Enhance Estimation of Canopy Gap Fraction: Adaptive 2D Gamma Adjustment Algorithm for Raw Image

In the process of using digital canopy photography to measure Leaf Area Index (LAI), the estimation of canopy gap fraction (GF) is a critical factor that significantly influences LAI calculations. The accuracy of gap fraction estimates directly impacts the calculated LAI values. Typically, upward canopy images for gap fraction estimation are sensitive to exposure conditions. In this study, we conducted two experiments: the first experiment involved capturing canopy images with different lens focal lengths and relative exposures using a perforated test board with precisely known gap fractions; the second experiment employed a fixed-position fisheye lens to continuously capture a series of canopy images with varying shutter speeds. During the image processing, we utilized an adaptive 2D gamma adjustment algorithm based on luminance components to process the RAW images acquired by the camera during canopy photography. Our findings indicate that the algorithmically processed RAW images exhibit greater stablity to changes in exposure conditions when estimating canopy gap fraction compared to the JPEG images derived by the camera.This suggests the efficacy of the proposed algorithm in enhancing the reliability of gap fraction estimation in canopy photography under varying exposure conditions.