Application of UAS-Based Spectral Imaging in Agriculture and Forestry

Dear Colleagues,

Following trends in the increasing miniaturization and automation of both platforms and sensors over the last 5–10 years, unoccupied aircraft system (UAS)-based spectral imaging has grown from pure research to operational deployment in agriculture and forestry. From detecting crop issues such as drought and disease at an early stage to quantifying zonal or plant-specific characteristics, supporting phenotyping or variable treatment, refining and filling gaps in satellite measurements, and generalizing in-situ and proximally sensed data, the variety in applications reported in the literature continues to expand. In addition to innovations in sensor technology, recent advancements in machine learning are playing an important role in opening new applications. This shows the continuing relevance of UAS-based spectral imaging in understanding local vegetation processes, as a crucial aspect in dealing with climate change and environmental pressures in agriculture and forestry.

Despite these recent advancements, important questions remain. Some of these include:

• Which scientific or operational applications in agriculture and forestry still require narrowband VNIR multi- and hyperspectral imaging, given that learning-based approaches have sparked a renewed interest in exploiting the generally wider availability, lower acquisition cost and higher spatial resolutions of drone-based RGB cameras (even for applications that previously seemed unachievable without higher spectral resolution)?
• Can fusion with thermal, LiDAR or non-imaging spectrometer data and machine learning approaches help to overcome deficiencies in the spatial resolution of drone-based spectral cameras?
• How can insights gained from advanced drone-based spectral imaging techniques be transferred to more operationally feasible methods that would increase uptake in agriculture and forestry in resource-limited areas?
• What are the trade-offs between multiple available drone-based hyperspectral imaging techniques, and will there be an evolution towards a single optimal all-round solution?
• Which new areas of research in agriculture and forestry can benefit from recent advances in drone-based spectral imaging, for example, using SWIR cameras?

This Special Issue aims to answer some of these questions by:

• Bundling state-of-the-art scientific experimental results and novel methods that promote operational capabilities and increased integration with in-situ, airborne and satellite measurements.
• Providing a forum for reviews which identify outstanding knowledge gaps and compelling future research paths.

We also aim to maximize diversity in drone-based spectral imaging in agriculture and forestry literature by encouraging contributions from all kinds of research backgrounds that are typically underrepresented in this field.

Dr. Klaas Pauly
Prof. Dr. Hiep Luong
Guest Editors

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• UAS-based spectral imaging
• agriculture
• forestry
• multispectral
• hyperspectral
• data fusion
• machine learning