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

Abstract

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 OPEN ACCESSRemote Sens. 2013, 5 5007 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.

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

“…The effect was the most noticeable in the 'Barley UAV 140 m' dataset, which was collected during 4.5 h, when illumination changed significantly, and in the 'Grass UAV 50 m' dataset, which was collected during sunny conditions at a low flying height that caused remarkable anisotropy effects ( Figure 5). Multiple studies have shown that radiometric correction using the RBA method improved the uniformity of image orthomosaics [7,11,12,63,77]. Our results showed that the corrections also improved the accuracy of the crop parameter estimations.…”

“…where A priori values and standard deviations used in this study (Table 6) were selected based on suggestions by Honkavaara et al [7,63]. During the drone-based grass data collection, weather was mainly sunny (see Table 2); therefore, we used the BRDF correction to compensate for the reflectance anisotropy effects.…”

“…First the sensor response was corrected for the FPI images using the dark signal correction and the photon response nonuniformity correction (PRNU) [6,7]. The dark signal correction was calculated using a black image collected right before the data capture with a covered lens, and the PRNU correction was determined in the laboratory.…”

“…A hexacopter drone with a Tarot 960 foldable frame belonging to the FGI was equipped with a hyperspectral camera based on a tuneable FPI and a high-quality Samsung NX500 RGB camera. In this study, the FGI2012b FPI camera [6,7,58] was used; it was configured with 36 spectral bands in the 500 nm to 900 nm spectral range (Table 3). The drone had a NV08C-CSM L1 GNSS receiver (NVS Navigation Technologies Ltd., Montlingen, Switzerland) and a Raspberry Pi single-board computer (Raspberry Pi Foundation, Cambridge, UK).…”

“…Radiometric processing of the hyperspectral datasets was carried out using FGI's RadBA software [7,63]. The objective of the radiometric correction was to provide accurate reflectance orthomosaics.…”