Radiometric calibration framework for ultra-high-resolution UAV-derived orthomosaics for large-scale mapping of invasive alien plants in semi-arid woodlands:Harrisia pomanensisas a case study

Abstract

Orthomosaics derived from consumer grade digital cameras on board unmanned aerial vehicles (UAVs) are increasingly being used for biodiversity monitoring and remote sensing of the environment. To have lasting quantitative value, remotely sensed imagery should be calibrated to physical units of reflectance. Radiometric calibration improves the quality of raw imagery for consistent quantitative analysis and comparison across different calibrated imagery. Moreover, calibrating remotely sensed imagery to units of reflectance improves its usefulness for deriving quantitative biochemical and biophysical metrics. Notwithstanding the existing radiometric calibration procedures for correcting single images, studies on radiometric calibration of UAV-derived orthomosaics remain scarce. In particular, this study presents a cost-and time-efficient radiometric calibration framework for designing calibration targets, checking scene illumination uniformity, converting orthomosaic digital numbers to units of reflectance, and accuracy assessment using in situ mean reflectance measurements (i.e. the average reflectance in a particular waveband). The empirical line method was adopted for the development of radiometric calibration prediction equations using mean reflectance values measured in only one spot within a 97 ha orthomosaic for three wavebands, i.e. red, green and blue of the Sony NEX-7 camera. A scene illumination uniformity check experiment was conducted to establish whether 10 randomly distributed regions within the orthomosaic experienced similar atmospheric and illumination conditions. This methodological framework was tested in a relatively flat terrain semi-arid woodland that is invaded by Harrisia pomanensis (the Midnight Lady). The scene illumination uniformity check results showed that at a 95% confidence interval, the prediction equations developed using mean reflectance values measured from only one spot within the scene can be used to calibrate the entire 97 ha RGB orthomosaic. Furthermore, the radiometric calibration accuracy assessment results showed a correlation coefficient r value of 0.977 (p < 0.01) between measured and estimated reflectance values with an overall root

Development of a Simplified Radiometric Calibration Framework for Water-Based and Rapid Deployment Unmanned Aerial System (UAS) Operations

“…Flight altitudes of small UAS (sUAS) typically do not exceed 250 meters due to governmental restrictions on UAS operations [34]. As a result, numerous UAS studies have implemented an empirical line method (ELM) for radiometric calibration of UAS imagery [35,36,[45][46][47][37][38][39][40][41][42][43][44]. The foundation of an ELM is the regression of ground-based spectroradiometer measurements and airborne remotely sensed measurements of a spectrally stable calibration target to develop calibration equations that convert image digital numbers (DNs) to physical at-surface reflectance units [48].…”

“…For example, using nine near-Lambertian calibration targets, Wang and Myint [40] found an exponential relationship between their consumergrade sensor-recorded digital numbers (DNs) and ground-based spectroradiometer reflectance. Following the simplified ELM radiometric calibration framework recommended by Wang and Myint [40], Mafanya et al [45] found that using a calibration target with six gray levels could quantify this nonlinear relationship and produced an accurate image calibration.…”

“…There are problems with using these man-made surfaces because of the lack of control that may translate into noise in the data, such as light-coloured compounds in certain asphalts [48]. For UAS applications, the size of the calibration tarp should not be an issue because of the high image spatial resolution; rather, recent studies have prioritized the cost of the calibration tarp construction because the benefit of using UAS in the first place was the relatively low operating costs to implement a remote sensing system with high spatial resolution and flexible sampling frequency [40,45]. To develop a more cost-effective calibration target, Wang and Myint [40] tested 10 different materials for use as calibration targets and found that Masonite hardboard painted with a nine-level gray gradient worked best.…”

“…Wang and Myint [40] demonstrated the efficacy of a simplified ELM radiometric calibration framework to calibrate UAS imagery on an image-by-image approach, where a calibration target is required in each image prior to the mosaicking of the images to produce the final multi-band orthomosaic; however, this approach is not feasible for relatively large-scale studies (> 90 ha) due to the high spatial resolution and relatively small footprint of UAS images [52]. Instead, Mafanya et al [45] proposed the potential for calibrating a large uniform area with calibration equations derived from single point. While their proposed large-scale simplified ELM radiometric calibration presents a cost-effective and time-efficient framework for converting UAS DNs to reflectance units, Mafanya et al [45] mention there is a need to test this method on larger areas.…”

A Full-Waveform Airborne Laser Scanning Metric Extraction Tool for Forest Structure Modelling. Do Scan Angle and Radiometric Correction Matter?

“…The goal of this correction is to reduce errors in the acquired digital values of the pixels due to atmospheric or sensor factors [46]. This process is fundamental when dealing with images acquired from different sensors or on different days [47]. Additionally, radiometric correction may involve converting digital numbers to physical units [48].…”