Remote Sensing of 3-D Geometry and Surface Moisture of a Peat Production Area Using Hyperspectral Frame Cameras in Visible to Short-Wave Infrared Spectral Ranges Onboard a Small Unmanned Airborne Vehicle (UAV)

Abstract

Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and shortwave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry-Pérot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area. UAV image blocks were captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. Georeferencing showed consistent behavior, with accuracy levels better than GSD for the FPI cameras. The best accuracy in moisture estimation was obtained when using the reflectance difference of the SWIR band at 1246 nm and of the VNIR band at 859 nm, which gave a root mean square error (rmse) of 5.21 pp (pp is the mass fraction in percentage points) and a normalized rmse of 7.61%. The results are encouraging, indicating that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production.

Optimizing the Processing of UAV-Based Thermal Imagery

“…Most software packages have been extended and allow processing multi-and hyperspectral imagery as well [9,10,19]. Similarly, SfM can be used to process thermal images, which is the focus of this study.…”

Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment

“…In recent studies, the radiometric correction of UAV imagery and spectrometry has mostly emphasized the atmospheric correction using reflectance panels [6,[12][13][14][15][31][32][33][34][35][36][37], atmospheric radiative transfer modeling [2,4] or normalization of UAV based radiance observations using field based radiance observations of a reference panel or irradiance [7,8,31]. Aasen et al [13] and Yang et al [15] calibrated the Cubert UHD 185-Firefly hyperspectral imagery; they emphasized the sensor calibration and performed reflectance calibration using single or multiple reflectance panels.…”

Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

“…Burkart et al [33] used a hyperspectral UAV-based point spectrometer to collect multi-angular data of several vegetated areas and compared the measurements with the SCOPE model. Honkavaara et al [34] have demonstrated a method for extracting the reflectance anisotropy from multi/hyperspectral frame images in the georectification process. Duan et al [35] collected hyperspectral images of several crop types during two consecutive flights performed in different directions using a UAV, which resulted in pixels that were observed from two directions in the overlapping part of the two flight lines.…”