Automatic mapping of tree crown size (radius, diameter, or width) from remote sensing can provide a major benefit for practical and scientific purposes, but requires the development of accurate methods. This study presents an improved method for average tree crown diameter estimation at a forest plot level from high-resolution airborne data. The improved method consists of the combination of a window binarization procedure and a granulometric algorithm, and avoids the complicated crown delineation procedure that is currently used to estimate crown size. The systematic error in average crown diameter estimates is corrected with the improved method. The improved method is tested with coniferous, beech, and mixed-species forest plots based on airborne images of various spatial resolutions. The absolute (quantitative) accuracy of the improved crown diameter estimates is comparable or higher for both monospecies plots and mixed-species plots than the current methods. The ability of the improved method to produce good estimates for average crown diameters for monoculture and mixed species, to use remote sensing data of various spatial resolution and to operate in automatic mode promisingly suggests its applicability to a wide range of forest systems.
KEYWORDS: Spatial resolution, Sun, Data acquisition, Near infrared, LIDAR, Cadmium, Data modeling, Critical dimension metrology, Remote sensing, Algorithm development
Tree crown size is a key parameter of tree structure that has a variety of uses, including assessment of stand density, tree growth, and amount of timber volume assessment. Remote sensing techniques provide a potentially low-cost alternative to field-based assessments, but require the development of algorithms to easily and accurately extract the required information. This study presents a method for average crown diameter estimation on a plot level based on high-resolution airborne data. The method consists of the combination of a window binarization procedure and a granulometric algorithm. This approach avoids the complicated crown delineation procedure that is currently used to estimate crown size. The method was applied to a spruce mountain forest and was verified on 23 reference plots. The method achieved best results of R2=76% [RMSE=0.37 m (11.2% of the observed mean)] and R2=79% [RMSE=0.49 m (16.7% of the observed mean)]. The study investigates the dependence of the algorithm results on the sun altitude of each image, and determines the optimal combination of spectral bands from hyperspectral airborne images for the application of the method.
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