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 s...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 classifcation between sky and canopy,which works not well for closed canopies.We supposed such inaccuracy probably is due to the infuence of sky pixels on their neighbor canopy pixels.Here,we introduced a new term‘neighbor distance’,defned as the distance between pixels participated in the calculation of contrast at the edges between classifed canopy and sky,into the‘edge detection’method.We showed that choosing a suitable neighbor distance for a photo with a specifc gap fraction can signifcantly improve the accuracy of the original‘edge detection’method.We developed an ND-IS(Neighbor Distance-Iteration Selection)method that can automatically determine the threshold values of hemisphere photos with high accuracy and reproductivity.It combines the modifed‘edge detection’method and an iterative selection method,with the aid of an empirical power function for the relationship between neighbor distance and manually verifed gap fraction.This procedure worked well throughout a broad range of gap fractions(0.019-0.945)with different canopy compositions and structures,in fve 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 feld were also discussed.展开更多
基金supported by the Fang Jingyun ecological study studio of Yunnan province,the National Natural Science Foundation of China(32271652,32201258)the Major Program for Basic Research Project of Yunnan Province(202101BC070002)。
文摘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 classifcation between sky and canopy,which works not well for closed canopies.We supposed such inaccuracy probably is due to the infuence of sky pixels on their neighbor canopy pixels.Here,we introduced a new term‘neighbor distance’,defned as the distance between pixels participated in the calculation of contrast at the edges between classifed canopy and sky,into the‘edge detection’method.We showed that choosing a suitable neighbor distance for a photo with a specifc gap fraction can signifcantly improve the accuracy of the original‘edge detection’method.We developed an ND-IS(Neighbor Distance-Iteration Selection)method that can automatically determine the threshold values of hemisphere photos with high accuracy and reproductivity.It combines the modifed‘edge detection’method and an iterative selection method,with the aid of an empirical power function for the relationship between neighbor distance and manually verifed gap fraction.This procedure worked well throughout a broad range of gap fractions(0.019-0.945)with different canopy compositions and structures,in fve 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 feld were also discussed.
