Airborne laser scanning (ALS) technology provides a distinctive, innovative, accurate and cost effective approach to forest inventory. ALS data is used for information extraction with respect to the detection of tree characteristics like height, diameter at breast height (dbh) and crown diameter. Also, timber volume and biomass can be estimated from this information. Forest studies and applications aim to achieve the operational fast methods and reliable results. It is possible if the data contain dense point clouds. The first step to generate high resolution canopy height model (CHM) is to normalize digital surface model (DSM) by subtracting DTM from DSM. Thus, CHM contains the same X, Y coordinates as DTM and tree height as Z value. CHM accuracy is mostly affected by the accuracy of DTM and DSM. The interpolation method in high density point does not cause considerable error while corresponding filtering methods have the most effect. Recent studies show that, moderate to dense forest, estimations tend to underestimate tree height. In this work a technique based on the searching of local maximum canopy height is used to detect individual tree with variable window size and shape. The performance of this technique for the detection of the tree height is more than 90 %.