Canopy gap fraction estimation from digital hemispherical images using sky radiance models and a linear conversion method

Thresholding methods for estimating canopy indices from gamma corrected digital hemispherical images are influenced both by the image interpreter and applicability of the automatic interpretation algorithm. The matrix output data of camera sensors (i.e., raw data) can be used to avoid gamma correction. Nevertheless, the impact of an interpreter or a thresholding algorithm is still present. In this paper, we present a new methodology which applies a previously published linear conversion method of raw data to a single below-canopy image to obtain unbiased estimates of canopy gap fraction. The new methodology derives an above-canopy reference image from a below-canopy image using sky radiance information available in gaps. A sky radiance model and interpolation are used to create the above-canopy image which could alternatively be obtained with a second sensor. To evaluate the new method, we conducted test measurements in three mature forest stands. The comparison of image processing output to LAI-2000 Plant Canopy Analyzer data showed high agreement and repeatability. The proposed method can be applied in versatile future versions of hemispherical image analysis programs to create unbiased comparison data for other manual or automatic image processing methods designed for plant canopies.