Individual Tree-Crown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Aerial RGB Imagery
2.2.2. Training and Validation Data
2.2.3. Test Data
2.3. CNN-Based Tree Species Mapping
2.4. Model Performance Assessment
3. Results
3.1. CNN Performance for Single-Species Detection Models
3.2. CNs Performance for Multi-Species Detection Models
3.3. Effect of Training Data Augmentation and Forest Stand Conditions on Model Performance
4. Discussion
4.1. Significance of the Study
4.2. Performance of CNNs in Detecting Individual Tree Species with Single-Species Models
4.3. Performance of CNNs in Detecting Multiple Tree Species in Multi-Species Models
4.4. Model Generalization
4.5. Reference Data and Application
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Scientific Name | Common Name | Training | Validation | Test | Total |
---|---|---|---|---|---|
Picea abies | Norway spruce | 3113 | 346 | 497 | 3956 |
Abies alba | Silver fir | 2137 | 238 | 128 | 2503 |
Pinus sylvestris | Scots pine | 539 | 60 | 26 | 625 |
Fagus sylvatica | European beech | 3763 | 418 | 172 | 4353 |
Total | 9552 | 1062 | 823 | 11,437 |
Model | Detected sp. | Precision | Recall | F1 Score | AP | TP | FP | FN |
---|---|---|---|---|---|---|---|---|
Spruce | Spruce | 0.93 | 0.80 | 0.86 | 0.78 | 189 | 15 | 48 |
Fir | Fir | 0.81 | 0.86 | 0.84 | 0.78 | 110 | 25 | 18 |
Pine | Pine | 0.50 | 0.73 | 0.59 | 0.57 | 19 | 19 | 7 |
Beech | Beech | 0.77 | 0.74 | 0.75 | 0.64 | 126 | 38 | 46 |
Model | Detected sp. | Precision | Recall | F1 Score | AP | TP | FP | FN |
---|---|---|---|---|---|---|---|---|
Spruce-fir | Spruce | 0.91 | 0.79 | 0.85 | 0.77 | 188 | 18 | 49 |
Fir | 0.94 | 0.38 | 0.54 | 0.37 | 49 | 3 | 79 | |
Spruce-fir-pine | Spruce | 0.96 | 0.68 | 0.79 | 0.66 | 160 | 6 | 77 |
Fir | 0.83 | 0.63 | 0.72 | 0.58 | 81 | 17 | 47 | |
Pine | 1.00 | 0.85 | 0.92 | 0.85 | 22 | 0 | 4 | |
Spruce-fir-beech | Spruce | 0.94 | 0.80 | 0.86 | 0.78 | 189 | 12 | 48 |
Fir | 0.95 | 0.44 | 0.60 | 0.42 | 56 | 3 | 72 | |
Beech | 0.97 | 0.40 | 0.56 | 0.39 | 66 | 2 | 105 | |
Spruce-fir-pine-beech | Spruce | 0.99 | 0.63 | 0.77 | 0.63 | 150 | 1 | 87 |
Fir | 1.00 | 0.38 | 0.55 | 0.38 | 48 | 0 | 80 | |
Pine | 0.96 | 0.88 | 0.92 | 0.88 | 23 | 1 | 3 | |
Beech | 0.93 | 0.30 | 0.46 | 0.29 | 52 | 4 | 121 |
Model | Spruce-Fir | Spruce-Fir-Pine | Spruce-Fir-Beech | Spruce-Fir-Pine-Beech | ||||
---|---|---|---|---|---|---|---|---|
Non-Aug | Aug | Non-Aug | Aug | Non-Aug | Aug | Non-Aug | Aug | |
All species | 0.70 | 0.81 | 0.81 | 0.69 | 0.68 | 0.71 | 0.67 | 0.73 |
Spruce | 0.85 | 0.87 | 0.79 | 0.67 | 0.86 | 0.86 | 0.77 | 0.85 |
Fir | 0.54 | 0.74 | 0.72 | 0.65 | 0.60 | 0.75 | 0.55 | 0.80 |
Pine | - | - | 0.92 | 0.84 | - | - | 0.92 | 0.39 |
Beech | - | - | - | - | 0.55 | 0.37 | 0.46 | 0.49 |
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Beloiu, M.; Heinzmann, L.; Rehush, N.; Gessler, A.; Griess, V.C. Individual Tree-Crown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning. Remote Sens. 2023, 15, 1463. https://doi.org/10.3390/rs15051463
Beloiu M, Heinzmann L, Rehush N, Gessler A, Griess VC. Individual Tree-Crown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning. Remote Sensing. 2023; 15(5):1463. https://doi.org/10.3390/rs15051463
Chicago/Turabian StyleBeloiu, Mirela, Lucca Heinzmann, Nataliia Rehush, Arthur Gessler, and Verena C. Griess. 2023. "Individual Tree-Crown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning" Remote Sensing 15, no. 5: 1463. https://doi.org/10.3390/rs15051463