Individual tree detection in digital aerial images by combining locally adaptive binarization and local maxima methods

Locating local maxima of grey levels in aerial images was used for individu al tree detection in boreal, closed forest conditions in southern Finland. Image smoothing and binarization were used as preprocessing steps. Binariza tion was used to restrict the local maxima searching to the bright areas of the images, which were assumed to be tree crowns. Because brightness varia tions are typical of aerial images, both within and among images, locally a daptive methods were suggested for binarization. Aerial digital camera imag es and mapped tree data of eight stands in three field plots were used. Fou r adaptive binarization methods were compared. Differences in tree detectio n accuracy were small even though the appearance of the binarized images we re different. Image smoothing improved the results of tree detection in the three stands that had the largest mean tree size. Tree detection worked fa irly well in all seven stands with a density of less than 1500 trees/ha. In these stands, 70-95% of the trees were detected, whereas only 54% were det ected in the last stand, which had a density of approximately 1900 trees/ha .