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Unmanned Aerial Systems (UASs) for Environmental Applications

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

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 2577

Special Issue Editors


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Co-Guest Editor
Università Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e dell’ Architettura (DICEA). Via brecce bianche, 60131 Ancona, Italy
Interests: GIS; geomatics; remote sensing; classification; cultural heritage; photogrammetry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental monitoring plays a pivotal role in many fields. It allows for a proper diagnosis of climate changes, managing their impacts on natural, forestal, and agricultural systems. In addition, efficient methods for monitoring our environment can enhance our understanding of hydrological processes, facilitating the optimization and distribution of water resources. Innovative approaches towards this end are now available and affordable, providing decision makers with forecasting solutions for preventing natural disasters. In this context, unmanned aerial systems (UASs) are drastically growing their capabilities. Like never before, UAVs are now ready to bridge the gap between field observations and space-borne remote sensing. In other words, they cover an essential matter of scale. Leveraging the full potential of UAS-based approaches for environmental monitoring means exploiting the most striking feature of UAS data: the very fine spatial resolution. The purpose of this Special Issue is thus to collect research articles proposing innovative solutions about the use of UAS drones equipped with different sensors for application domains including but not limited to:

  • Land cover/land use analysis (including forestry, building damages, hazards monitoring, change detection);
  • Image segmentation;
  • Multi/hyperspectral image analysis;
  • Multi-resolution (hyper-multi spectral) image analysis;
  • Precision agriculture;
  • Precision forestry;
  • GIS applications;
  • Glacier monitoring;
  • Costal changes.
Dr. Roberto Pierdicca
Prof. Eva Savina Malinverni
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 
  • multi-hyper spectral imaging 
  • photogrammetry 
  • environment 
  • artificial intelligence 
  • precision agriculture 
  • forestry 
  • autonomous vehicles

Published Papers (1 paper)

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Research

24 pages, 8047 KiB  
Article
Bathymetric Detection of Fluvial Environments through UASs and Machine Learning Systems
by Pontoglio Emanuele, Grasso Nives, Cagninei Andrea, Camporeale Carlo, Dabove Paolo and Lingua Andrea Maria
Remote Sens. 2020, 12(24), 4148; https://doi.org/10.3390/rs12244148 - 18 Dec 2020
Cited by 10 | Viewed by 2018
Abstract
In recent decades, photogrammetric and machine learning technologies have become essential for a better understanding of environmental and anthropic issues. The present work aims to respond one of the most topical problems in environmental photogrammetry, i.e., the automatic classification of dense point clouds [...] Read more.
In recent decades, photogrammetric and machine learning technologies have become essential for a better understanding of environmental and anthropic issues. The present work aims to respond one of the most topical problems in environmental photogrammetry, i.e., the automatic classification of dense point clouds using the machine learning (ML) technology for the refraction correction on the fluvial water table. The applied methodology for the acquisition of multiple photogrammetric flights was made through UAV drones, also in RTK configuration, for various locations along the Orco River, sited in Piedmont (Italy) and georeferenced with GNSS—RTK topographic method. The authors considered five topographic fluvial cross-sections to set the correction methodology. The automatic classification in ML has found a valid identification of different patterns (Water, Gravel bars, Vegetation, and Ground classes), in specific hydraulic and geomatic conditions. The obtained results about the automatic classification and refraction reduction led us the definition of a new procedure, with precise conditions of validity. Full article
(This article belongs to the Special Issue Unmanned Aerial Systems (UASs) for Environmental Applications)
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