Diversity of Algorithm and Spectral Band Inputs Improves Landsat Monitoring of Forest Disturbance
Abstract
:1. Introduction
2. Methods
2.1. Sample-Based Disturbance Reference Data
2.2. Landsat Data Preparation and Algorithm Application
2.2.1. LandTrendr
2.2.2. COLD
2.2.3. LandTrendr+COLD
3. Results and Discussion
3.1. Individual Algorithm Detection Errors
3.2. Combined Algorithm Detection Errors
3.3. Band Importance
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Unique Combinations | LandTrendr | COLD | LandTrendr v. COLD | LandTrendr+COLD (best) | Best v. LandTrendr | Best v. COLD | LandTrendr+COLD (worst) | Best v. Worst | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Number of bands | Individual | Combined | Median Error | Top 5 Mean | Median Error | Top 5 Mean | Mean Difference | Median Error | Top 5 Mean | Mean Difference (LandTrendr) | Mean Difference (COLD) | Median Error | Top 5 Mean | Mean Difference |
1 | 13 | 12 | 0.626 | 0.464 | 0.487 | 0.469 | −0.005 | 0.486 | 0.454 | −0.010 | −0.015 | 0.643 | 0.607 | −0.153 |
2 | 78 | 66 | 0.429 | 0.374 | 0.448 | 0.389 | −0.015 | 0.406 | 0.347 | −0.027 | −0.042 | 0.588 | 0.406 | −0.059 |
3 | 286 | 220 | 0.397 | 0.354 | 0.405 | 0.358 | −0.004 | 0.359 | 0.317 | −0.037 | −0.041 | 0.519 | 0.371 | −0.054 |
4 | 715 | 495 | 0.373 | 0.339 | 0.381 | 0.338 | 0.002 | 0.332 | 0.301 | −0.039 | −0.037 | 0.475 | 0.357 | −0.056 |
5 | 1287 | 792 | 0.362 | 0.334 | 0.365 | 0.328 | 0.006 | 0.317 | 0.292 | −0.041 | −0.035 | 0.441 | 0.347 | −0.055 |
6 | 1716 | 924 | 0.353 | 0.330 | 0.352 | 0.320 | 0.010 | 0.305 | 0.286 | −0.044 | −0.034 | 0.401 | 0.339 | −0.053 |
7 | 1716 | 792 | 0.346 | 0.327 | 0.343 | 0.314 | 0.013 | 0.296 | 0.280 | −0.047 | −0.034 | 0.367 | 0.336 | −0.056 |
8 | 1287 | 495 | 0.342 | 0.327 | 0.336 | 0.311 | 0.017 | 0.291 | 0.279 | −0.049 | −0.032 | 0.354 | 0.335 | −0.057 |
9 | 715 | 220 | 0.337 | 0.326 | 0.329 | 0.309 | 0.016 | 0.286 | 0.278 | −0.048 | −0.032 | 0.347 | 0.333 | −0.055 |
10 | 286 | 66 | 0.334 | 0.325 | 0.321 | 0.309 | 0.017 | 0.282 | 0.277 | −0.048 | −0.032 | 0.341 | 0.332 | −0.055 |
11 | 78 | 12 | 0.331 | 0.325 | 0.314 | 0.307 | 0.018 | 0.278 | 0.277 | −0.049 | −0.031 | 0.337 | 0.333 | −0.056 |
12 | 13 | 1 | 0.332 | 0.329 | 0.313 | 0.310 | 0.019 | 0.279 | 0.279 | −0.050 | −0.031 | 0.337 | 0.337 | −0.058 |
13 | 1 | - | 0.329 | 0.329 | 0.310 | 0.310 | 0.019 | - | - | - | - | - | - | - |
LandTrendr | COLD | LandTrendr + COLD | ||||
---|---|---|---|---|---|---|
Rank | Band | Score | Band | Score | Band | Score |
1 | nbr | 35.17 | NBR | 36.29 | NBR | 35.26 |
2 | b5 | 33.23 | B4 | 33.31 | nbr | 33.21 |
3 | tcw | 29.12 | TCB | 28.62 | tcw | 28.30 |
4 | tcg | 26.76 | B7 | 26.36 | B7 | 26.21 |
5 | b7 | 24.04 | NDMI | 25.78 | tcg | 21.52 |
6 | tca | 23.44 | NDVI | 25.39 | b5 | 19.32 |
7 | ndmi | 19.28 | B5 | 23.66 | NDVI | 18.73 |
8 | ndvi | 16.78 | TCA | 17.93 | TCB | 15.79 |
9 | b4 | 14.74 | TCW | 15.52 | B4 | 13.70 |
10 | tcb | 11.43 | B2 | 8.97 | NDMI | 12.26 |
11 | b2 | 10.09 | B3 | 8.96 | tca | 10.82 |
12 | b3 | 8.08 | TCG | 7.59 | b7 | 8.31 |
13 | b1 | 8.07 | B1 | 6.90 | - | - |
LandTrendr | COLD | LandTrendr + COLD | |||||
---|---|---|---|---|---|---|---|
N | Rank | Error | Bands | Error | Bands | Error | Bands |
1 | 1 | 0.429 | tcw | 0.450 | TCW | 0.430 | tcw |
1 | 2 | 0.463 | b7 | 0.454 | B7 | 0.453 | NBR |
1 | 3 | 0.468 | ndmi | 0.458 | NBR | 0.454 | B7 |
1 | 4 | 0.477 | nbr | 0.487 | NDMI | 0.458 | b7 |
1 | 5 | 0.485 | b5 | 0.498 | B5 | 0.474 | nbr |
2 | 1 | 0.366 | nbr.tcw | 0.383 | NBR.TCW | 0.334 | tcw.NBR |
2 | 2 | 0.369 | b5.nbr | 0.384 | B7.NBR | 0.347 | nbr.NBR |
2 | 3 | 0.377 | ndmi.tcw | 0.390 | B5.NBR | 0.347 | b7.NBR |
2 | 4 | 0.380 | tcg.tcw | 0.393 | NBR.TCB | 0.352 | nbr.B7 |
2 | 5 | 0.380 | tca.tcw | 0.396 | B7.NDMI | 0.356 | tcw.B7 |
3 | 1 | 0.352 | b5.nbr.tcw | 0.355 | B4.B7.NBR | 0.314 | nbr.tcw.NBR |
3 | 2 | 0.354 | nbr.tca.tcw | 0.357 | B4.B5.NBR | 0.317 | tcw.tcg.NBR |
3 | 3 | 0.354 | b5.nbr.ndmi | 0.359 | B4.NBR.TCW | 0.318 | tcw.NBR.TCB |
3 | 4 | 0.355 | b4.b5.nbr | 0.360 | B4.B7.NDMI | 0.318 | nbr.b7.NBR |
3 | 5 | 0.355 | b5.nbr.tcg | 0.360 | B7.NBR.TCB | 0.320 | nbr.b5.NBR |
4 | 1 | 0.337 | b5.nbr.tcg.tcw | 0.333 | B4.B7.NBR.TCB | 0.300 | nbr.tcw.NBR.B7 |
4 | 2 | 0.338 | b4.b5.nbr.tcw | 0.338 | B4.B5.NBR.NDVI | 0.300 | nbr.b5.tcw.NBR |
4 | 3 | 0.340 | b5.b7.nbr.tcg | 0.339 | B4.NBR.TCB.TCW | 0.301 | nbr.tcw.NBR.TCB |
4 | 4 | 0.340 | b5.nbr.tca.tcw | 0.339 | B4.B5.NBR.TCB | 0.301 | nbr.b7.NBR.B7 |
4 | 5 | 0.341 | b5.nbr.ndvi.tcw | 0.339 | B4.B5.NBR.NDMI | 0.301 | nbr.b5.NBR.TCB |
5 | 1 | 0.330 | b4.b5.nbr.ndvi.tcw | 0.326 | B4.B5.NBR.NDVI.TCW | 0.290 | nbr.tcw.tcg.NBR.B7 |
5 | 2 | 0.334 | b5.b7.nbr.ndmi.tcg | 0.327 | B4.B5.NBR.NDMI.TCB | 0.292 | nbr.tcw.NBR.TCB.B7 |
5 | 3 | 0.335 | b5.nbr.ndvi.tcg.tcw | 0.328 | B4.B5.NBR.NDVI.TCB | 0.293 | nbr.b5.b7.NBR.B7 |
5 | 4 | 0.335 | b5.b7.nbr.tcg.tcw | 0.328 | B4.B7.NBR.NDMI.TCB | 0.293 | nbr.tcg.b7.NBR.B7 |
5 | 5 | 0.335 | b2.b5.nbr.tcg.tcw | 0.329 | B4.NBR.NDVI.TCB.TCW | 0.294 | nbr.b5.tcw.tcg.NBR |
6 | 1 | 0.329 | b4.b5.b7.nbr.ndvi.tcw | 0.319 | B4.B5.B7.NBR.NDMI.TCB | 0.284 | nbr.tcw.tcg.NBR.B7.NDVI |
6 | 2 | 0.329 | b5.b7.nbr.ndvi.tcg.tcw | 0.319 | B4.B7.NBR.NDMI.NDVI.TCB | 0.286 | nbr.b5.tcw.NBR.B4.B7 |
6 | 3 | 0.330 | b5.nbr.tca.tcb.tcg.tcw | 0.320 | B3.B4.B7.NBR.NDMI.TCB | 0.287 | nbr.b5.tcw.tcg.NBR.NDMI |
6 | 4 | 0.330 | b5.nbr.ndvi.tca.tcg.tcw | 0.320 | B4.B7.NBR.NDVI.TCB.TCW | 0.287 | nbr.b5.tcw.tcg.NBR.NDVI |
6 | 5 | 0.331 | b2.b5.b7.nbr.tcg.tcw | 0.321 | B4.B5.NBR.NDVI.TCB.TCW | 0.287 | nbr.tcw.NBR.B4.B7.NDVI |
7 | 1 | 0.326 | b1.b5.b7.nbr.tca.tcg.tcw | 0.312 | B4.B7.NBR.NDMI.NDVI.TCA.TCB | 0.278 | nbr.b5.tcw.tcg.NBR.B7.NDVI |
7 | 2 | 0.327 | b2.b5.b7.nbr.ndmi.tca.tcg | 0.313 | B4.B5.B7.NBR.NDMI.NDVI.TCB | 0.281 | nbr.tcw.tcg.tca.NBR.B7.NDVI |
7 | 3 | 0.327 | b5.b7.nbr.ndmi.tca.tcg.tcw | 0.315 | B4.B7.NBR.NDMI.TCA.TCB.TCW | 0.281 | nbr.b5.tcw.NBR.B4.B7.NDVI |
7 | 4 | 0.328 | b1.b5.nbr.ndmi.tca.tcg.tcw | 0.315 | B2.B4.B7.NBR.NDMI.NDVI.TCB | 0.281 | nbr.tcw.tcg.NBR.TCB.B7.NDVI |
7 | 5 | 0.328 | b5.b7.nbr.ndmi.tca.tcb.tcg | 0.315 | B4.B5.NBR.NDMI.NDVI.TCA.TCB | 0.281 | nbr.tcw.tcg.NBR.B4.TCB.B7 |
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Cohen, W.B.; Healey, S.P.; Yang, Z.; Zhu, Z.; Gorelick, N. Diversity of Algorithm and Spectral Band Inputs Improves Landsat Monitoring of Forest Disturbance. Remote Sens. 2020, 12, 1673. https://doi.org/10.3390/rs12101673
Cohen WB, Healey SP, Yang Z, Zhu Z, Gorelick N. Diversity of Algorithm and Spectral Band Inputs Improves Landsat Monitoring of Forest Disturbance. Remote Sensing. 2020; 12(10):1673. https://doi.org/10.3390/rs12101673
Chicago/Turabian StyleCohen, Warren B., Sean P. Healey, Zhiqiang Yang, Zhe Zhu, and Noel Gorelick. 2020. "Diversity of Algorithm and Spectral Band Inputs Improves Landsat Monitoring of Forest Disturbance" Remote Sensing 12, no. 10: 1673. https://doi.org/10.3390/rs12101673
APA StyleCohen, W. B., Healey, S. P., Yang, Z., Zhu, Z., & Gorelick, N. (2020). Diversity of Algorithm and Spectral Band Inputs Improves Landsat Monitoring of Forest Disturbance. Remote Sensing, 12(10), 1673. https://doi.org/10.3390/rs12101673