Suitable model of detecting the position of individual treetop based on local maximum method

Based on airborne laser scanning data, we employed local maximum method with variable window size to detect the positions of individual trees in high-density forest of Liangshui Nature Reserve in Heilongjiang Province. Two models, canopy height model (CHM) and canopy maximum model (CMM), were applied to detect the treetop; tree height-crown size regression and its 95% lower predicting limit were used to adjust variable window size, and precisions were assessed by the number of detection percentage, 1∶1 hits, producer’s accuracy and user’s accuracy. The results showed that: CMM could restrain commission error caused by branches within canopies; meanwhile, variable window size defined by 95% lower predicting limit of the regression could avoid omission error caused by small trees with local maximum method. Thus, treetop detection using CMM and 95% lower predicting limit of regression as window size in local maximum method could improve accuracy of measuring the position of individual tree and provide theoretical basis for automatically detecting individual trees in high-density forests.