4.7 Article

Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture

Journal

REMOTE SENSING
Volume 5, Issue 10, Pages 5006-5039

Publisher

MDPI AG
DOI: 10.3390/rs5105006

Keywords

photogrammetry; radiometry; spectrometry; hyperspectral; UAV; DSM; point cloud; biomass; agriculture

Funding

  1. Academy of Finland [134181]
  2. Academy of Finland (AKA) [134181, 134181] Funding Source: Academy of Finland (AKA)

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Imaging using lightweight, unmanned airborne vehicles (UAVs) is one of the most rapidly developing fields in remote sensing technology. The new, tunable, Fabry-Perot interferometer-based (FPI) spectral camera, which weighs less than 700 g, makes it possible to collect spectrometric image blocks with stereoscopic overlaps using light-weight UAV platforms. This new technology is highly relevant, because it opens up new possibilities for measuring and monitoring the environment, which is becoming increasingly important for many environmental challenges. Our objectives were to investigate the processing and use of this new type of image data in precision agriculture. We developed the entire processing chain from raw images up to georeferenced reflectance images, digital surface models and biomass estimates. The processing integrates photogrammetric and quantitative remote sensing approaches. We carried out an empirical assessment using FPI spectral imagery collected at an agricultural wheat test site in the summer of 2012. Poor weather conditions during the campaign complicated the data processing, but this is one of the challenges that are faced in operational applications. The results indicated that the camera performed consistently and that the data processing was consistent, as well. During the agricultural experiments, promising results were obtained for biomass estimation when the spectral data was used and when an appropriate radiometric correction was applied to the data. Our results showed that the new FPI technology has a great potential in precision agriculture and indicated many possible future research topics.

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