AI-POWERED ANALYSIS
Single-tree Delineation by Instance Segmentation Using Drone-based Lidar and Multispectral Imagery: a Comparative Study in Various Forest Structures
This study demonstrates the superiority of deep-learning-based tree segmentation (Mask R-CNN) over traditional methods in complex, dense forest structures. Using drone-based lidar and multispectral imagery, the Mask R-CNN model achieved an average F1 score of 70% (range 36–100%) across various forest types. Performance was notably better in coniferous areas (81%) than deciduous areas (63%), influenced by higher stem densities. Leaf-off conditions improved accuracy by up to 20%. The lidar-derived Canopy Height Model (CHM) was the most critical input, with CHM-based variants outperforming RGB and multispectral channel combinations. Mask R-CNN consistently delivered higher segmentation quality (6-15% better mean IoU) and pixel-precise crown masks compared to baseline methods, despite challenges in very high-density forests or with smaller trees.
Executive Impact
Our AI-driven analysis of Single-tree Delineation by Instance Segmentation Using Drone-based Lidar and Multispectral Imagery: a Comparative Study in Various Forest Structures reveals key metrics crucial for strategic decision-making.
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Average F1 Score for Tree Segmentation
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Improvement Over Baseline Methods
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Accuracy in Coniferous Forest Areas
Deep Analysis & Enterprise Applications
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Overall Performance
The Mask R-CNN model consistently outperformed traditional baseline methods (NCut, Li, Silva, WS) across diverse forest structures, achieving an average F1 score of 70%. This highlights the strong capability of deep learning in handling complex environmental data.
Input Data Impact
Lidar-derived Canopy Height Models (CHM) were identified as the most critical input, resulting in up to 10% better F1 scores than RGB-based variants in the Bavarian Forest. Multispectral combinations (CIR, CHMIRIR2) contributed only marginally, indicating CHM's superior value for structural information in tree delineation.
Forest Type & Density
Segmentation accuracy varied significantly with forest type and stem density. Coniferous areas yielded F1 scores of 81%, nearly 20% higher than deciduous areas (63%), partly due to higher stem densities (~1000 stems/ha for both types). The model showed robust performance even in high-density forests but faced challenges with very dense or smaller trees.
Leaf Phenology
Results showed significantly better performance (up to 20% higher F1 scores) under leaf-off conditions compared to leaf-on. This suggests that the absence of foliage occlusion allows for clearer structural detection, improving segmentation accuracy.
Segmentation Quality
Mask R-CNN consistently produced significantly better segmentation quality, with mean IoU values 6-15% higher than baseline methods (NCut). This indicates more precise and accurate crown delineations, crucial for detailed forest inventory and management.
70%
Average F1 Score for Mask R-CNN Across All Study Areas
Enterprise Process Flow
Lidar Data Acquisition
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Multispectral Data Acquisition
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Data Fusion (CHM, RGB, CIR, NIR)
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Mask R-CNN Training & Validation
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Single-Tree Segmentation
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Accuracy Evaluation
| Method | Mask R-CNN (F1 Score) | Baseline Methods (Max F1 Score) | Improvement |
|---|---|---|---|
| Coniferous Forest (N1/N2) | 82% | 50% | +32% |
| Deciduous Forest (L3) | 86% | 26% | +60% |
| Leaf-off Conditions | 85% | 35% | +50% |
| High Stem Density (K6) | 87% | 67% | +20% |
| Mask R-CNN consistently outperforms traditional methods in diverse forest structures. | |||
Case Study: Bavarian Forest National Park
In the Bavarian Forest, Mask R-CNN with CHM-based variant achieved over 10% better F1 scores (average 82%) compared to RGB variants. This demonstrates the critical role of lidar-derived height information for accurate tree delineation in dense, structurally complex old-growth forests, despite challenges with very small trees.
Key Metric: 82% F1 Score (CHM variant)
Context: Dense, old-growth forest, high structural diversity
+20%
F1 Score Improvement in Leaf-off Conditions
Case Study: Landshut (Leaf-off vs. Leaf-on)
In Landshut, leaf-off conditions yielded F1 scores up to 20% higher than leaf-on, with the CHM variant performing significantly better (73-100% F1). This highlights the benefit of reduced foliage occlusion for improved segmentation accuracy and more precise tree height estimation.
Key Metric: Up to 100% F1 (leaf-off CHM)
Context: Mixed deciduous forest, varying stem densities
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