Abstract
Viewing and illumination geometry has a strong influence on optical measurements of natural surfaces due to their anisotropic reflectance properties. Typically, cameras on-board unmanned aerial vehicles (UAVs) are affected by this because of their relatively large field of view (FOV) and thus large range of viewing angles. In this study, we investigated the magnitude of reflectance anisotropy effects in the 500–900 nm range, captured by a frame camera mounted on a UAV during a standard mapping flight. After orthorectification and georeferencing of the images collected by the camera, we calculated the viewing geometry of all observations of each georeferenced ground pixel, forming a dataset with multi-angular observations. We performed UAV flights on two days during the summer of 2016 over an experimental potato field where different zones in the field received different nitrogen fertilization treatments. These fertilization levels caused variation in potato plant growth and thereby differences in structural properties such as leaf area index (LAI) and canopy cover. We fitted the Rahman–Pinty–Verstraete (RPV) model through the multi-angular observations of each ground pixel to quantify, interpret, and visualize the anisotropy patterns in our study area. The Θ parameter of the RPV model, which controls the proportion of forward and backward scattering, showed strong correlation with canopy cover, where in general an increase in canopy cover resulted in a reduction of backward scattering intensity, indicating that reflectance anisotropy contains information on canopy structure. In this paper, we demonstrated that anisotropy data can be extracted from measurements using a frame camera, collected during a typical UAV mapping flight. Future research will focus on how to use the anisotropy signal as a source of information for estimation of physical vegetation properties.
Highlights
In optical remote sensing, the sensors are mostly passive systems and their measured signal depends on the position and orientation of the sensor and the position of the sun relative to the observed surface
In the data collected on 9 June 2016, the different initial fertilization levels were clearly visible as horizontal zones in normalized difference vegetation index (NDVI) map of the potato field (Figure 3a)
This study demonstrates that pixel-wise multi-angular observations can be obtained from frame-based cameras by calculation of the View Azimuth Angle (VAA) and VZAs of georeferenced ground pixels that were captured from multiple camera positions
Summary
The sensors are mostly passive systems and their measured signal depends on the position and orientation of the sensor and the position of the sun relative to the observed surface. Differences in the intensity of the measured signal of natural surfaces as a function of observation and viewing geometry are the result of their anisotropic reflectance behavior. Anisotropic reflectance effects are the result of optical and structural properties of the surface. Knowledge of the anisotropic reflectance characteristics of a surface is important for correction and normalization of effects due to viewing and illumination geometry [10,11,12]. It can be considered an information source. Information on anisotropic reflectance effects has shown to improve classification results [13,14,15,16] or parameter retrieval such as leaf area index (LAI) [17,18], canopy height [19], and canopy clumping [20,21]
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