Abstract
Small unmanned aerial systems (UAS) have allowed the mapping of vegetation at very high spatial resolution, but a lack of standardisation has led to uncertainties regarding data quality. For reflectance measurements and vegetation indices (Vis) to be comparable between sites and over time, careful flight planning and robust radiometric calibration procedures are required. Two sources of uncertainty that have received little attention until recently are illumination geometry and the effect of flying height. This study developed methods to quantify and visualise these effects in imagery from the Parrot Sequoia, a UAV-mounted multispectral sensor. Change in illumination geometry over one day (14 May 2018) had visible effects on both individual images and orthomosaics. Average near-infrared (NIR) reflectance and NDVI in regions of interest were slightly lower around solar noon, and the contrast between shadowed and well-illuminated areas increased over the day in all multispectral bands. Per-pixel differences in NDVI maps were spatially variable, and much larger than average differences in some areas. Results relating to flying height were inconclusive, though small increases in NIR reflectance with height were observed over a black sailcloth tarp. These results underline the need to consider illumination geometry when carrying out UAS vegetation surveys.
Highlights
Changing illumination geometry had a visible effect on reflectance in images from the 14 May flights (Figure 7)
This research investigated the effects of illumination geometry and flying height on imagery captured by a unmanned aerial systems (UAS) with a multispectral sensor
The results showed that changing illumination geometry had visible effects on individual images, due to anisotropic reflectance of the vegetation
Summary
Vegetation mapping using small unmanned aerial systems (UAS) has recently found many applications in environmental and ecological monitoring [1,2,3], forestry [4,5], precision agriculture [6,7]. The influences of collection method and environmental conditions on data quality are poorly understood [9,11]. This creates problems if data from different sources are to be combined, or if time series of observations are to be assembled, as data collected using different sensors and methods or under different environmental conditions may not be comparable
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