This study outlines an algorithm that can be used for individual tree detection and crown delineation; it was applied to coniferous forest using aerial imagery. This article explains the assumptions and processes involved in the algorithm, presents the results of the applications, and discusses possible limitations. The algorithm, which adopts contextual analysis that excludes the need to specify window size, was applied to detect and delineate individual trees based on morphological and reflective characteristics. The preprocessing steps included suppression of the non-coniferous area (i.e. non-forest and leaf-off deciduous forest) and the creation of appropriately smoothed imagery using an optimal smoothing level based on accuracy index (AI); thereafter, unconstrained directional peak- and edge-finding algorithms were processed separately. To assess the tree detection and crown delineation processes, the results of the algorithms were evaluated carefully against visually interpreted crowns in six square plots using several statistical measures based on tree top correspondence, positional difference of tree top, directional crown width, and crown area assessment. The average tree top correspondence had an AI of 88.83%. The positional difference between detected and visually interpreted tree tops was measured and its average was 0.6 m. For our 0.5 m/pixel aerial imagery, the average root mean square error (RMSE) of crown width in six sample plots was found to be 2.8 m, and crown area estimation resulted in RMSE of approximately 9.23 m2 (23.25%). In general, this study highlights the potentiality of the proposed algorithm to efficiently and automatically acquire forest information such as tree numbers, crown width, and crown area. 1. |
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