%0 Articles
%T Calibration of the tree size distributions by combining the area-based approach and the individual tree  detection using the airborne laser scanning
%A Xu, Qing
%D 2014
%J Dissertationes Forestales
%V 2014
%N 182
%R doi:10.14214/df.182
%U http://dissertationesforestales.fi/article/1964
%X Individual tree detection (ITD) - based forest inventory using the airborne laser scanning (ALS) data suffers from under-estimation problem, which arises mainly from the suppressed trees that are difficult to be detected from the air. Uncertainty of tree-level estimates, like tree height, diameter at breast height (DBH) and the modeled stem volume also contributes to the inaccuracy of the plot-level estimates. The doctoral work tackled the under-estimation problem from the perspectives of both tree and plot levels. At the plot level, suppressed trees were retrieved from the left tail of the tree size distributions derived from the area-based approach. At the tree level, DBH of single trees were predicted using the quantile-based nearest neighbor imputation.

Area-based approach (ABA) - based forest inventory is able to provide accurate and unbiased plot-level estimates of forest attributes, such as total stem volume. K-MSN method is used in the ABA to simultaneously predict the forest attributes of interest. If tree-level field measurements are available in the sample plots, it’s possible to apply the k-MSN method to predict tree size (DBH or height) distributions for the sample plots. The combination of the ITD-derived tree size distributions with the ABA-derived distributions
makes it possible not only to improve the ABA-derived saw log estimates, but also to retrieve the suppressed trees for the ITD-derived tree size distributions. The replacement and the histogram matching were utilized to calibrate the tree size distributions. The results showed that after the calibration, the RMSE of the predicted total volume decreased by 2 %, and the bias was negligible. The quantile-based nearest neighbor imputation was able to predict the DBH as accurate as the benchmarking method, the k-MSN. It utilized the ALS-measured tree height and crown diameter as the two predictor variables and achieved even better accuracy than the k-MSN method for larger trees with diameters ≥ 16 cm. The improved DBH estimates also benefit stem volume estimation, which led to the improved tree- and plot-level estimations of the stem volume.