Detecting and measuring individual trees with laser scanning in Latvian forest conditions
2010
Priedītis, G., Latvia Univ. of Agriculture, Jelgava (Latvia) | Smits, I., Latvia Univ. of Agriculture, Jelgava (Latvia) | Dagis, S., Latvia Univ. of Agriculture, Jelgava (Latvia) | Dubrovskis, D., Latvia Univ. of Agriculture, Jelgava (Latvia)
Researching new remote sensing and data processing methods is very important subject in forestry. The objectives of research are to explore methods to determine single tree characteristics using LIDAR and adapt them for Latvian forest conditions. Different algorithms and mathematical relations for automatic calculation of tree species, coordinates, height and diameter at breast height are described. Within the project four different clustering methods for tree identification were evaluated. The first method's construction is based on reflection point count in certain height range. The second and third methods are searching for global and local maximums on height axis of LIDAR data collection. In the fourth method segmentation of aerial photography is done by using the user selected sample data. Tree tops were s discovered by searching similarly coloured regions. Field measurements were used for the calibration of LIDAR data and analysis. Sample plots were fitted in the study area with different species composition, age and density. The total number of measured trees in sample plots is 1844. Results show that height can be found mainly for I, II, III craft class trees with average error 2.5%. Stem diameter estimation error of pine is 28%, spruce 17%, birch 4.2% and second storey trees 5.4% using linear equations D = 0.6616*H + 4.6969 (for coniferous trees) and D = 0.7756*H + 3.7132 (for deciduous trees). Dividing trees in classes of coniferous and deciduous can be done by using near infra red photography. The total number of first storey trees identified by LIDAR is 91%, by aerial photographic method 94%.
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