Special Issue "Application of Remote Sensing in Agroforestry"

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

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Dr. Emanuel Peres
E-Mail Website
Guest Editor
University of Trás-os-Montes e Alto Douro & INESC TEC, Quinta de Prados, 5000-801 Vila Real, Portugal
Interests: distributed sensor networks; precision agriculture; UAS; precision viticulture; machine learning; precision viticulture
Special Issues and Collections in MDPI journals
Prof. Dr. Joaquim João Moreira de Sousa
E-Mail Website
Guest Editor
Department of Engineering, School of Sciences and Technology of the University of Trás-os-Montes e Alto Douro, Vila Real, 5000-801, Portugal
Interests: UAS; image processing algorithms (RGB, NIR, multi- and hyperspectral, thermal and LiDAR sensors); InSAR; precision agriculture; precision forestry
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Technological development, integration, and adoption in both agriculture and forestry management practices continues to grow. The need to increase yield and quality and introduce sustainable practices while simultaneously reducing disease incidence and minimizing chemical inputs requires careful and detailed management. Knowledge with the highest detail level possible about context, culture, and environmental parameters that can influence both agriculture and forests’ high variabilities is needed to improve management practices.

Remote sensing enables the acquisition of diverse data with variable levels of detail, both in agriculture and in forestry. Indeed, the use of satellites, manned aircrafts, and unmanned aerial vehicles, equipped with different types of sensors (e.g., RGB, NIR, LiDAR, multi- and hyperspectral and thermal) has been gaining special attention in their different applications in agriculture and forests.

Moreover, the need for systems that are able to deal with the massive amounts of data being generated by remote sensing is also emerging. They must be capable of aggregating and extracting useful and intelligible information to stakeholders, preferably in a (semi)automatic way, throughout the application of deep learning.

This Special Issue aims at collecting new developments, methodologies, algorithms, best practices, and applications in remote sensing. We welcome submissions that provide the community with the most recent advancements on all aspects of remote sensing.

Dr. Emanuel Peres
Dr. Joaquim João Sousa
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.


  • Deep learning in remote sensing
  • Decision support systems
  • Forecasting models (e.g., yield, diseases)
  • Management systems
  • Box-to-box approaches in precision agriculture and precision forestry
  • Automatic yield/diseases mapping
  • Data visualization

Published Papers (1 paper)

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Open AccessArticle
Optimal Spectral Wavelengths for Discriminating Orchard Species Using Multivariate Statistical Techniques
Remote Sens. 2020, 12(1), 63; https://doi.org/10.3390/rs12010063 - 23 Dec 2019
Sustainable management of orchard fields requires detailed information about the tree types, which is a main component of precision agriculture programs. To this end, hyperspectral imagery can play a major role in orchard tree species mapping. Efficient use of hyperspectral data in combination [...] Read more.
Sustainable management of orchard fields requires detailed information about the tree types, which is a main component of precision agriculture programs. To this end, hyperspectral imagery can play a major role in orchard tree species mapping. Efficient use of hyperspectral data in combination with field measurements requires the development of optimized band selection strategies to separate tree species. In this study, field spectroscopy (350 to 2500 nm) was performed through scanning 165 spectral leaf samples of dominant orchard tree species (almond, walnut, and grape) in Chaharmahal va Bakhtiyari province, Iran. Two multivariable methods were employed to identify the optimum wavelengths: the first includes three-step approach ANOVA, random forest classifier (RFC) and principal component analysis (PCA), and the second employs partial least squares (PLS). For both methods we determined whether tree species can be spectrally separated using discriminant analysis (DA) and then the optimal wavelengths were identified for this purpose. Results indicate that all species express distinct spectral behaviors at the beginning of the visible range (from 350 to 439 nm), the red edge and the near infrared wavelengths (from 701 to 1405 nm). The ANOVA test was able to reduce primary wavelengths (2151) to 792, which had a significant difference (99% confidence level), then the RFC further reduced the wavelengths to 118. By removing the overlapping wavelengths, the PCA represented five components (99.87% of variance) which extracted optimal wavelengths were: 363, 423, 721, 1064, and 1388 nm. The optimal wavelengths for the species discrimination using the best PLS-DA model (100% accuracy) were at 397, 515, 647, 1386, and 1919 nm. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry)
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