Individual tree identification using combined LIDAR data and optical imagery
2012
Prieditis, 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)
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 was to explore remote sensing methods to determine individual tree position using LiDAR and digital aerial photography in Latvian forest conditions. The study site was a forest in the middle of Latvia – in Jelgava district (56º39’ N, 23º47’ E). Aerial photography camera (ADS 40) and laser scanner (ALS 50 II) were used to capture the data. LiDAR resolution was 9p m2 (500 m altitude). The image data is RGB, NIR and PAN spectrum with 20 cm pixel resolution. Image processing was made using Fourier transform, frequency filtering, and reverse Fourier transform. LiDAR data processing methods was based on canopy height model, Gaussian mask, and local maxima. Field measurements were tree coordinates, species, height, diameter at breast height, crown width and length. Using combined LiDAR and optical imagery data allows detecting at least 63% of all trees and about 85% of the dominant trees.
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