriere, North-Western Italy
Sensors 2019, 19(10), 2364; https://doi.org/10.3390/s19102364 - 22 May 2019
Abstract
Structure from Motion (SfM) is a powerful tool to provide 3D point clouds from a sequence of images taken from different remote sensing technologies. The use of this approach for processing images captured from both Remotely Piloted Aerial Vehicles (RPAS), historical aerial photograms, [...] Read more.
Structure from Motion (SfM) is a powerful tool to provide 3D point clouds from a sequence of images taken from different remote sensing technologies. The use of this approach for processing images captured from both Remotely Piloted Aerial Vehicles (RPAS), historical aerial photograms, and smartphones, constitutes a valuable solution for the identification and characterization of active landslides. We applied SfM to process all the acquired and available images for the study of the Champlas du Col landslide, a complex slope instability reactivated in spring 2018 in the Piemonte Region (north-western Italy). This last reactivation of the slide, principally due to snow melting at the end of the winter season, interrupted the main road used to reach Sestriere, one of the most famous ski resorts in north-western Italy. We tested how SfM can be applied to process high-resolution multisource datasets by processing: (i) historical aerial photograms collected from five diverse regional flights, (ii) RGB and multi-spectral images acquired by two RPAS, taken in different moments, and (iii) terrestrial sequences of the most representative kinematic elements due to the evolution of the landslide. In addition, we obtained an overall framework of the historical development of the area of interest, and distinguished several generations of landslides. Moreover, an in-depth geomorphological characterization of the Champlas du Col landslide reactivation was done, by testing a cost-effective and rapid methodology based on SfM principles, which is easily repeatable to characterize and investigate active landslides. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles in Earth and Environmental Sciences)
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Open AccessArticle
Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry
Sensors 2019, 19(5), 1027; https://doi.org/10.3390/s19051027 - 28 Feb 2019
Cited by 1
Abstract
This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth [...] Read more.
This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles in Earth and Environmental Sciences)
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