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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 5, pp. 195-206

Assessment of the consistency of structural and biometric characteristics of Scots pine forests based on field measurements and UAV imagery

A.D. Nikitina 1 , S.V. Knyazeva 1 , E.V. Tikhonova 1 , A.V. Gornov 1 
1 Isaev Centre for Forest Ecology and Productivity RAS, Moscow, Russia
Accepted: 16.08.2025
DOI: 10.21046/2070-7401-2025-22-5-195-206
The paper presents the results of a study evaluating the consistency between structural and biometric characteristics of Scots pine forests derived from ground-based measurements and high-resolution UAV-based RGB imagery. The evaluation included: tree stem diameters obtained from field surveys and corresponding crown areas delineated from UAV imagery; mean tree height measured in the field and height derived from digital surface models; and tree count and canopy cover assessed through ground-based methods versus UAV-based orthomosaics, using both visual interpretation and automatic segmentation approaches. A high Spearman’s correlation (rs) was found between ground measurements and visual interpretation: rs = 0.71 for tree count and rs = 0.75 for mean height. Moderate agreement in canopy cover (rs = 0.59) may reflect not so much the limitations of RGB imagery as the uncertainty of subjective visual assessments in the field, meanwhile UAV-derived data may provide a more objective interpretation. The relationship between stem diameter and crown area derived from orthomosaics reached rs = 0.96 in old-growth sparse pine forests. Additionally, the agreement between visual interpretation and automatic crown segmentation using the Mask R-CNN neural network was assessed: rs = 0.93…0.97 for tree count, crown area, and perimeter; rs = 0.73 for canopy cover, with lower consistency explained by the contribution of undetected crowns to total canopy closure. The study highlights the potential of UAV-based RGB data and automatic segmentation techniques to complement traditional field-based approaches in forest monitoring.
Keywords: UAV, RGB imagery, Spearman correlation, Scots pine, crown delineation, automatic segmentation, Mask R-CNN, forest monitoring
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