Application of Remote Sensing in Agroforestry (Third Edition)

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

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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.

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