Estimation of the Some Quantitative Characteristics of Individual tree Using Airborne Laser Scanning Data in part of Shast-Kalate forests of Gorgan

In this study we evaluated the potential of ALS data in estimation of height, volume, basal area and DBH and canopy cover area of individualtrees for the part of Shast kalate of Gorgan.

In this study 125 tree that located in dominant story and without overlay with adjacent trees, were selected. Tree species, tree diameter at breast height (DBH) and tree crown diameter were measured. The height of trees was measured using a Vertex VL 402. Center coordinates of sample trees were determined using Digital Golobal Position System. After separating the crown border of a single tree and providing a polygon of the boundaries of them using aerial digital images, all of height and density metrics were created.Then, we explored the possibility of defining relationships between combination of airborne laser scanning data and height, volume, basal area, DBH and canopy cover area of individual trees using machine learning algorithms (random forest (RF), support vector machine (SVM), k-nearest neighbor (k-NN) and artificial neural network (ANN)).

The best RMSE% on an independent validation data for height, volume, canopy basal area, diameter, and basal area were 13.39, 56.88, 33.17, 22.34 and 25.88%. Also, the results demonstrate that the ANN algorithm can be useful for modeling biophysical properties of individual tree in the North of Iran.

Overall, the results showed the ALS data has the ability to estimate of tree height, basal area, diameter at breasts, and canopy cover but this data hasn’t the ability to estimate the volume very accurate. Also, the results showed that between all algorithms, the ANN algorithm have a better performance than other algorithms.