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Individual tree identification using different LIDAR and optical imagery data processing methods

The most important part in forest inventory based on remote sensing data is individual tree identification, because only when the tree is identified, we can try to determine its characteristic features. The objective of research is to explore remote sensing methods to determine individual tree position using LIDAR and digital aerial photography in Latvian forest conditions. The study site is a forest in the middle of Latvia at Jelgava district (56o39' N, 23o47' E). Aerial photography camera (ADS 40) and laser scanner (ALS 50 II) were used to capture the data. A LIDAR data is 1.4 to 9 p/m2 depending on the altitude. Image data is RGB (Red, Green, and Blue), NIR (Near Infrared) and PAN (Panchromatic) spectrum with 20 to 50 cm pixel resolution depending on the altitude. Image processing was made using Fourier transform and RGB colour segmentation. LIDAR data are processed with DBSCAN algorithm, global maximum algorithm, and local maximum algorithm. Field measurement's parameters were tree coordinates, species, height, diameter at breast height, crown width, and length. Best results on both ALS and ADS data were achieved using local maximum methods.