To provide reliable results on tree location, height, and number of detected trees the local maximum detection method is introduced by Vazirabad and Karslioglu (2009). This method determines the canopy height by applying a variable window size. The window size selection is related to the height and density of trees. High trees were easier to detect with large windows while short trees were easier to detect with small windows. The derivation of the appropriate window size to search for tree tops relies on the assumption that there is a relation between the height of trees and their crown size. In the 100*100 m test area, the correctness of single tree detection was calculated approximately 91%. The main reason for 9% error is referred to the not detected trees which are located in the corners and edges of the searched patch. To deal with this problem, the standard rectangle windows, variable size and variable shape are recommended (figure 6).

Four window sizes such as standard 3*3 m, standard 5*5 m, rotated 3*3 m (5*5 m), and rotated 5*5 m (9*9 m) are employed (each pixel represents one meter). Tree heights from CHM show that they vary between 2 m to 25 m (figure 6, right). The single tree detection method works in several steps. First generation of a tree height model is required to obtain the tree height. In this model the algorithm looks for all nonzero values and then creates a sorted list depending on the point height above ground (reducing data makes searching procedure faster). In the second step a tree height specific filtering is accomplished, by moving the window pixel by pixel over the tree height model. By changing the window size and shape repeatedly the procedure is continuing up to the end. Six reference patches are provided for counting manually the number of trees by using orthophotos. Density and height of trees are variable inside the patches. The total 7479 trees are detected in whole 1*1 km2. Tree height, dbh, and crown diameters are estimated in the whole area. All this information is adapted to the Log Transformed model for biomass estimation. Hence the total biomass which is given in kilograms for every tree in vegetation cover area is calculated as 1,966,123.3 kg.

Fig. 8. Biomass model and dbh

3. Conclusion

A comprehensive review has been done within this chapter concerning the use of Lidar for biomass estimation. As a consequence it can be said that the reasons for the underestimation of biomass in relation to the tree height need further studies. The development of large footprint Lidar systems on the spaceborne platform GLAS will allow the biomass estimations on a global scale. Spaceborne systems are restricted to record regional and detailed forest data mainly due to the ground track resolution of the system. However, since they receive data continuously, biomass estimation and carbon storage studies are possible every time which can be regarded as a great benefit. Airborne Lidar has the advantages of variable height flying systems and hence collects more precise data with respect to the shape of the terrain. Taking advantages of intensity information from Lidar data provides more information about the interpretation of the ground surface. There are several full waveform airborne Lidar operational systems. But some substantial challenges still exist such as the huge data processing and the interpretation of waveform for complex objects like trees. The fast progresses in computer technologies will help overcome such problems. On the other hand, the high point density in terrestrial systems can help to evaluate the results of other systems. Besides, it allows to model vegetation canopy characteristics particularly concerning tree species estimations in detail. From the data acquisition point of view, it is obvious that models and methods need to exploit the whole potential of the full waveform data for biomass estimation in future. The investigation on the point density in Lidar data represents that having a sufficient number of points has a large impact on the filtering results. The result of the segmentation filtering shows a high capability of adaptation in different landscapes. But it requires choosing correct segmentation parameters by considering the point density. Point spacing plays also an important role for the selection of the interpolation method with respect to the DTM, DSM, and CHM resolution. The methods for individual tree detection which are described and evaluated in the application part are performing well, but they are still under development. Hence more empirical studies are required for improving the quality of the approaches.