Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier
Abstract
:1. Introduction
2. Study Area and Datasets
2.1. Study Area
2.2. Datasets
2.2.1. Satellite Imagery
2.2.2. Field Samples
3. Methods
3.1. Image Preprocessing
3.2. Crown Condition Classification Standard
Indicator | Healthy | Medium Dieback | Severe Dieback |
---|---|---|---|
Live crown ratio | >90% | 70%–85% | 50%–65% |
Crown density | >80% | 50%–70% | 20%–40% |
Crown diameter | >55% | 26%–54% | 1%–25% |
Dieback | 0%–5% | 10%–25% | >30% |
Foliar transparency | 0%–20% | 30%–50% | >60% |
3.3. GLCM Textures and Local Spatial Statistics
3.3.1. The Grey-Level Co-Occurrence Matrix (GLCM)
3.3.2. Local Spatial Statistics
Name | Feature | Formula |
---|---|---|
GLCM Texture Measures | ||
MEA (B#) | Mean | |
VAR (B#) | Variance | |
HOM (B#) | Homogeneity | |
CON (B#) | Contrast | |
DIS (B#) | Dissimilarity | |
ENT (B#) | Entropy | |
ASM (B#) | Angular Second Moment | |
COR (B#) | Correlation | |
Gi Statistic | ||
Gi (B#) | Getis statistic |
3.4. Random Forest Classification
3.5. Experimental Procedure
- (1)
- classifying imagery using MS spectral bands only;
- (2)
- classifying imagery using the GLCM textural features only;
- (3)
- classifying imagery using the local spatial Gi features only; and
- (4)
- classifying imagery using combined MS bands, GLCM, and Gi features, and ranking the contribution of the spectral/textural/spatial features to the classification.
4. Results
4.1. Determining the Optimal GLCM Window Size and Direction
4.2. Determining the Optimal Gi Distance Value and Neighborhood Rule
4.3. RF Classification Results
Spectral Features (4) | GLCM Features from Band 4 (8) | GLCM Features from 4 MS Bands (32) | Gi Features from 4 MS Bands (4) | All Combined Features (16) | ||||||
---|---|---|---|---|---|---|---|---|---|---|
OA | 79.5 | 97.1 | 93.3 | 94.0 | 96.9 | |||||
Kappa | 0.7123 | 0.9554 | 0.8963 | 0.9065 | 0.9416 | |||||
PA | UA | PA | UA | PA | UA | PA | UA | PA | UA | |
Healthy | 87.3 | 88.7 | 99.5 | 100.0 | 99.1 | 100.0 | 97.1 | 100.0 | 99.5 | 100.0 |
M dieback | 69.9 | 67.3 | 98.1 | 93.2 | 92.5 | 87.0 | 97.3 | 85.5 | 98.4 | 92.4 |
S dieback | 77.5 | 79.2 | 91.5 | 97.3 | 83.6 | 89.5 | 83.8 | 95.9 | 90.3 | 97.7 |
4.4. Contribution of All Predictive Variables
5. Discussion
5.1. GLCM Feature Analysis
5.2. Gi Feature Analysis
5.3. Stability of the RF Variable Importance
5.4. Implications for Classification of Forest Health Conditions
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Wang, H.; Zhao, Y.; Pu, R.; Zhang, Z. Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier. Remote Sens. 2015, 7, 9020-9044. https://doi.org/10.3390/rs70709020
Wang H, Zhao Y, Pu R, Zhang Z. Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier. Remote Sensing. 2015; 7(7):9020-9044. https://doi.org/10.3390/rs70709020
Chicago/Turabian StyleWang, Hong, Yu Zhao, Ruiliang Pu, and Zhenzhen Zhang. 2015. "Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier" Remote Sensing 7, no. 7: 9020-9044. https://doi.org/10.3390/rs70709020