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Individual tree crowns and individual tree locations were extracted to assess deviation caused by terrain of CHM retrieval from LiDAR data before and after correction. Individual tree crowns were also extracted by the canopy [http://www.selleckchem.com/products/azd3759.html AZD3759 in vitro] morphological-controlled watershed method from both CHMs before and after correction [36]. Morphological crown control was introduced to ensure that the watershed results are accurately located in the crown area. Additionally, both CHMs were used to extract individual tree locations through the region growing method [36]. The local maxima algorithm is used to identify potential tree positions in crown area. Regression analysis was conducted using Microsoft Excel to assess the correlation between the slope gradient and the proportions of points with different thresholds of canopy height differences before and after correction. 3.6. Assessing the Consequence of Correcting Topographic Effects We apply a mean stand height weighting scheme, named the Lorey��s height (basal-area-weighted average height) [37], which already is in common use in forestry. Lorey��s height is defined by Equation (2): Lh=��Gi?hi��Gi (2) where Lh is the Lorey��s height (basal-area-weighted average height), Gi is the basal area of stem [http://www.selleckchem.com/products/ozanimod-rpc1063.html Selleckchem Ozanimod] i, and hi is the height of stem i. Stepwise multiple regressions were used to find a relationship between canopy heights variables and field surveyed Lorey��s height. Canopy heights variables from the LiDAR data [http://en.wikipedia.org/wiki/ATP12A ATP12A] before and after terrain correction were used as independent variables in the regression analysis. Two models were built respectively (Equation (3)). We used a K-fold cross-validation procedure (JMP 10, SAS Institute, Cary, NC, USA) to identify the most appropriate dimension for the regression models. This procedure splits the dataset into K groups and fits a regression model to all groups except one. The model giving the best validation statistic is chosen as the final model. This method is best for small data sets, because it makes efficient use of limited amounts of data: Lh=��0+��1h10+��2h20+?+��9h90+��10hmean+�� (3) where h10, h20, ��, h90 and hmean is 10%, 20%, ��, 90% height quantile and average height of airborne LiDAR point cloud respectively; ��0, ��1, ��2, ��, ��9 and ��10 is the coefficient of model respectively; �� is the error of model. Stepwise variable selection and the maximum K-fold R-square improvement variable selection techniques were applied to select the LiDAR-derived variables to be included in the models [24]. The two Lorey��s height estimation models based on 41 plots were named Models I and II, respectively. In assessing deviation caused by terrain of canopy height, we report R2 for statistically significant (at p