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Remote Sensing
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Application of Remote Sensing in Agroforestry (Third Edition)
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Application of Remote Sensing in Agroforestry (Third Edition)

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

Deadline for manuscript submissions: closed (20 September 2025) | Viewed by 2632

Special Issue Editors

Dr. Emanuel Peres

E-Mail Website
Guest Editor
Department of Engineering, School of Sciences and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: remote sensing; precision agriculture; in-field data processing; remote monitoring; UAV; UAS; precision forestry; sensors and data processing; human–computer interfaces; augmented reality; virtual reality; embedded systems
Special Issues, Collections and Topics in MDPI journals
Dr. Joaquim João Sousa

E-Mail Website
Guest Editor
Engineering Department, School of Science and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: UAV; image processing algorithms (RGB, NIR, multi- and hyperspectral, thermal and LiDAR sensors); InSAR; precision agriculture; precision forestry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As innovation continues to expand in agriculture and forestry, the integration of advanced technologies plays a pivotal role in reshaping management practices. The management of agricultural and forestry ecosystems requires a nuanced approach to enhance crop yield and quality, adopt sustainable methods, mitigate disease prevalence and reduce reliance on chemical treatments. This necessitates an in-depth understanding of the various factors influencing complex variability.

Remote sensing technology has revolutionized our ability to gather nuanced data across a spectrum of detail levels in both fields. The use of satellites, manned and unmanned aerial vehicles equipped with various sensors, including RGB, NIR, LiDAR, GPR, multi- or hyperspectral and thermal imaging, is becoming increasingly important in agriculture and forestry management.

In addition, the increasing amount of data generated by remote sensing technologies requires advanced systems that can process and integrate this information into useful insights for stakeholders. The use of deep learning and decision support systems enables the conversion of large datasets into understandable and valuable information through automated or semi-automated processes.

The third edition of this Special Issue aims to showcase the latest research, methodologies, algorithms, best practices and novel applications in remote sensing. The focus is on pushing the envelope in data analysis, visualization and practical application. Contributions are invited to advance the field through innovations in deep learning for remote sensing, decision support and management systems tailored to agroforestry, precise forecasting models for the yield and disease prediction, innovative precision agriculture and forestry approaches, and pioneering techniques for automatic yield and disease mapping and data visualization.

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 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 technologies
  • unmanned aerial vehicles (UAVs)
  • deep learning applications
  • decision support systems
  • yield and disease forecasting models
  • sustainable management practices
  • data visualization and analysis
  • sensor fusion and integration
  • climate impact assessment on agroforestry
  • ecological and environmental monitoring

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (2 papers)

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Research

23 pages, 6045 KB  
Article
Early Warning of Anthracnose on Illicium verum Through the Synergistic Integration of Environmental and Remote Sensing Time Series Data
by Junji Li, Yuxin Zhao, Tianteng Zhang, Jiahui Du, Yucai Li, Ling Wu and Xiangnan Liu
Remote Sens. 2025, 17(19), 3294; https://doi.org/10.3390/rs17193294 - 25 Sep 2025
Abstract
Anthracnose on Illicium verum Hook.f (I. verum) significantly affects the yield and quality of I. verum, and timely detection methods are urgently needed for early control. However, early warning is difficult due to two major challenges, including the sparse availability [...] Read more.
Anthracnose on Illicium verum Hook.f (I. verum) significantly affects the yield and quality of I. verum, and timely detection methods are urgently needed for early control. However, early warning is difficult due to two major challenges, including the sparse availability of optical remote sensing observations due to frequent cloud and rain interference, and the weak spectral responses caused by infestation during early stages. In this article, a framework for early warning of anthracnose on I. verum that combines high-frequency environmental (meteorological and topographical) data and Sentinel-2 remote sensing time-series data, along with a Time-Aware Long Short-Term Memory (T-LSTM) network incorporating an attentional mechanism (At-T-LSTM) was proposed. First, all available environmental and remote sensing data during the study period were analyzed to characterize the early anthracnose outbreaks, and sensitive features were selected as the algorithm input. On this basis, to address the issue of unequal temporal lengths between environmental and remote sensing time series, the At-T-LSTM model incorporates a time-aware mechanism to capture intra-feature temporal dependencies, while a Self-Attention layer is used to quantify inter-feature interaction weights, enabling effective multi-source features time-series fusion. The results show that the proposed framework achieves a spatial accuracy (F1-score) of 0.86 and a temporal accuracy of 83% in early-stage detection, demonstrating high reliability. By integrating remote sensing features with environmental drivers, this approach enables multi-feature collaborative modeling for the risk assessment and monitoring of I. verum anthracnose. It effectively mitigates the impact of sparse observations and significantly improves the accuracy of early warnings. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry (Third Edition))
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20 pages, 5338 KB  
Article
Early Detection of Dendroctonus valens Infestation with UAV-Based Thermal and Hyperspectral Images
by Peiyun Bi, Linfeng Yu, Quan Zhou, Jinjia Kuang, Rui Tang, Lili Ren and Youqing Luo
Remote Sens. 2024, 16(20), 3840; https://doi.org/10.3390/rs16203840 - 16 Oct 2024
Cited by 1 | Viewed by 1639
Abstract
Dendroctonus valens is one of the main invasive pests in China, causing serious economic and ecological damage. Early detection and control of D. valens can help prevent further outbreaks. Based on unmanned aerial vehicle (UAV) thermal infrared and hyperspectral data, we compared the spectral [...] Read more.
Dendroctonus valens is one of the main invasive pests in China, causing serious economic and ecological damage. Early detection and control of D. valens can help prevent further outbreaks. Based on unmanned aerial vehicle (UAV) thermal infrared and hyperspectral data, we compared the spectral characteristics of Pinus sylvestris var. mongolica in three states (healthy, early-infested, and dead), and constructed a classification model based on the random forest algorithm using four spectral datasets (reflectance, first derivative, second derivative, and spectral vegetation index) and one temperature parameter dataset. Our results indicated that the spectral differences between healthy and early-infested trees mainly occur in the near-infrared region, with dead trees showing different characteristics. While it was effective to distinguish healthy from early-infested trees using spectral data alone, the addition of a temperature parameter further improved classification accuracy across all datasets. The combination of the spectral vegetation index and temperature parameter achieved the highest accuracy at 93.75%, which is 3.13% higher than using the spectral vegetation index alone. This combination also significantly improved early detection precision by 13.89%. Our findings demonstrated the applicability of UAV-based thermal infrared and combined hyperspectral datasets in monitoring D. valens early-infested trees, providing important technical support for the scientific prevention and control of D. valens. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry (Third Edition))
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