Detecting and Attributing Drivers of Forest Disturbance in the Colombian Andes Using Landsat Time-Series
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Image Processing
3.2. Disturbance Detection Using BFAST-Monitor
3.3. Disturbance Validation Using TimeSync
3.4. Characterizing Drivers of Change
3.5. Attributing Drivers of Change
4. Results
4.1. Disturbance Detection Agreement
4.2. Characterization of Drivers’
4.3. Driver Attribution Agreement
4.4. Driver Dynamics
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Metrics Employed | Source |
---|---|
Spectral summary metrics | |
Average spectral value pre-change | NIR, SWIR1, SWIR2, NDMI |
Average spectral value post-change | NIR, SWIR1, SWIR2, NDMI |
Standard deviation spectral value pre-change | NIR, SWIR1, SWIR2, NDMI |
Standard deviation spectral value post-change | NIR, SWIR1, SWIR2, NDMI |
Trend time-series metrics | |
Aggregated annual trend (mean, median) | NDVI |
Average and median change magnitude | NDMI |
Standard deviation change magnitude | NDMI |
Kurtosis and skewness change magnitude | NDMI |
Pattern metrics | |
Area | |
Perimeter | |
Shape index | |
Fractal dimension | |
Topographic indicators | |
Elevation | STRM DEM |
Slope | STRM DEM |
Topographic position index | STRM DEM |
TimeSync Validation (Reference) | |||||||
---|---|---|---|---|---|---|---|
Disturbance | Stable Forest | Proportion of Area Mapped (Wi) | User’s Accuracy | Producer’s Accuracy | Total Accuracy | ||
BFAST-Monitor (Map) | Disturbance | 0.134 | 0.004 | 0.14 | 0.96 ± 0.024 | 0.79 ± 0.080 | 0.96 ± 0.017 |
Stable Forest | 0.03 | 0.82 | 0.86 | 0.96 ± 0.020 | 0.99 ± 0.004 | ||
Total | 0.186 | 0.811 | 1 |
Reference | ||||||||
---|---|---|---|---|---|---|---|---|
Conversion to Pasture | Conversion to Agriculture | Non-Stand Replacing | Proportion of Area Mapped (Wi) | User’s Accuracy | Producer’s Accuracy | Total Accuracy | ||
Map | Conversion to pasture | 0.853 | 0.004 | 0.857 | 0.99 ± 0.005 | 0.99 ± 0.005 | 0.98 ± 0.008 | |
Conversion to agriculture | 0.021 | 0.001 | 0.022 | 0.96 ± 0.057 | 0.7 ± 0.121 | |||
Non-stand replacing | 0.006 | 0.009 | 0.107 | 0.121 | 0.88 ± 0.052 | 0.95 ± 0.040 | ||
Total | 0.859 | 0.03 | 0.112 | 1 |
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Murillo-Sandoval, P.J.; Hilker, T.; Krawchuk, M.A.; Van Den Hoek, J. Detecting and Attributing Drivers of Forest Disturbance in the Colombian Andes Using Landsat Time-Series. Forests 2018, 9, 269. https://doi.org/10.3390/f9050269
Murillo-Sandoval PJ, Hilker T, Krawchuk MA, Van Den Hoek J. Detecting and Attributing Drivers of Forest Disturbance in the Colombian Andes Using Landsat Time-Series. Forests. 2018; 9(5):269. https://doi.org/10.3390/f9050269
Chicago/Turabian StyleMurillo-Sandoval, Paulo J., Thomas Hilker, Meg A. Krawchuk, and Jamon Van Den Hoek. 2018. "Detecting and Attributing Drivers of Forest Disturbance in the Colombian Andes Using Landsat Time-Series" Forests 9, no. 5: 269. https://doi.org/10.3390/f9050269
APA StyleMurillo-Sandoval, P. J., Hilker, T., Krawchuk, M. A., & Van Den Hoek, J. (2018). Detecting and Attributing Drivers of Forest Disturbance in the Colombian Andes Using Landsat Time-Series. Forests, 9(5), 269. https://doi.org/10.3390/f9050269