Automatic Tree Species Identification in a Cold Temperate Natural Broadleaf Mixed Forest Using High-Resolution UAV Imagery and Mask R-CNN

V. D. Bukin; Maximo Larry Lopez Caceres; Yago Diez Donoso; Takashi Kobayashi; Le Tien Nguyen; Friederich Blum; Muhammad Iqbal Faishal; Anna Trigubenko

doi:10.3390/rs18111692

May 23, 2026Remote SensingOpen Access

Automatic Tree Species Identification in a Cold Temperate Natural Broadleaf Mixed Forest Using High-Resolution UAV Imagery and Mask R-CNN

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Key Points

This research aims to enhance tree species identification in complex mixed forests using UAV imagery and Mask R-CNN.
Utilized UAV-QField leaf-canopy validation approach for tree species validation.
Employed multi-temporal UAV imagery for manual annotation of tree canopies.
Implemented multi-class and species-specific Mask R-CNN models for tree detection.
Mask R-CNN models demonstrated varying performance in detecting different tree species.
The UAV-QField approach validated tree species effectively in closed canopy environments.

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Implication

Validates tree species identification in mixed forests, suggesting enhanced accuracy in ecological monitoring.

Abstract

What are the main findings?· A new UAV-QField leaf-canopy validation approach was used to validate tree species in complex natural mixed forests.· Multi-temporal UAV imagery facilitated the manual annotation of closed canopies in mixed forests.· The multi-class and species-specific Mask R-CNN models showed differential performance for tree detection.

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Bukin et al. (2026) studied this question.

synapsesocial.com/papers/6a153b00b5d9c58d83e8d2ef https://doi.org/10.3390/rs18111692

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Automatic Tree Species Identification in a Cold Temperate Natural Broadleaf Mixed Forest Using High-Resolution UAV Imagery and Mask R-CNN

Key Points

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Implication

Abstract

Discussion

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Automatic Tree Species Identification in a Cold Temperate Natural Broadleaf Mixed Forest Using High-Resolution UAV Imagery and Mask R-CNN

Key Points

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Implication

Abstract

Discussion

Cite This Study

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