Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
New Insights into the Use of Small-Unmanned Aircraft Systems for...
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

New Insights into the Use of Small-Unmanned Aircraft Systems for Environmental Assessment and Monitoring

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 7816

Special Issue Editors

Dr. Niall Burnside

E-Mail Website
Guest Editor
School of Environment & Technology, The University of Brighton, Brighton, UK
Interests: GIS; remote sensing; UAS and UAV; spatial analysis; environmental sciences; ecology conservation; landscape change
Dr. Jonathan Dale

E-Mail Website
Guest Editor
Centre for Agroecology, Water and Resilience & School of Energy, Construction and Environment, Coventry University, Coventry, UK
Interests: coastal wetlands; estuarine environments; coastal management; remote sensing; GIS

Special Issue Information

Dear Colleagues,

The recent emergence of, and increased accessibility to, small unmanned aircraft systems (sUAS) presents a breadth of new possibilities for environmental assessment and monitoring. This rapidly developing field has been shown, in many cases, to be more effective than more traditional remote sensing methods in meeting the requirements of researchers and practitioners seeking the rapid, adaptable and successful monitoring of management initiatives and approaches. sUAS and sUAS-mounted sensors offer significant opportunities to increase spatial detail and temporal frequency and to assist environmental managers and scientists in bridging the gap between field observations and traditional air- and space-borne remote sensing.

This special edition will bring together a range of papers demonstrating the capacity of sUAS and sUAS-mounted sensors across a diverse range of environmental assessment and management applications.

Dr. Niall Burnside
Dr. Jonathan Dale
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

  • Drones
  • UAV
  • Remote sensing
  • Image processing
  • Structure-from-motion
  • Landscape
  • Environmental management
  • Environmental monitoring

Published Papers (2 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

22 pages, 10975 KiB  
Article
The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials
by Kai-Yun Li, Niall G. Burnside, Raul Sampaio de Lima, Miguel Villoslada Peciña, Karli Sepp, Ming-Der Yang, Janar Raet, Ants Vain, Are Selge and Kalev Sepp
Remote Sens. 2021, 13(10), 1994; https://doi.org/10.3390/rs13101994 - 19 May 2021
Cited by 9 | Viewed by 3620
Abstract
A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool [...] Read more.
A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool when it comes to supporting farm-scale phenotyping trials. In this study, we explored the variation of the red clover-grass mixture dry matter (DM) yields between temporal periods (one- and two-year cultivated), farming operations [soil tillage methods (STM), cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and six multispectral vegetation indices (VIs) to predict DM yields. The ML evaluation results showed the best performance for ANN in the 11-day before harvest category (R2 = 0.90, NRMSE = 0.12), followed by RFR (R2 = 0.90 NRMSE = 0.15), and SVR (R2 = 0.86, NRMSE = 0.16), which was furthermore supported by the leave-one-out cross-validation pre-analysis. In terms of VI performance, green normalized difference vegetation index (GNDVI), green difference vegetation index (GDVI), as well as modified simple ratio (MSR) performed better as predictors in ANN and RFR. However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage. Full article
(This article belongs to the Special Issue New Insights into the Use of Small-Unmanned Aircraft Systems for Environmental Assessment and Monitoring)
►▼ Show Figures

Figure 1

22 pages, 3933 KiB  
Article
An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest
by Raul Sampaio de Lima, Mait Lang, Niall G. Burnside, Miguel Villoslada Peciña, Tauri Arumäe, Diana Laarmann, Raymond D. Ward, Ants Vain and Kalev Sepp
Remote Sens. 2021, 13(6), 1121; https://doi.org/10.3390/rs13061121 - 16 Mar 2021
Cited by 12 | Viewed by 3238
Abstract
The application of unmanned aerial systems (UAS) in forest research includes a wide range of equipment, systems, and flight settings, creating a need for enhancing data acquisition efficiency and quality. Thus, we assessed the effects of flying altitude and lateral and longitudinal overlaps [...] Read more.
The application of unmanned aerial systems (UAS) in forest research includes a wide range of equipment, systems, and flight settings, creating a need for enhancing data acquisition efficiency and quality. Thus, we assessed the effects of flying altitude and lateral and longitudinal overlaps on digital aerial photogrammetry (DAP) processing and the ability of its products to provide point clouds for forestry inventory. For this, we used 18 combinations of flight settings for data acquisition, and a nationwide airborne laser scanning (ALS) dataset as reference data. Linear regression was applied for modeling DAP quality indicators and model fitting quality as the function of flight settings; equivalence tests compared DAP- and ALS-products. Most of DAP-Digital Terrain Models (DTM) showed a moderate to high agreement (R2 > 0.70) when fitted to ALS-based models; nine models had a regression slope within the 1% region of equivalence. The best DAP-Canopy Height Model (CHM) was generated using ALS-DTM with an R2 = 0.42 when compared with ALS-CHM, indicating reduced similarity. Altogether, our results suggest that the optimal combination of flight settings should include a 90% lateral overlap, a 70% longitudinal overlap, and a minimum altitude of 120 m above ground level, independent of the availability of an ALS-derived DTM for height normalization. We also provided insights into the effects of flight settings on DAP outputs for future applications in similar forest stands, emphasizing the benefits of overlaps for comprehensive scene reconstruction and altitude for canopy surface detection. Full article
(This article belongs to the Special Issue New Insights into the Use of Small-Unmanned Aircraft Systems for Environmental Assessment and Monitoring)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop