Lessons Learned While Implementing a Time-Series Approach to Forest Canopy Disturbance Detection in Nepal
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Region
2.2. CODED
2.3. Parameter Testing
2.4. Accuracy Assessments and Unbiased Area
3. Results
3.1. Parameter Testing
3.2. Final Map
4. Discussion
Lessons Learned from Using CODED in an Applied REDD+ Context
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Inputs and Parameters | |||||||
---|---|---|---|---|---|---|---|
Iteration Number | CODED Version | Training Data | Forest Mask | Tree Cover Threshold | Number of Consecutive Observations | Chi-Squared | Change Magnitude Threshold |
1 | Original | Original | Created * | N/A | 3 | N/A | 1 * |
2 | Updated | Original | GFW * | 80 * | 4 * | 0.99 * | 1 * |
3 | Updated | Original | GFW * | 50 | 4 * | 0.99 * | 1 * |
4 | Updated | 2017 only | GFW * | 50 | 4 * | 0.99 * | 1 * |
5 | Updated | 2017 only | GFW * | 20 | 4 * | 0.99 * | 1 * |
6 | Updated | 2017 only | UMD | 10 | 4 * | 0.99 * | 1 * |
7 | Updated | 2017 only | NLCMS | N/A | 4 * | 0.99 * | 1 * |
8 | Original | 2017 only | Created * | N/A | 4 * | N/A | 1 * |
9 | Updated | 2017 only | GFW * | 20 | 6 | 0.99 * | 1 * |
10 | Updated | 2017 only | GFW * | 20 | 10 | 0.99 * | 1 * |
11 | Updated | 2017 only | Created (Map 8) | N/A | 5 | 0.90 | 0.4 |
Stable Forest | Stable Nonforest | Degradation | Deforestation | Total | |
---|---|---|---|---|---|
Stable forest | 208 | 25 | 92 | 26 | 351 |
Stable nonforest | 1 | 249 | 37 | 1 | 288 |
Degradation | 28 | 13 | 116 | 21 | 178 |
Deforestation | 0 | 35 | 88 | 60 | 183 |
Total | 237 | 322 | 333 | 108 |
Model Variable | Variable Name in CODED | Variable Controls | Increase If You Want To | Decrease If You Want To |
---|---|---|---|---|
Chi-squared | chiSquareProbability | Size of the window for detecting statistical change | Strongly decrease the amount of change detected | Strongly increase the amount of change detected |
Consecutive observations of change | Consecutive Obs | Required number of observed NDFI values outside the predicted range for the algorithm to identify a disturbance event | Detect even short-duration forest disturbances, but potentially have overestimation from outlier pixels | Exclude potential errors and short-duration forest disturbances |
Threshold for defining GFW forest mask | Tree cover threshold | The percentage of tree cover required to categorize a pixel as forest when making the forest max within the algorithm | Include sparse forests in the generated forest mask, including more area in the analysis | Include only dense canopy forests in the forest mask, potentially excluding sparse or deciduous forests from the analysis |
Minimum threshold of NDFI change to define disturbance | Minimum Change Magnitude | The cutoff magnitude in post-processing for what NDFI change events will be counted as a disturbance in the final output | Include only severe changes, like deforestation, and ignore less drastic changes to the forest that may be erroneous degradation detections | Observe lower-magnitude changes in NDFI and accept some small erroneous detections |
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Aryal, R.R.; Wespestad, C.; Kennedy, R.; Dilger, J.; Dyson, K.; Bullock, E.; Khanal, N.; Kono, M.; Poortinga, A.; Saah, D.; et al. Lessons Learned While Implementing a Time-Series Approach to Forest Canopy Disturbance Detection in Nepal. Remote Sens. 2021, 13, 2666. https://doi.org/10.3390/rs13142666
Aryal RR, Wespestad C, Kennedy R, Dilger J, Dyson K, Bullock E, Khanal N, Kono M, Poortinga A, Saah D, et al. Lessons Learned While Implementing a Time-Series Approach to Forest Canopy Disturbance Detection in Nepal. Remote Sensing. 2021; 13(14):2666. https://doi.org/10.3390/rs13142666
Chicago/Turabian StyleAryal, Raja Ram, Crystal Wespestad, Robert Kennedy, John Dilger, Karen Dyson, Eric Bullock, Nishanta Khanal, Marija Kono, Ate Poortinga, David Saah, and et al. 2021. "Lessons Learned While Implementing a Time-Series Approach to Forest Canopy Disturbance Detection in Nepal" Remote Sensing 13, no. 14: 2666. https://doi.org/10.3390/rs13142666