An improved method for edge detection based on neighbor distance for processing hemispheric photography in studying canopy structure and radiative transfer

Hemisphere photos are now widely applied to provide information about solar radiation dynamics, canopy structure and their contribution to biophysical processes, plant productivity and ecosystem properties. The present study aims to improve the original “edge detection” method for binary classification between sky and canopy, which works not well for closed canopies. We supposed such inaccuracy probably is due to the influence of sky pixels on their neighbor canopy pixels. Here we introduced a new term “neighbor distance”, defined as the distance between pixels participated in the calculation of contrast at the edges between classified canopy and sky, into the “edge detection” method. We showed that choosing a suitable neighbor distance for a photo with specific gap fraction can significantly improve the accuracy of the original “edge detection” method. Combining the modified “edge detection” method and an iterative selection method, with the aid of an empirical power function for the relationship between neighbor distance and manually verified gap fraction, we developed a ND-IS (Neighbor Distance-Iteration Selection) method that can automatically determine the threshold values of hemisphere photos with high accuracy and reproductivity. This procedure worked well throughout a broad range of gap fraction (0.019 to 0.945) with different canopy composition and structure, in five forest biomes along a broad gradient of latitude and longitude across Eastern China. Our results highlight the necessity of integrating neighbor distance into the original “edge detection” algorithm. The advantages and limitations of the method, and the application of the method in the field were also discussed.