**4. Results**

#### *4.1. Land Cover Mapping and Validation*

Figure 4a–c are the outcomes of the classification of the metrics for the three epochs, and are accompanied by pie charts that summarise the proposition covered by each class. For the middle and latest epochs (Figure 4b,c), the combination of the optical with the SAR data produced slightly better results (Table 2) and were, therefore, the ones chosen for the subsequent analyses. The largest land cover class is by far woodland, which covers ~40% of the area (~23,000 km2). Agricultural land is the second largest in all three time points (~12,000 km2), while mangroves (degraded and non-degraded) and grassland occupy significant portions of the delta, too (~8000 km2).

The classification results produced high overall accuracies of 79% (95% CI: ±3%), 83% (95% CI: ±3%), and 82% (95% CI: ±2.6%) for the three epochs, respectively (Table 2). Per-class accuracies (% correct, producer's and user's Accuracies; Table 2) were also high, with the exception of the bareland and grassland classes. The lower accuracies for these two types are attributed to the spectral confusion with the agricultural class: when fields are fallow, it gets confused with bareland, while when they are covered with vegetation, it is mostly confused with grassland (Tables S1–S5). The latter is also confused with woodland, as open woodland pixels contain a significant amount of spectral response from grasses.

Most importantly for the objective of this study, the mangrove class was mapped with high accuracy, with percentage correct and user's and producer's accuracies above 90% in all three time steps and models (Table 2). The degraded mangrove class was also mapped accurately, with producer's accuracies being consistently very high for all epochs and data combinations. However, there was some confusion between this class and the non-degraded mangroves (confusion matrices Tables S2–S6 in the Supplementary Material), resulting in lower user's accuracies, ranging from 77% to 79% for the first two time points (Table 2).

The inclusion of the SAR data in the classification of the more recent epochs generally improved the results but only slightly (Table 2). The most noteworthy improvements were achieved by the inclusion of the PALSAR-2-based metrics in the latest time point, with the user's accuracies of the water and urban classes improving by 4% (Table 2).

**Figure 4.** Land cover over the Niger Delta Region in (**a**) 1988, (**b**) 2000, and (**c**) 2013. Pie charts show the respective estimates of the area covered by each land cover type (%); scale bar corresponds to (**<sup>a</sup>**–**<sup>c</sup>**). Figures (**d**,**<sup>e</sup>**) are the losses and gains of each land cover type between 1988 and 2000; (**f**,**g**) the same for 2000–2013. The white background in (**d**–**g**) signifies persistence.


**Table 2.** Overall and per-class accuracy statistics for the three epochs (Wa: Water; U: Urban; Wo: Woodland; B: Bareland; A: Agricultural; G: Grassland; DM: Degraded Mangrove; M: Mangrove; CI: Confidence Interval; C = Correct; PA: Producer's Accuracy; UA = User's Accuracy).

#### *4.2. Land Cover Change Dynamics*

The three land cover maps were used to calculate the contingency matrix in Table 3. The matrix summarises, for the two periods, the area that has remained unchanged and the area and the type of change observed for each individual class. It also provides a summary of the area covered by each class in the beginning and in the end of each period as well as of the gains and losses they experienced. The spatial distribution of the latter is also illustrated in Figure 4d–g.

**Table 3.** Contingency matrix for the two periods of study representing stable (in bold) and changed areas in km2. (a) 1988–2000; (b) 2000–2013. Wa: Water; U: Urban; Wo: Woodland; B: Bareland; A: Agricultural: G: Grassland; DM: Degraded Mangrove; M: Mangrove.

