The transparency of trees is the most important indicator for a forest health assessment. This paper presents an efficient method for calculating the crown transparency coefficient from tree binary images. This coefficient is based on the automated quantification of the deep indentation, macro-hole and micro-hole densities. Circular structuring elements are introduced, among other things, to automatically find the significant biological size. The symmetric tree convex hull and the tree smoothed contour are defined to delineate the reference areas necessary to evaluate the above-mentioned densities. Statistical thresholds are proposed to eliminate human operator subjectivity, especially in the automated identification of anatomical elements such as soft and deep crown-indentations or macro and micro crown-holes. A point-wise transparency map is produced to better appreciate the origin of the visible skylight areas in the crown. The crown micro-hole density is calculated from the 0.1-to-0.5 transparency points, the crown macro-hole density from the 0.5-to-1 transparency points. We finally opt for weighting of the above three densities with regard to the importance of the symptoms they describe for a more relevant crown transparency coefficient. A comparative study on several trees from full-size and half-size binary images showed that our method is similar overall to the DSO and less sensitive to scale reduction.
['Philippe Borianne', 'Gérard Subsol', 'Yves Caraglio']
Automated efficient computation of crown transparency from tree silhouette images