UAV and IoT-based Measurement of Vegetation Indices for Environmental Monitoring

Dear Colleagues,

Innovations in unmanned aerial vehicles (UAV) and on-farm connected Internet-of-Things (IoT) devices has led to novel ways of monitoring agricultural land and whole environments at much higher spatial and temporal resolutions. While big data is helpful for decision-making, there is still a need to integrate this information into a comprehensive and appropriate summary. Vegetation indices, initiated in the research fields of Earth observation through satellites, are one of the solutions to this dimensionality problem, since they integrate different spectral signals into a single numerical value that can be correlated to the phenomena under observation. New challenges have arisen with the advent of multispectral and hyperspectral cameras in this regard. New vegetation indices will also need to be developed to better incorporate this different scale of observation, which is no longer in metres, but rather in centimetres or millimetres. Furthermore, while traditional pixels of information from satellites were really mixels of different surfaces (crop, weed and soil), the pixels generated by UAV/IoT platforms are often pure and represent real parts of some leaves, plants, fruits, vegetables or trees. Finally, the advent of deep learning and edge computing platforms will allow on-site computation and interpretation of vegetation indices.

In this Special Issue of Remote Sensing, we are challenging authors to submit papers on the following:

• Novel application of vegetation indices using UAVs and IoTs;
• Application of vegetation indices using deep learning platforms;
• Novel vegetation indices more adapted to drones, robots and IoT devices;
• New challenges, pitfalls and solutions using vegetation indices in agriculture and environmental monitoring;
• Use of vegetation indices in characterizing on-farm biodiversity.

Dr. Etienne Lord
Dr. Louis Longchamps
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.

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.

• vegetation indices
• hyperspectral/multispectral imagery
• deep learning
• unmanned aerial vehicle
• Internet-of-Things (IoT)
• agriculture
• environment