Special Issue "Progress on the Use of UAS Techniques for Environmental Monitoring"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (20 December 2019).

Special Issue Editors

Dr. Salvatore Manfreda
Website
Guest Editor
Dipartimento delle Culture Europee e del Mediterraneo: Architettura, Ambiente, Patrimoni Culturali (DiCEM), Università degli Studi della Basilicata, via dell’Ateneo Lucano, 10, 85100 Potenza – ITA, Italy
Interests: stochastic processes; hydrological modelling; flood risk; geomorphology; ecohydrology; UAS monitoring
Special Issues and Collections in MDPI journals
Dr. Brigitta Szabó (Tóth)
Website
Guest Editor
Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Budapest, Hungary
Interests: soil science; machine learning; pedotransfer functions; predictive soil mapping; uncertainty assessment
Special Issues and Collections in MDPI journals
Dr. Giorgios Mallinis
Website
Guest Editor
1. Laboratory of Photogrammetry and Remote Sensing, The Polytechnical School, Aristotle University of Thessaloniki, Greece 2. Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Xanthi, Greece
Interests: remote sensing; land use/land cover (LULC) mapping; classification development and comparison; geographic object-based image analysis; natural disasters; UAS
Special Issues and Collections in MDPI journals
Asst. Prof. Antonino Maltese
Website
Guest Editor
Dipartimento di Ingegneria, University of Palermo, 90128 Palermo, Italy
Interests: remote sensing; thermal infrared; geomatics
Special Issues and Collections in MDPI journals
Dr. Matthew Perks
Website
Guest Editor
School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: geomorphology; hydrology; catchment science, image velocimetry techniques
Prof. Dr. Zhongbo Su
Website
Guest Editor
University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), The Netherlands
Interests: remote sensing and numerical modeling of land surface processes and interactions with the atmosphere, earth observation of water cycle and applications in climate, ecosystem and water resources studies
Special Issues and Collections in MDPI journals
Dr. Jana Müllerová
Website
Guest Editor
Department of GIS and Remote Sensing, Institute of Botany, The Czech Academy of Sciences, Průhonice, 25243, Czech Republic
Interests: Environmental Monitoring; Plant Ecology; Vegetation; Nature Conservation; Invasive Species; High Resolution Imagery; OBIA; Unmanned Aircraft

Special Issue Information

Dear Colleagues,

Environmental monitoring is critical for comprehending climate impact on natural and agricultural systems, understanding ecological processes, optimizing water resources, and preventing natural disasters. In this context, one of the greatest potentials in environmental monitoring is represented by the use of Unmanned Aerial Systems (UASs), growing rapidly in last decade.

UASs offer the extraordinary opportunity to fill the existing gap between remote sensing and field measurements by providing high spatial resolution measurements at high frequency. They allow to extend and improve the description of river basin hydrology, agricultural systems, and natural ecosystems, affording an impressive level of detail. Several new UAS-based approaches have been recently introduced to monitor vegetation state, crop production, soil water content, river evolution, and stream flow during low-flow and floods.

The Special Issue is dedicated to multidisciplinary contributions focusing on the demonstration of the benefit of UAS data and algorithms for environmental monitoring. The research presented might focus on:

- Added value of UAS data in environmental monitoring;

- Methods and procedures for UAS data processing;

- Use of UAS in precision farming;

- Innovative applications of UAS data for rapid environmental mapping and change detection;

- Advanced applications of UAS data for monitoring vegetation state, crop production, soil water content, river evolution, and stream flow;

- Potential of different sensors (e.g., thermal, visual, radar, laser, and/or their fusion) and algorithms for environmental variables.

Dr. Salvatore Manfreda
Dr. Brigitta Toth
Dr. Giorgios Mallinis
Dr. Antonino Maltese
Dr. Matthew Perks
Dr. Zhongbo Su
Dr. Eyal Ben-Dor
Dr. Jana Müllerová
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. 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 2000 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

  • Evironmental Monitoring
  • UAS
  • Precision agriculture
  • Soil moisture
  • Vegetation
  • River systems

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees
Remote Sens. 2020, 12(7), 1106; https://doi.org/10.3390/rs12071106 - 31 Mar 2020
Cited by 2
Abstract
3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction [...] Read more.
3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction of vegetation. In this paper, we propose a novel application for the fusion of multispectral images and high-resolution point clouds of an olive orchard. Our methodology is based on a multi-temporal approach to study the evolution of olive trees. This process is fully automated and no human intervention is required to characterize the point cloud with the reflectance captured by multiple multispectral images. The main objective of this work is twofold: (1) the multispectral image mapping on a high-resolution point cloud and (2) the multi-temporal analysis of morphological and spectral traits in two flight campaigns. Initially, the study area is modeled by taking multiple overlapping RGB images with a high-resolution camera from an unmanned aerial vehicle (UAV). In addition, a UAV-based multispectral sensor is used to capture the reflectance for some narrow-bands (green, near-infrared, red, and red-edge). Then, the RGB point cloud with a high detailed geometry of olive trees is enriched by mapping the reflectance maps, which are generated for every multispectral image. Therefore, each 3D point is related to its corresponding pixel of the multispectral image, in which it is visible. As a result, the 3D models of olive trees are characterized by the observed reflectance in the plant canopy. These reflectance values are also combined to calculate several vegetation indices (NDVI, RVI, GRVI, and NDRE). According to the spectral and spatial relationships in the olive plantation, segmentation of individual olive trees is performed. On the one hand, plant morphology is studied by a voxel-based decomposition of its 3D structure to estimate the height and volume. On the other hand, the plant health is studied by the detection of meaningful spectral traits of olive trees. Moreover, the proposed methodology also allows the processing of multi-temporal data to study the variability of the studied features. Consequently, some relevant changes are detected and the development of each olive tree is analyzed by a visual-based and statistical approach. The interactive visualization and analysis of the enriched 3D plant structure with different spectral layers is an innovative method to inspect the plant health and ensure adequate plantation sustainability. Full article
(This article belongs to the Special Issue Progress on the Use of UAS Techniques for Environmental Monitoring)
Show Figures

Figure 1

Open AccessFeature PaperArticle
Drone-Based Optical Measurements of Heterogeneous Surface Velocity Fields around Fish Passages at Hydropower Dams
Remote Sens. 2020, 12(3), 384; https://doi.org/10.3390/rs12030384 - 25 Jan 2020
Cited by 1
Abstract
In Austria, more than a half of all electricity is produced with the help of hydropower plants. To reduce their ecological impact, dams are being equipped with fish passages that support connectivity of habitats of riverine fish species, contributing to hydropower sustainability. The [...] Read more.
In Austria, more than a half of all electricity is produced with the help of hydropower plants. To reduce their ecological impact, dams are being equipped with fish passages that support connectivity of habitats of riverine fish species, contributing to hydropower sustainability. The efficiency of fish passages is being constantly monitored and improved. Since the likelihood of fish passages to be discovered by fish depends, inter alia, on flow conditions near their entrances, these conditions have to be monitored as well. In this study, we employ large-scale particle image velocimetry (LSPIV) in seeded flow conditions to analyse images of the area near a fish passage entrance, captured with the help of a ready-to-fly consumer drone. We apply LSPIV to short image sequences and test different LSPIV interrogation area sizes and correlation methods. The study demonstrates that LSPIV based on ensemble correlation yields velocities that are in good agreement with the reference values regarding both magnitude and flow direction. Therefore, this non-intrusive methodology has a potential to be used for flow monitoring near fish passages on a regular basis, enabling timely reaction to undesired changes in flow conditions when possible. Full article
(This article belongs to the Special Issue Progress on the Use of UAS Techniques for Environmental Monitoring)
Show Figures

Graphical abstract

Open AccessArticle
UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features
Remote Sens. 2019, 11(7), 890; https://doi.org/10.3390/rs11070890 - 11 Apr 2019
Cited by 5
Abstract
Accurate estimation of above ground biomass (AGB) is very important for crop growth monitoring. The objective of this study was to estimate rice biomass by utilizing structural and meteorological features with widely used spectral features. Structural features were derived from the triangulated irregular [...] Read more.
Accurate estimation of above ground biomass (AGB) is very important for crop growth monitoring. The objective of this study was to estimate rice biomass by utilizing structural and meteorological features with widely used spectral features. Structural features were derived from the triangulated irregular network (TIN), which was directly built from structure from motion (SfM) point clouds. Growing degree days (GDD) was used as the meteorological feature. Three models were used to estimate rice AGB, including the simple linear regression (SLR) model, simple exponential regression (SER) model, and machine learning model (random forest). Compared to models that do not use structural and meteorological features (NDRE, R2 = 0.64, RMSE = 286.79 g/m2, MAE = 236.49 g/m2), models that include such features obtained better estimation accuracy (NDRE*Hcv/GDD, R2 = 0.86, RMSE = 178.37 g/m2, MAE = 127.34 g/m2). This study suggests that the estimation accuracy of rice biomass can benefit from the utilization of structural and meteorological features. Full article
(This article belongs to the Special Issue Progress on the Use of UAS Techniques for Environmental Monitoring)
Show Figures

Figure 1

Open AccessArticle
Mapping and Monitoring of Biomass and Grazing in Pasture with an Unmanned Aerial System
Remote Sens. 2019, 11(5), 473; https://doi.org/10.3390/rs11050473 - 26 Feb 2019
Cited by 5
Abstract
The tools available to farmers to manage grazed pastures and adjust forage demand to grass growth are generally rather static. Unmanned aerial systems (UASs) are interesting versatile tools that can provide relevant 3D information, such as sward height (3D structure), or even describe [...] Read more.
The tools available to farmers to manage grazed pastures and adjust forage demand to grass growth are generally rather static. Unmanned aerial systems (UASs) are interesting versatile tools that can provide relevant 3D information, such as sward height (3D structure), or even describe the physical condition of pastures through the use of spectral information. This study aimed to evaluate the potential of UAS to characterize a pasture’s sward height and above-ground biomass at a very fine spatial scale. The pasture height provided by UAS products showed good agreement (R2 = 0.62) with a reference terrestrial light detection and ranging (LiDAR) dataset. We tested the ability of UAS imagery to model pasture biomass based on three different combinations: UAS sward height, UAS sward multispectral reflectance/vegetation indices, and a combination of both UAS data types. The mixed approach combining the UAS sward height and spectral data performed the best (adj. R2 = 0.49). This approach reached a quality comparable to that of more conventional non-destructive on-field pasture biomass monitoring tools. As all of the UAS variables used in the model fitting process were extracted from spatial information (raster data), a high spatial resolution map of pasture biomass was derived based on the best fitted model. A sward height differences map was also derived from UAS-based sward height maps before and after grazing. Our results demonstrate the potential of UAS imagery as a tool for precision grazing study applications. The UAS approach to height and biomass monitoring was revealed to be a potential alternative to the widely used but time-consuming field approaches. While reaching a similar level of accuracy to the conventional field sampling approach, the UAS approach provides wall-to-wall pasture characterization through very high spatial resolution maps, opening up a new area of research for precision grazing. Full article
(This article belongs to the Special Issue Progress on the Use of UAS Techniques for Environmental Monitoring)
Show Figures

Figure 1

Back to TopTop