Determining factors that impact the calibration of consumer-grade digital cameras used for vegetation analysis

• Published in: International journal of remote sensing Vol. 37; no. 14; pp. 3365 - 3383
• Main Authors: Nguy-Robertson, Anthony L., Brinley Buckley, Emma M., Suyker, Andrew S., Awada, Tala N.
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 17.07.2016
• Subjects: Calibration; Cameras; Consumption; Digital; Digital cameras; Exposure; Leaf area index; Vegetation
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2016.1199061

Digital cameras can collect quantitative leaf data, such as chlorophyll content and leaf area index (LAI), because they act as a simple broadband radiometer. However, a cross-calibration between cameras is needed for the purpose of extracting vegetation information from various image repositories. The objective of this study was to examine the variation between multiple consumer-grade camera types - single reflex lens (SLR), point-and-shoot, and cellphone cameras - for the purpose of collecting reliable quantitative data when monitoring vegetation. The specific objectives were to: 1) identify the optimal light conditions for the calibration procedure, 2) determine the variability of exposure value (EV)-corrected calibrated digital numbers (cDN ev ) values among eight consumer-grade digital cameras, and 3) compare the cDN ev values with the raw digital numbers (DN), exposure-adjusted digital numbers (DN ev ), and calibrated digital numbers (cDN) as these latter three components are easier to compute. This study demonstrated that light intensity was important for calibrating cameras to ensure sensor saturation, and that an improper white-balance setting can negatively impact data collection. In one experiment, the coefficient of variation (CV) between the eight cameras examined in the study was reduced from 29% using raw DN to 16% using cDN ev values. Likewise, the root mean square error in estimating leaf chlorophyll-a using a common vegetation index for digital camera, excess green index (EGI), was reduced from 131 to 96 mg g −2 . However, for both experiments, there was only a weak statistical difference between cDN ev and DN ev , indicating that exposure information was the most useful in minimizing the differences between cameras. Although digital cameras are not nearly as accurate as specialized remote-sensing equipment, they do offer the potential for greater collection opportunities. This study demonstrates the potential of using consumer-grade digital cameras to derive quantitative information from citizen science projects.