Abstract
Unmanned aircraft systems (UAS) allow us to collect aerial data at high spatial and temporal resolution. Raw images are taken along a predetermined flight path and processed into a single raster file covering the entire study area. Radiometric calibration using empirical or manufacturer methods is required to convert raw digital numbers into reflectance and to ensure data accuracy. The performance of five radiometric calibration methods commonly used was investigated in this study. Multispectral imagery was collected using a Parrot Sequoia camera. No method maximized data accuracy in all bands. Data accuracy was higher when the empirical calibration was applied to the processed raster rather than the raw images. Data accuracy achieved with the manufacturer-recommended method was comparable to the one achieved with the best empirical method. Radiometric error in each band varied linearly with pixel radiometric values. Smallest radiometric errors were obtained in the red-edge and near-infrared (NIR) bands. Accuracy of the composite indices was higher for the pixels representing a dense vegetative cover in comparison to a lighter cover or bare soil. Results provided a better understanding of the advantages and limitations of existing radiometric calibration methods as well as the impact of the radiometric error on data quality. The authors recommend that researchers evaluate the performance of their radiometric calibration before analyzing UAS imagery and interpreting the results.
Highlights
Before small unmanned aircraft systems (UAS) became available, aerial imagery was solely acquired by satellite or aircraft
D resulted in better data accuracy than method B, and results indicated that data quality provided by the manufacturer-recommended calibration could be further improved with an empirical calibration
E resulted in better data accuracy than method C, and results showed that calibrating the processed uncalibrated rasters would be preferable to calibrating the raw images before data processing
Summary
Before small unmanned aircraft systems (UAS) became available, aerial imagery was solely acquired by satellite or aircraft. Satellite images cover large surfaces with a ground sampling distance of 30 m/px for the newest systems and a revisit time of seven to 16 days [1,2] This provides valuable information for large-scale Earth observation applications such as weather forecasting, land use, land cover classification, and monitoring of the Earth environment [3]. The recent development of UAS has become a viable alternative to conventional platforms [4] These systems can be equipped with Red-Green-Blue (RGB), multispectral, thermal, and hyperspectral cameras producing high spatial resolution imagery with ground sampling distances of a few centimeters. They can be flown whenever weather conditions are favorable, permitting data acquisition at a high temporal resolution [5]
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