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

Deadline for manuscript submissions: closed (15 February 2025) | Viewed by 10590

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Prof. Dr. Pavel Fiala

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Guest Editor

Department of Theoretical and Experimental Electrical Engineering, Brno University of Technology, Technická 12, 616 00 Brno, Czech Republic
Interests: numerical modeling; coupled model; sensing;measurement method; low-level measurement; electromagnetic field; photonics; noise spectroscopy; IR measurement
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Prof. Dr. Petr Marcoň

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Guest Editor

Department of Theoretical and Experimental Electrical Engineering, Brno University of Technology, Technická 12, 616 00 Brno, Czech Republic
Interests: measurement; MRI/NQR; spectroscopy; image processing; multispectral imaging; UAV; drones
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Dr. Jiri Maxa

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Guest Editor

Department of Electrical and Electronic Technology, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 10, 616 00 Brno, Czech Republic
Interests: numerical modeling; fluid flow model; electron microscope technology; measurement method; mechanical modeling
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Dr. Jiri Janousek

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Guest Editor

Department of Theoretical and Experimental Electrical Engineering, Brno University of Technology, Technická 12, 616 00 Brno, Czech Republic
Interests: measurement; image processing; multispectral imaging; UAV; drones; signal processing; low-level measurement; experiments
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Special Issue Information

Dear Colleagues,

Precision agriculture is an internationally recognized concept and term referring to land cultivation by means of nontraditional technologies that were first designed and developed at the end of the 1980s.

The aim of the concept rests in adjusting cultivation procedures to suit local conditions, the main principle being to perform the crop-growing tasks at the right place, intensity, and time.

The optimal crop harvest time differs between individual harvest scenarios, depending on the intended use of the crop and on the technical equipment of the actual farm.

It is therefore economically significant to specify the period as precisely as possible. There is a scientific space which uses a more detail-oriented approach for estimating the correct harvest time; the method focuses on the relationship between ripeness data obtained via photogrammetry and parameters produced by the chemical analysis of the crop.

A corresponding imaging methodology using an unmanned aerial vehicle (UAV) equipped with a spectral camera allows spectral reflectance values to be obtained and vegetation indices to be calculated.

The topics of interest for this Special Issue include but are not limited to:

Image processing theory;
Experimental measurement and validation in the laboratory or in situ;
Sensing and signal processing of electrical and non-electrical quantities (e.g., images);
Methods of identifying the type, species and parts of melts, organisms or micro-organisms (crop diseases);
Mathematical methods and procedures to achieve the above main objectives.

Prof. Dr. Pavel Fiala
Prof. Dr. Petr Marcoň
Dr. Jiri Maxa
Dr. Jiri Janousek
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

UAV
measurement
data transfer
image evaluation
image processing
multispectral imaging
sensing techniques

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Published Papers (5 papers)

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Research

19 pages, 10968 KiB

Open AccessArticle

Monitoring Abiotic Stressors in Rainfed Vineyards Involves Combining UAV and Field Monitoring Techniques to Enhance Precision Management

by Federico Valerio Moresi, Pasquale Cirigliano, Andrea Rengo, Elena Brunori, Rita Biasi, Giuseppe Scarascia Mugnozza and Mauro Maesano

Remote Sens. 2025, 17(5), 803; https://doi.org/10.3390/rs17050803 - 25 Feb 2025

Viewed by 746

Abstract

Future climate conditions may jeopardize the suitability of traditional grape-growing areas in the Mediterranean. However, precise vineyard management is a crucial component of adaptation strategies aimed at optimizing resource efficiency, which is essential for sustainable farming practices. A fine-scale characterization, based on the spatial variability of soil’s physical–chemical and hydrological traits combined with temporal variability of vine canopy temperature extracted from UAV thermal images has been adopted in a rainfed vineyard of central Italy, for better understanding the impact of soil and climate abiotic factors in the vineyard for planning precision adaptation strategies encouraging sustainable resource use. This study identifies significant soil heterogeneity within the tested vineyard, affecting water retention, nutrient availability, and vine water stress. We combined ground-based measurements with remote sensing-enhanced data spatialization and helped to advocate for site-specific management techniques as short- and long-term strategies (such as canopy management, deficit irrigation, and compost application) to counter climate emergencies, restore soil health, and preserve vine function and economic yields. Full article

(This article belongs to the Special Issue Innovative UAV Applications)

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23 pages, 5437 KiB

Open AccessArticle

Navigation of a Team of UAVs for Covert Video Sensing of a Target Moving on an Uneven Terrain

by Talal S. Almuzaini and Andrey V. Savkin

Remote Sens. 2024, 16(22), 4273; https://doi.org/10.3390/rs16224273 - 16 Nov 2024

Viewed by 843

Abstract

Unmanned aerial vehicles (UAVs) have become essential tools with diverse applications across multiple sectors, including remote sensing. This paper presents a trajectory planning method for a team of UAVs aimed at enhancing covert video sensing in uneven terrains and urban environments. The approach establishes a feasible flight zone, which dynamically adjusts to accommodate line of sight (LoS) occlusions caused by elevated terrains and structures between the UAVs’ sensors and the target. By avoiding ‘shadows’—projections of realistic shapes on the UAVs’ operational plane that represent buildings and other obstacles—the method ensures continuous target visibility. This strategy optimizes UAV trajectories, maintaining covertness while adapting to the changing environment, thereby improving overall video sensing performance. The method’s effectiveness is validated through comprehensive MATLAB simulations at both single and multiple UAV levels, demonstrating its ability to prevent LoS occlusions while preserving a high level of camouflage. Full article

(This article belongs to the Special Issue Innovative UAV Applications)

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19 pages, 5996 KiB

Open AccessArticle

Proximal Sensing for Characterising Seaweed Aquaculture Crop Conditions: Optical Detection of Ice-Ice Disease

by Evangelos Alevizos, Nurjannah Nurdin, Agus Aris and Laurent Barillé

Remote Sens. 2024, 16(18), 3502; https://doi.org/10.3390/rs16183502 - 21 Sep 2024

Cited by 1 | Viewed by 1878

Abstract

Crop monitoring is a fundamental practice in seaweed aquaculture. Seaweeds are vulnerable to several threats such as ice-ice disease (IID) causing a whitening of the thallus due to depigmentation. Crop condition assessment is important for minimizing yield losses and improving the biosecurity of seaweed farms. The recent influence of modern technology has resulted in the development of precision aquaculture. The present study focuses on the exploitation of spectral reflectance in the visible and near-infrared regions for characterizing the crop condition of two of the most cultivated Eucheumatoids species: Kappaphycus alvareezi and Eucheuma denticulatum. In particular, the influence of spectral resolution is examined towards discriminating: (a) species and morphotypes, (b) different levels of seaweed health (i.e., from healthy to completely depigmented) and (c) depigmented from silted specimens (thallus covered by a thin layer of sediment). Two spectral libraries were built at different spectral resolutions (5 and 45 spectral bands) using in situ data. In addition, proximal multispectral imagery using a drone-based sensor was utilised. At each experimental scenario, the spectral data were classified using a Random Forest algorithm for crop condition identification. The results showed good discrimination (83–99% overall accuracy) for crop conditions and morphotypes regardless of spectral resolution. According to the importance scores of the hyperspectral data, useful wavelengths were identified for discriminating healthy seaweeds from seaweeds with varying symptoms of IID (i.e., thalli whitening). These wavelengths assisted in selecting a set of vegetation indices for testing their ability to improve crop condition characterisation. Specifically, five vegetation indices (the RBNDVI, GLI, Hue, Green–Red ratio and NGRDI) were found to improve classification accuracy, making them recommended for seaweed health monitoring. Image-based classification demonstrated that multispectral library data can be extended to photomosaics to assess seaweed conditions on a broad scale. The results of this study suggest that proximal sensing is a first step towards effective seaweed crop monitoring, enhancing yield and contributing to aquaculture biosecurity. Full article

(This article belongs to the Special Issue Innovative UAV Applications)

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19 pages, 4003 KiB

Open AccessArticle

Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

by Jiří Janoušek, Petr Marcoň, Přemysl Dohnal, Václav Jambor, Hana Synková and Petr Raichl

Remote Sens. 2023, 15(12), 3152; https://doi.org/10.3390/rs15123152 - 16 Jun 2023

Cited by 5 | Viewed by 2585

Abstract

Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (NDRE), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic. Full article

(This article belongs to the Special Issue Innovative UAV Applications)

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20 pages, 10683 KiB

Open AccessArticle

Using UAV to Identify the Optimal Vegetation Index for Yield Prediction of Oil Seed Rape (Brassica napus L.) at the Flowering Stage

by Vojtěch Lukas, Igor Huňady, Antonín Kintl, Jiří Mezera, Tereza Hammerschmiedt, Julie Sobotková, Martin Brtnický and Jakub Elbl

Remote Sens. 2022, 14(19), 4953; https://doi.org/10.3390/rs14194953 - 4 Oct 2022

Cited by 22 | Viewed by 3266

Abstract

Suitability of the vegetation indices of normalized difference vegetation index (NDVI), blue normalized difference vegetation index (BNDVI), and normalized difference yellowness index (NDYI) obtained by means of UAV at the flowering stage of oil seed rape for the prediction of seed yield and usability of these vegetation indices in the identification of anomalies in the condition of the flowering growth were verified based on the regression analysis. Correlation analysis was performed to find the degree of yield dependence on the values of NDVI, BNDVI, and NDYI indices, which revealed a strong, significant linear positive dependence of seed yield on BNDVI (R = 0.98) and NDYI (R = 0.95). The level of correlation between the NDVI index and the seed yield was weaker (R = 0.70) than the others. Regression analysis was performed for a closer determination of the functional dependence of NDVI, BNDVI, and NDYI indices and the yield of seeds. Coefficients of determination in the linear regression model of NDVI, BNDVI, and NDYI indices reached the following values: R² = 0.48 (NDVI), R² = 0.95 (BNDVI), and R² = 0.90 (NDYI). Thus, it was shown that increased density of yellow flowers decreased the relationship between NDVI and crop yield. The NDVI index is not appropriate for assessing growth conditions and prediction of yields at the flowering stage of oil seed rape. High accuracy of yield prediction was achieved with the use of BNDVI and NDYI. The performed analysis of NDVI, BNDVI, and NDYI demonstrated that particularly the BNDVI and NDYI indices can be used to identify problems in the development of oil seed rape growth at the stage of flowering, for their precise localization, and hence to targeted and effective remedial measures in line with the principles of precision agriculture. Full article

(This article belongs to the Special Issue Innovative UAV Applications)

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