Author Contributions
Conceptualization, Methodology and Investigation J.R.-R. and A.M.; Formal analysis, J.R.-R.; Resources, J.S. and C.B.; Supervision, A.M., C.B. and J.S.; Writing—original draft preparation, J.R.-R.; Writing-review and editing, J.R.-R., A.M., C.B. and J.S.; Funding acquisition, C.B. and A.M. All authors have read and agreed to the published version of the manuscript.
Figure 1.
Reference map showing the location and areas covered by the Queen Elizabeth Forest Park.
Figure 1.
Reference map showing the location and areas covered by the Queen Elizabeth Forest Park.
Figure 2.
Forest logging activity identification. (a) Landsat 8 shortwave infrared image used for the visual identification of forest changes. Acquired: 28 June 2019; pink tone areas represent land cover transformation from forest to bare ground. (b) Colored polygons represent Forest and Land Scotland (FLS)-verified forest logging activities carried out between 1 January 2019 and 30 September 2019.
Figure 2.
Forest logging activity identification. (a) Landsat 8 shortwave infrared image used for the visual identification of forest changes. Acquired: 28 June 2019; pink tone areas represent land cover transformation from forest to bare ground. (b) Colored polygons represent Forest and Land Scotland (FLS)-verified forest logging activities carried out between 1 January 2019 and 30 September 2019.
Figure 3.
Proposed methods for the cumulative sum (CUSUM) and cumulative sum-spatial mean corrected (CUSU-SMC) forest change detectors.
Figure 3.
Proposed methods for the cumulative sum (CUSUM) and cumulative sum-spatial mean corrected (CUSU-SMC) forest change detectors.
Figure 4.
Spatial mean normalization. Ratio of backscatter variation of each class (‘forest’, ‘conifer’, ‘broadleaves’) for the entire timeseries, based on the cumulative sum methodologies; (
a) CUSUM based on [
46], (
b) CUSU-SM spatial forest mean variant, (
c) CUSU-SMC proposed method (spatial mean constant bias correction applied).
Figure 4.
Spatial mean normalization. Ratio of backscatter variation of each class (‘forest’, ‘conifer’, ‘broadleaves’) for the entire timeseries, based on the cumulative sum methodologies; (
a) CUSUM based on [
46], (
b) CUSU-SM spatial forest mean variant, (
c) CUSU-SMC proposed method (spatial mean constant bias correction applied).
Figure 5.
Assessing if CUSUM and CUSU-SMC are normally distributed over the reference year. The data used for generating the histograms was obtained by masking out each region (“forest”, “conifer”, “broadleaves”) over a CUSU-SMC ratio image (29 July 2019). Data used for Q–Q plots correspond to temporal values of unique pixels, randomly selected, for each region.
Figure 5.
Assessing if CUSUM and CUSU-SMC are normally distributed over the reference year. The data used for generating the histograms was obtained by masking out each region (“forest”, “conifer”, “broadleaves”) over a CUSU-SMC ratio image (29 July 2019). Data used for Q–Q plots correspond to temporal values of unique pixels, randomly selected, for each region.
Figure 6.
This figure illustrates the sensitivity of different polarization channels to forest structural changes by comparing the annual backscatter timeseries of logging areas with undisturbed forest. Temporal differences extracted from the comparison of logging areas and undisturbed forest (bottom) showed the higher sensitivity of the VH polarization channel.
Figure 6.
This figure illustrates the sensitivity of different polarization channels to forest structural changes by comparing the annual backscatter timeseries of logging areas with undisturbed forest. Temporal differences extracted from the comparison of logging areas and undisturbed forest (bottom) showed the higher sensitivity of the VH polarization channel.
Figure 7.
Synthetic aperture radar (SAR) backscatter signal comparison for mean reference values used for the CUSUM and CUSU-SMC change detection methods. The lack of significant differences in the values obtained for the forest mask for both cases tested could explain the similarity in the final results.
Figure 7.
Synthetic aperture radar (SAR) backscatter signal comparison for mean reference values used for the CUSUM and CUSU-SMC change detection methods. The lack of significant differences in the values obtained for the forest mask for both cases tested could explain the similarity in the final results.
Figure 8.
This figure compares the performance of the pairwise approaches with the CUSUM and CUSU-SMC methods using a ROC curve representation, for both the (A) Ratio (VH/VV) and (B) VH channels. PW = pairwise. ROC curves were generated using the results obtained for the accuracy assessment, performed for the image acquired on 29 September 2019, using significance levels of (α = 0.4, 0.3, 0.2, 0.15, 0.1, 0.05, 0.01, 0.001).
Figure 8.
This figure compares the performance of the pairwise approaches with the CUSUM and CUSU-SMC methods using a ROC curve representation, for both the (A) Ratio (VH/VV) and (B) VH channels. PW = pairwise. ROC curves were generated using the results obtained for the accuracy assessment, performed for the image acquired on 29 September 2019, using significance levels of (α = 0.4, 0.3, 0.2, 0.15, 0.1, 0.05, 0.01, 0.001).
Figure 9.
Logging activities change detection maps obtained for pairwise (static approach) (left) and CUSU-SMC (right) methods. The images show the probability of change (being P = 1 considered as change, and P = 0 as no change), obtained for the 29 September 2019 and ratio data cube, for the areas affected by clear-cuts “Unknown 4” (top), and “33020” (bottom).
Figure 9.
Logging activities change detection maps obtained for pairwise (static approach) (left) and CUSU-SMC (right) methods. The images show the probability of change (being P = 1 considered as change, and P = 0 as no change), obtained for the 29 September 2019 and ratio data cube, for the areas affected by clear-cuts “Unknown 4” (top), and “33020” (bottom).
Figure 10.
Contribution of the tested sieve filter to detection performance of the CUMSUM and CUSU-SMC approaches. Accuracy test carried out using a significance level of (.
Figure 10.
Contribution of the tested sieve filter to detection performance of the CUMSUM and CUSU-SMC approaches. Accuracy test carried out using a significance level of (.
Figure 11.
Forest changes detected in the Queen Elizabeth Forest Park using the conservative strategy (CUSUM VH). (a) Shows the logging activities reference dataset obtained for the period of study. (b) Illustrates the changes detected (red) for the CUMSUM VH (avg 3 × 3) detector. Subsets (c,d) show a closer look of the effect of the sieve filter on the final change detection maps [(c) 10 pixels; (d) 25 pixels].
Figure 11.
Forest changes detected in the Queen Elizabeth Forest Park using the conservative strategy (CUSUM VH). (a) Shows the logging activities reference dataset obtained for the period of study. (b) Illustrates the changes detected (red) for the CUMSUM VH (avg 3 × 3) detector. Subsets (c,d) show a closer look of the effect of the sieve filter on the final change detection maps [(c) 10 pixels; (d) 25 pixels].
Table 1.
Confusion matrix for change detection classification. H0: No change, H1: Change.
Table 1.
Confusion matrix for change detection classification. H0: No change, H1: Change.
Product |
---|
Reference | H0 | H1 |
H0 | True Negative (TN) | False Positive (FP) |
H1 | False Negative (FN) | True Positive (TP) |
Table 2.
CUSUM and CUSU-SMC accuracy assessment. Accuracy rates were calculated using 2019 logging areas as reference, the same acquisition date (29 September 2019) and a significance level of (α = 0.05). TP = true positives, TN = true negatives, FP = false positives, FN = false negatives, OA = overall accuracy, PA = producer accuracy, UA = user accuracy. (*C = conservative approach, *T = tolerant approach, and *NC = results obtained without the application of the fitted ramp correction).
Table 2.
CUSUM and CUSU-SMC accuracy assessment. Accuracy rates were calculated using 2019 logging areas as reference, the same acquisition date (29 September 2019) and a significance level of (α = 0.05). TP = true positives, TN = true negatives, FP = false positives, FN = false negatives, OA = overall accuracy, PA = producer accuracy, UA = user accuracy. (*C = conservative approach, *T = tolerant approach, and *NC = results obtained without the application of the fitted ramp correction).
| | CUSUM | | CUSU-SMC |
---|
| VV | VH *C | Ratio (VH/VV) | VH *T | Ratio (VH/VV) | Ratio (VH/VV) *NC |
---|
TP | 0.324 | 0.602 | 0.731 | 0.724 | 0.685 | 0.654 |
TN | 0.917 | 0.870 | 0.602 | 0.594 | 0.650 | 0.583 |
FP | 0.083 | 0.130 | 0.398 | 0.406 | 0.350 | 0.417 |
FN | 0.676 | 0.398 | 0.269 | 0.276 | 0.315 | 0.346 |
OA | 0.621 | 0.736 | 0.667 | 0.659 | 0.668 | 0.618 |
PA | 0.324 | 0.602 | 0.731 | 0.724 | 0.685 | 0.654 |
UA | 0.797 | 0.822 | 0.648 | 0.641 | 0.662 | 0.610 |
F-score | 0.460 | 0.695 | 0.687 | 0.680 | 0.674 | 0.631 |
Table 3.
Pairwise vs CUSUMs detection performance comparison. The change detection accuracy assessment was performed using a significance level of (α = 0.05) and the same image (29 September 2019) for both the CUSUMs and pairwise approaches. The CUSUMs approaches were the ones for conservative (*C) and tolerant (*T) strategies. TP = true positives, TN = true negatives, FP = false positives, FN = false negatives, OA = overall accuracy, PA = producer accuracy, UA = user accuracy.
Table 3.
Pairwise vs CUSUMs detection performance comparison. The change detection accuracy assessment was performed using a significance level of (α = 0.05) and the same image (29 September 2019) for both the CUSUMs and pairwise approaches. The CUSUMs approaches were the ones for conservative (*C) and tolerant (*T) strategies. TP = true positives, TN = true negatives, FP = false positives, FN = false negatives, OA = overall accuracy, PA = producer accuracy, UA = user accuracy.
| CUSUMs | Pairwise |
---|
| Continuous | Static |
---|
| CUSUM *C(VH) | CUSU-SMC *T(Ratio) | Continuous Ratio | Continuous VH | Static Ratio | Static VH |
---|
TP | 0.602 | 0.685 | 0.223 | 0.594 | 0.459 | 0.389 |
TN | 0.870 | 0.650 | 0.882 | 0.718 | 0.697 | 0.792 |
FP | 0.130 | 0.350 | 0.118 | 0.282 | 0.303 | 0.208 |
FN | 0.398 | 0.315 | 0.777 | 0.406 | 0.541 | 0.611 |
OA | 0.736 | 0.668 | 0.553 | 0.656 | 0.578 | 0.590 |
PA | 0.602 | 0.685 | 0.223 | 0.594 | 0.459 | 0.389 |
UA | 0.822 | 0.662 | 0.655 | 0.678 | 0.602 | 0.651 |
F-score | 0.695 | 0.674 | 0.333 | 0.633 | 0.521 | 0.487 |
Table 4.
Results showing the spatial filtering effects on detection performance/accuracy.
Table 4.
Results showing the spatial filtering effects on detection performance/accuracy.
| OA | PA | UA | F-Score | Spatial Loss |
---|
CUSU-SMC (Ratio) | 0.668 | 0.685 | 0.662 | 0.674 | - |
Sieve (10 pixels) | 0.717 | 0.822 | 0.679 | 0.744 | 0.4 ha |
Sieve (15 pixels) | 0.711 | 0.822 | 0.672 | 0.740 | 0.6 ha |
Sieve (25 pixels) | 0.708 | 0.823 | 0.669 | 0.738 | 1 ha |
CUSUM (VH) | 0.736 | 0.602 | 0.822 | 0.695 | - |
Sieve (10 pixels) | 0.772 | 0.650 | 0.859 | 0.740 | 0.4 ha |
Sieve (15 pixels) | 0.778 | 0.649 | 0.875 | 0.745 | 0.6 ha |
Sieve (25 pixels) | 0.786 | 0.646 | 0.898 | 0.751 | 1 ha |