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In this paper, an assessment of a method using a correlation filter over a lidar-derived digital canopy height model (CHM) is presented. The objective of the procedure is to obtain stem density, position, and height values, on a stand with the following characteristics: ellipsoidal canopy shape (Pinus pinaster), even-aged and single-layer structure. The process consists of three steps: extracting a correlation map from CHM by applying a template whose size and shape resembles the canopy to be detected, applying a threshold mask to the correlation map to keep a subset of candidate-pixels, and then applying a local maximum filter to the remaining pixel groups. The method performs satisfactorily considering the experimental conditions. The mean tree extraction percentage is 65% with a coefficient of agreement of 0.4. The mean absolute error of height is ~0.5 m for all plots except one. It can be considered a valid approach for extracting tree density and height in regularly spaced stands (i.e., poplar plantations) which are fundamental for extracting related forest parameters such as volume and biomass.

Assessing a Template Matching Approach for Tree Height and Position Extraction from Lidar-Derived Canopy Height Models of Pinus Pinaster Stands