How to reduce day-to-day variation of leaf area index derived from digital cover photography?
Abstract:Leaf area index (LAI) is essential for computing canopy level carbon and water fluxes. Nowadays, it is possible to automatically monitor daily LAI using low-cost sensors, such as digital cameras and LED-sensors. Recent studies have shown that RAW camera format images can improve the estimation of gap fractions and LAI compared to JPEG format. However, whether RAW-based methods can effectively reduce day-to-day variation of LAI time series has not been investigated.
In this study, we used two methods to compute gap fraction. The first method separates sky and vegetation pixels using a single threshold in the blue band histogram. The second method interpolates the background sky image from pure sky pixels, and computes the transmittance from original and reconstructed images. In order to investigate which method is more accurate in reducing day-to-day variation of LAI, we first conducted a controlled experiment with punched panels which included different hole size and gap fractions on the rooftop. Then, we applied both methods to photos collected daily over a year at deciduous forest and evergreen forest in South Korea.