Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis
Abstract
:1. Introduction
- (a)
- Evaluation of SOIT in hyperspatial image segmentation;
- (b)
- Development of a hierarchical OBIA classification approach; and,
- (c)
- Assessment of accuracy of the classification.
2. Experimental Section
2.1. Study Area
2.2. Field Data
2.3. Remotely Sensed Data
2.4. Object-Based Image Analysis (OBIA) Approach
2.4.1. Hierarchical Segmentation
Segmentation Level | Scale Parameter | Number of Objects | Average Object Size (m2) | Average Neighborhood (segments) |
---|---|---|---|---|
III | 200 | 84 | 160,000 | 4.4 |
II | 100 | 317 | 4,245 | 4.71 |
I | 9 | 39,665 | 33.92 | 5.36 |
2.4.2. Hierarchical classification
2.5. Classification Assessment
3. Results and Discussion
3.1. Application of Image Texture
Mean Contrast Texture | ||
---|---|---|
Uniform object | Non-uniform object | |
(i.e., image boundary) | (i.e., seedlings) | |
fine | 0.9435 | 0.1090 |
0.9050 | 0.1910 | |
0.8148 | 0.4211 | |
coarse | 0.6739 | 0.6725 |
3.2. Hierarchical Classification
3.3. Class Separability and Classification Stability Assessments
Reference Data | |||||||
Class | Forest | Burned | Clearing | Total | Producer’s Accuracy | ||
Classification Data | Forest | 17 | 3 | 0 | 20 | 85.0% | |
Burned | 3 | 57 | 2 | 62 | 91.9% | ||
Clearing | 0 | 2 | 8 | 10 | 80.0% | ||
Total | 20 | 62 | 10 | 92 | |||
User’s Accuracy | 85.0% | 91.9% | 80.0% | ||||
Overall Accuracy | 89.1% | ||||||
Khat | 0.78 |
Reference Data | |||||||
Class | Low | Medium | High | Total | Producer’s Accuracy | ||
Classification Data | Low | 15 | 2 | 1 | 18 | 83.3% | |
Medium | 3 | 19 | 0 | 22 | 86.4% | ||
High | 2 | 1 | 19 | 22 | 86.4% | ||
Total | 20 | 22 | 20 | 62 | |||
User’s Accuracy | 75.0% | 86.4% | 95.0% | ||||
Overall Accuracy | 85.5% | ||||||
Khat | 0.78 |
Reference Data | |||||||
Class | Tree | Shadow | Understory | Total | Producer’s Accuracy | ||
Classification Data | Tree | 149 | 2 | 13 | 200 | 74.5% | |
Shadow | 39 | 149 | 12 | 200 | 74.5% | ||
Understory | 12 | 13 | 175 | 200 | 87.5% | ||
Total | 200 | 200 | 200 | 600 | |||
User’s Accuracy | 74.5% | 74.5% | 87.5% | ||||
Overall Accuracy | 78.8% | ||||||
Khat | 0.68 |
4. Conclusions
Acknowledgments
Conflicts of Interest
References
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Moskal, L.M.; Jakubauskas, M.E. Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis. Forests 2013, 4, 808-829. https://doi.org/10.3390/f4040808
Moskal LM, Jakubauskas ME. Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis. Forests. 2013; 4(4):808-829. https://doi.org/10.3390/f4040808
Chicago/Turabian StyleMoskal, L. Monika, and Mark E. Jakubauskas. 2013. "Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis" Forests 4, no. 4: 808-829. https://doi.org/10.3390/f4040808