Figure 1.
The regions of interest for Africa are defined by the degree tiles intersecting with the mangrove regions across Africa, as defined by the GMW v3.0 2018 mangrove extent map. The figure also shows the locations of the sites used to optimise the parameters for the baseline refinement (Red) and change detection threshold (Yellow).
Figure 1.
The regions of interest for Africa are defined by the degree tiles intersecting with the mangrove regions across Africa, as defined by the GMW v3.0 2018 mangrove extent map. The figure also shows the locations of the sites used to optimise the parameters for the baseline refinement (Red) and change detection threshold (Yellow).
Figure 2.
Flowchart illustrating the steps involved in the analysis. Acronym definitions: Global Mangrove Watch (GMW), Norway’s International Climate and Forests Initiative Satellite Data Program (NICFI), normalised difference vegetation index (NDVI), normalised difference water index (NDWI), modified normalised difference water index (MNDWI), mangrove vegetation index (MVI).
Figure 2.
Flowchart illustrating the steps involved in the analysis. Acronym definitions: Global Mangrove Watch (GMW), Norway’s International Climate and Forests Initiative Satellite Data Program (NICFI), normalised difference vegetation index (NDVI), normalised difference water index (NDWI), modified normalised difference water index (MNDWI), mangrove vegetation index (MVI).
Figure 3.
Examples of the reference images defined for the sensitivity analysis for identifying the baseline thresholds ((
c);
Table 1) and the change thresholds ((
d);
Table 2). (
a,
b) illustrate the 2018 Planet imagery for the area while (
c) mangroves that should be within the 2018 baseline (orange) and pixels which should be removed from the mangrove mask by the thresholding (blue). (
d) Mangroves that have not been lost (orange) or lost (blue) between 2018 and 2022. (Image courtesy: Planet Labs/NICFI).
Figure 3.
Examples of the reference images defined for the sensitivity analysis for identifying the baseline thresholds ((
c);
Table 1) and the change thresholds ((
d);
Table 2). (
a,
b) illustrate the 2018 Planet imagery for the area while (
c) mangroves that should be within the 2018 baseline (orange) and pixels which should be removed from the mangrove mask by the thresholding (blue). (
d) Mangroves that have not been lost (orange) or lost (blue) between 2018 and 2022. (Image courtesy: Planet Labs/NICFI).
Figure 4.
Importance of the indices used for the baseline refinement, represented as a count of the number of pixels identified as non-mangroves using the 44 reference sites using each index as a base. Using each index as the base (x-axis), the number of additional non-mangrove pixels identified using each of the other indices is shown. This illustrates that the NDVI and NDWI individually identified more non-mangrove pixels than the MNDWI and MVI pixels, but the MNDWI was more complementary to the other indices. Acronym definitions: normalised difference vegetation index (NDVI), normalised difference water index (NDWI), modified normalised difference water index (MNDWI), mangrove vegetation index (MVI).
Figure 4.
Importance of the indices used for the baseline refinement, represented as a count of the number of pixels identified as non-mangroves using the 44 reference sites using each index as a base. Using each index as the base (x-axis), the number of additional non-mangrove pixels identified using each of the other indices is shown. This illustrates that the NDVI and NDWI individually identified more non-mangrove pixels than the MNDWI and MVI pixels, but the MNDWI was more complementary to the other indices. Acronym definitions: normalised difference vegetation index (NDVI), normalised difference water index (NDWI), modified normalised difference water index (MNDWI), mangrove vegetation index (MVI).
Figure 5.
Examples for individual pixels illustrating the scoring system. Examples: (a) a change occurring, (b) false positives (e.g., misclassified clouds) ignored by the scoring system, and (c) a more complex example of a change occurring with a false positive and some observations which have missed identified the change.
Figure 5.
Examples for individual pixels illustrating the scoring system. Examples: (a) a change occurring, (b) false positives (e.g., misclassified clouds) ignored by the scoring system, and (c) a more complex example of a change occurring with a false positive and some observations which have missed identified the change.
Figure 6.
Examples from within the NICFI Planet mosaics of mangrove losses associated with (a) agriculture, (b) infrastructure, (c) clearing, (d) erosion and (e) ‘other’. The green transparent areas within the first row illustrate the filtered GMW 2018 mangrove extent. (Image courtesy: Planet Labs/NICFI).
Figure 6.
Examples from within the NICFI Planet mosaics of mangrove losses associated with (a) agriculture, (b) infrastructure, (c) clearing, (d) erosion and (e) ‘other’. The green transparent areas within the first row illustrate the filtered GMW 2018 mangrove extent. (Image courtesy: Planet Labs/NICFI).
Figure 7.
The mangrove loss alert hot spots throughout Africa. The hexagon sizes indicate the number of alerts, as does the colour.
Figure 7.
The mangrove loss alert hot spots throughout Africa. The hexagon sizes indicate the number of alerts, as does the colour.
Figure 8.
Examples of the mangrove loss alerts (red dots) for (a) agriculture conversion in Guinea-Bissau, (b) coastal erosion in Nigeria, (c) infrastructure development in Kenya, and (d) storm damage in Mozambique. Note, the occurrence of geolocation errors between the Planet imagery and the resulting mangrove loss alerts from the Sentinel-2 data, which are most evident in (d)).
Figure 8.
Examples of the mangrove loss alerts (red dots) for (a) agriculture conversion in Guinea-Bissau, (b) coastal erosion in Nigeria, (c) infrastructure development in Kenya, and (d) storm damage in Mozambique. Note, the occurrence of geolocation errors between the Planet imagery and the resulting mangrove loss alerts from the Sentinel-2 data, which are most evident in (d)).
Figure 9.
Analysis testing the timing of the alerts versus a reference dataset. (a) The number of alerts for the dominant change countries in Africa throughout the period of observation, (b) the observed date of change versus the number of days from the reference, and (c) a box plot for the number of days difference between the observed and reference date of the change (median = 92 days).
Figure 9.
Analysis testing the timing of the alerts versus a reference dataset. (a) The number of alerts for the dominant change countries in Africa throughout the period of observation, (b) the observed date of change versus the number of days from the reference, and (c) a box plot for the number of days difference between the observed and reference date of the change (median = 92 days).
Figure 10.
Cyclone activity in the Mozambique Channel: (a) the number of storms grouped by 4-year periods, (b) wind speed statistics for the 4-year clustered storms, (c) storm tracks for the storms that occurred during the 2019–2022 study period, and (d) storm track and wind speed of the Idai cyclone (March 2019), the most significant storm within the study period.
Figure 10.
Cyclone activity in the Mozambique Channel: (a) the number of storms grouped by 4-year periods, (b) wind speed statistics for the 4-year clustered storms, (c) storm tracks for the storms that occurred during the 2019–2022 study period, and (d) storm track and wind speed of the Idai cyclone (March 2019), the most significant storm within the study period.
Figure 11.
Illustrating a common cause of commission errors within the results from the mangrove loss alerts system, where the 2018 refined baseline (green regions) has not captured small river channels and, therefore, false positives (red points) for mangrove loss alerts are identified (Image courtesy: Planet Labs/NICFI).
Figure 11.
Illustrating a common cause of commission errors within the results from the mangrove loss alerts system, where the 2018 refined baseline (green regions) has not captured small river channels and, therefore, false positives (red points) for mangrove loss alerts are identified (Image courtesy: Planet Labs/NICFI).
Figure 12.
To illustrate that a sufficient number of accuracy samples has been used for the accuracy assessment, the reference data were subsampled and cumulatively combined to calculate the (a) the F1-score for the mangrove loss alerts, (b) the F1-score for pixels with no mangrove loss and (c) the overall accuracy. The grey areas illustrate the 95th confidence interval of the accuracy metric, where values closer to 1.0 indicate better accuracy. For all three metrics, including additional reference data does not significantly alter the accuracy estimated, and therefore, it can be concluded that sufficient reference data has been sampled.
Figure 12.
To illustrate that a sufficient number of accuracy samples has been used for the accuracy assessment, the reference data were subsampled and cumulatively combined to calculate the (a) the F1-score for the mangrove loss alerts, (b) the F1-score for pixels with no mangrove loss and (c) the overall accuracy. The grey areas illustrate the 95th confidence interval of the accuracy metric, where values closer to 1.0 indicate better accuracy. For all three metrics, including additional reference data does not significantly alter the accuracy estimated, and therefore, it can be concluded that sufficient reference data has been sampled.
Figure 13.
A case study from Guinea-Bissau where a new dam was built, resulting in the mangroves behind the dam being removed for rice cultivation. (a) illustrates the Planet imagery before the dam was built, (b–e) the dam was built and mangroves lost over the period and (f) the GMW Mangrove Loss Alerts identified over the period. (Image courtesy: Planet Labs/NICFI, 2018).
Figure 13.
A case study from Guinea-Bissau where a new dam was built, resulting in the mangroves behind the dam being removed for rice cultivation. (a) illustrates the Planet imagery before the dam was built, (b–e) the dam was built and mangroves lost over the period and (f) the GMW Mangrove Loss Alerts identified over the period. (Image courtesy: Planet Labs/NICFI, 2018).
Figure 14.
Example field photos taken during the field visits to the area shown in
Figure 13. (
a) The dam was built to hold back the tidal waters allowing for rice cultivation. (
b) An area behind the dam where mangroves have started to be removed to be converted into rice paddies. Credit: Wetlands International; Taken: 7 April 2021.
Figure 14.
Example field photos taken during the field visits to the area shown in
Figure 13. (
a) The dam was built to hold back the tidal waters allowing for rice cultivation. (
b) An area behind the dam where mangroves have started to be removed to be converted into rice paddies. Credit: Wetlands International; Taken: 7 April 2021.
Figure 15.
A case study from Kenya where the alerts identified mangroves being lost due to charcoal production. (a) Illustrates the planet imagery before the mangrove loss, (b–e) mangroves lost over the period and (f) the GMW Mangrove Loss Alerts identified over the period. (Image courtesy: Planet Labs/NICFI).
Figure 15.
A case study from Kenya where the alerts identified mangroves being lost due to charcoal production. (a) Illustrates the planet imagery before the mangrove loss, (b–e) mangroves lost over the period and (f) the GMW Mangrove Loss Alerts identified over the period. (Image courtesy: Planet Labs/NICFI).
Figure 16.
An area of mangroves which has been cut for charcoal production within the region shown in
Figure 15. Rehabilitation activities have already started, with the mangrove seeding seen in this photo having been planted as part of that activity. Credit: Wetlands International; Taken: 1 December 2022.
Figure 16.
An area of mangroves which has been cut for charcoal production within the region shown in
Figure 15. Rehabilitation activities have already started, with the mangrove seeding seen in this photo having been planted as part of that activity. Credit: Wetlands International; Taken: 1 December 2022.
Table 1.
Optimal thresholds identified for refining the mangrove extent for 2018. The index thresholds were applied to each scene to identify non-mangrove pixels. The count threshold was used to threshold the number of times a pixel has crossed the threshold combining the results from the individual scenes to create a single mask for each index. Acronym definitions: normalised difference vegetation index (NDVI), normalised difference water index (NDWI), mangrove vegetation index (MVI), Sentinel-1 vertical–horizontal (VH) polarisation.
Table 1.
Optimal thresholds identified for refining the mangrove extent for 2018. The index thresholds were applied to each scene to identify non-mangrove pixels. The count threshold was used to threshold the number of times a pixel has crossed the threshold combining the results from the individual scenes to create a single mask for each index. Acronym definitions: normalised difference vegetation index (NDVI), normalised difference water index (NDWI), mangrove vegetation index (MVI), Sentinel-1 vertical–horizontal (VH) polarisation.
Index | Index Threshold | Count Threshold |
---|
NDVI | <0.1 | 4 |
MVI | <0.1 | 4 |
NDWI | >−0.1 | 4 |
MNDWI | >0.15 | 4 |
Min. Sentinel-1 VH | <−19 [dB] | - |
Table 2.
The upper part of the table provides the range of values tested during the sensitivity analysis to identify the optimal thresholds for the change analysis. The index threshold is the threshold to find a potential mangrove loss within an individual scene. The absolute difference threshold is the difference in the index between the current scene and the median pixel value for the month from the previous year. The score threshold is related to the number of times a change needs to have occurred for the mangrove loss alert to be confirmed. The bottom part of the table provides the resulting optimal thresholds identified through the sensitivity analysis. Acronym definitions: normalised difference vegetation index (NDVI), normalised difference water index (NDWI).
Table 2.
The upper part of the table provides the range of values tested during the sensitivity analysis to identify the optimal thresholds for the change analysis. The index threshold is the threshold to find a potential mangrove loss within an individual scene. The absolute difference threshold is the difference in the index between the current scene and the median pixel value for the month from the previous year. The score threshold is related to the number of times a change needs to have occurred for the mangrove loss alert to be confirmed. The bottom part of the table provides the resulting optimal thresholds identified through the sensitivity analysis. Acronym definitions: normalised difference vegetation index (NDVI), normalised difference water index (NDWI).
Index | Index Threshold | Abs. Diff Threshold | Score Threshold |
---|
Sensitivity Analysis Test Values |
NDVI | 0.05, 0.1, 0.15, 0.2, 0.25, 0.3 | 0.1, 0.15, 0.2, 0.25, 0.3 | 3, 5, 7, 9 |
NDWI | −0.2, −0.15, −0.1, −0.05, 0.0, 0.05 | 0.1, 0.15, 0.2, 0.25, 0.3 | 3, 5, 7, 9 |
Optimal Thresholds |
NDVI | <0.25 | 0.15 | 5 |
NDWI | >0.05 | 0.25 | 7 |
Table 3.
Summarising the mangrove loss alerts results on a per-country basis. Including the number of mangrove loss alerts identified within each country, the GMW 2018 mangrove extent for the country, the spatial density of mangrove loss alerts for the country, provided as the number of hectares per mangrove loss alert (Num. ha per Alert; mangrove extent/number of alerts) and the number of mangrove loss alerts assigned to each change pressure for each country.
Table 3.
Summarising the mangrove loss alerts results on a per-country basis. Including the number of mangrove loss alerts identified within each country, the GMW 2018 mangrove extent for the country, the spatial density of mangrove loss alerts for the country, provided as the number of hectares per mangrove loss alert (Num. ha per Alert; mangrove extent/number of alerts) and the number of mangrove loss alerts assigned to each change pressure for each country.
Country | Number of Alerts | 2018 Mangrove Extent (Hectares) | Num. ha per Alert | Agriculture | Infrastructure | Clearing | Erosion | Other |
---|
Nigeria | 16,380 | 845,359 | 52 | 0 | 3266 | 3996 | 3611 | 5507 |
Guinea-Bissau | 13,012 | 269,778 | 21 | 12,021 | 18 | 32 | 19 | 922 |
Madagascar | 12,964 | 277,221 | 21 | 0 | 0 | 0 | 533 | 12,431 |
Mozambique | 12,140 | 309,560 | 25 | 0 | 10 | 0 | 770 | 11,360 |
Guinea | 10,722 | 222,774 | 21 | 6917 | 362 | 1759 | 616 | 1068 |
Sierra Leone | 2659 | 157,629 | 59 | 59 | 99 | 2419 | 0 | 82 |
Senegal | 1421 | 127,031 | 89 | 0 | 0 | 0 | 31 | 1390 |
Tanzania | 845 | 111,542 | 132 | 818 | 11 | 0 | 5 | 11 |
Cameroon | 500 | 196,877 | 394 | 0 | 14 | 0 | 433 | 53 |
Ghana | 482 | 17,950 | 37 | 0 | 24 | 0 | 0 | 458 |
Kenya | 327 | 54,328 | 166 | 0 | 76 | 0 | 0 | 251 |
Gambia | 240 | 61,122 | 255 | 0 | 171 | 0 | 0 | 69 |
Angola | 218 | 28,358 | 130 | 0 | 147 | 0 | 0 | 71 |
Benin | 170 | 2941 | 17 | 0 | 0 | 6 | 0 | 164 |
Liberia | 161 | 18,691 | 116 | 0 | 6 | 137 | 0 | 18 |
Côte d’Ivoire | 65 | 5411 | 83 | 0 | 65 | 0 | 0 | 0 |
Seychelles | 42 | 382 | 9 | 0 | 0 | 0 | 0 | 42 |
Democratic Republic of the Congo | 12 | 23,659 | 1972 | 0 | 0 | 0 | 0 | 12 |
South Africa | 11 | 2648 | 241 | 11 | 0 | 0 | 0 | 0 |
Djibouti | 10 | 738 | 74 | 0 | 0 | 0 | 0 | 10 |
Somalia | 8 | 3554 | 444 | 0 | 0 | 0 | 0 | 8 |
Total | 72,389 | 2,737,553 | 38 | 19,826 | 4269 | 8349 | 6018 | 33,927 |
Table 4.
The accuracy assessment metrics using the 1 km hexagons to assess the accuracy of the mangrove loss alerts without reference to a mangrove baseline.
Table 4.
The accuracy assessment metrics using the 1 km hexagons to assess the accuracy of the mangrove loss alerts without reference to a mangrove baseline.
Metric | Median Estimate | 95th Confidence |
---|
Overall Accuracy | 92.1% | 89.0–95.7% |
Cohen Kappa | 0.789 | 0.704–0.865 |
Balanced Overall Accuracy | 88.0% | 83.1–93.3% |
Macro F1-Score | 0.894 | 0.851–0.937 |
Weighted Avg. F1-Score | 0.920 | 0.887–0.956 |
Matthews Correlation Coefficient | 0.792 | 0.709–0.876 |
Mangrove Loss Alerts |
F1-Score | 0.842 | 0.769–0.907 |
Precision/Users | 0.900 | 0.813–0.967 |
Recall/Producers | 0.794 | 0.696–0.892 |
Omission | 20.6% | 10.8–30.4% |
Commission | 10.4% | 3.3–18.6% |
No Mangrove Loss Class |
F1-Score | 0.948 | 0.926–0.970 |
Precision/Producers | 0.930 | 0.896–0.963 |
Recall/Users | 0.968 | 0.940–0.989 |
Omission | 3.2% | 1.1–6.0% |
Commission | 7.0% | 3.7–10.4% |
Table 5.
The 1 km hexagon intersections between the GMW Mangrove Loss Alerts (this study), the JJ-Fast alerts [
34] and GFW alerts [
32,
33]. This revealed a low correspondence between the three datasets, with only 14% of the GMW Mangrove Loss Alerts intersecting with the GFW and only 5% of the JJ-FAST and GMW alerts intersecting.
Table 5.
The 1 km hexagon intersections between the GMW Mangrove Loss Alerts (this study), the JJ-Fast alerts [
34] and GFW alerts [
32,
33]. This revealed a low correspondence between the three datasets, with only 14% of the GMW Mangrove Loss Alerts intersecting with the GFW and only 5% of the JJ-FAST and GMW alerts intersecting.
| GMW | GFW | JJ-Fast |
---|
GMW | 2678 | 397 | 121 |
GFW | 14% | 1665 | 90 |
JJ-Fast | 5% | 7% | 1047 |
Table 6.
Comparison of the accuracy of mapping mangrove loss between the GMW Mangrove Loss Alerts (this study), the JJ-FAST alerts [
34] and GFW alerts [
32,
33].
Table 6.
Comparison of the accuracy of mapping mangrove loss between the GMW Mangrove Loss Alerts (this study), the JJ-FAST alerts [
34] and GFW alerts [
32,
33].
Metric | Median Estimate | 95th Confidence |
---|
GMW Mangrove Loss Accuracy |
F1-Score | 0.842 | 0.769–0.907 |
Precision/Users | 0.900 | 0.813–0.967 |
Recall/Producers | 0.794 | 0.696–0.892 |
Omission | 20.6% | 10.8–30.4% |
Commission | 10.4% | 3.3–18.6% |
GFW Mangrove Loss Accuracy |
F1-Score | 0.40 | 0.298–0.504 |
Precision/Producers | 0.534 | 0.400–0.673 |
Recall/Users | 0.321 | 0.229–0.425 |
Omission | 67.9% | 57.5–77.1% |
Commission | 46.6% | 32.7–60.0% |
JJ-Fast Mangrove Loss Accuracy |
F1-Score | 0.148 | 0.063–0.244 |
Precision/Producers | 0.269 | 0.117–0.448 |
Recall/Users | 0.200 | 0.042–0.177 |
Omission | 89.7% | 82.3–95.8% |
Commission | 73.1% | 55.2–88.3% |