Developing objective methods for evaluating the effects of silvicultural treatments on carbon sequestration and stock in plantation forests

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We developed methods to obtain parpameters for estimating stand productivity from 3D data obtained by ground-based LiDAR. To estimate tree dimensions from LiDar data, we applied the neural network algorithm. We were able to estimate DBH with < 2m error and tree height with < 50cm error. The algorithm distinguished 95% of the stems in the stand, on average. This meant that error levels for ground-based LiDAR were similar to that of human workers. Thus, LiDAR could serve as a objective method for forest mensuration without introducing human error.