**5. Discussion**

Accurate and reliable information of land cover dynamics is essential for the sustainable managemen<sup>t</sup> of tropical deltas and mangrove ecosystems and their capacity for ecosystem service provision. The 'traditional' remote sensing mapping approach involving the use of image mosaics of optical data from two dates, together with likelihood function maximisation image classification algorithms, is not reliable in the humid tropics due to cloud cover [29,31], data availability [27,28], and algorithm performance. This has led to conflicting land cover change estimates for the largest river delta in Africa and the failure to assess the extent of degradation of one of the most endangered ecosystems in the world [60]. Our results show that, by incorporating novel image compositing techniques, spectral-temporal metrics, and machine learning classification algorithms, a reliable assessment of the change dynamics over the Niger Delta Region can be made. Our accurate land cover estimates also allowed for a more comprehensive land change analysis that incorporates an assessment of change intensity and the fragmentation of a key component of the NDR: its mangrove forests.

#### *5.1. Land Cover and Change Dynamics*

There is an inherent di fficulty in mapping land cover in tropical deltas, in general, and mangrove forests, in particular, as they are a ffected by seasonal and intertidal e ffects, with pixels often comprising of a mixture of vegetation, soil, and water due to their location between land and sea and the average tidal range in the Niger Delta being 1.5 m [9]. Nevertheless, we mapped the eight main land cover types for the entire NDR, achieving high overall accuracies in all epochs (~79% for 1988, and 82% for 2000 and 2013; Table 2) and high producer's accuracies for all classes and years. With the exception of the grassland and bareland classes, user's accuracies were also high (from 70% to 91%). Our results compare favourably with other studies in the NDR [19,23,44,45]. Regarding the mapping of degraded mangroves, one of the main objectives of this paper, our study is the first to map this accurately with user's accuracies between 77% and 87% and producer's consistently above 82%. The only other study thatattemptedtomapdegradedmangrovesreportedverylowaccuracies[27].

The results reveal some interesting dynamics:

• There is consistent net loss in mangrove and woodland types and a consistent net gain of the urban class in both periods of study


More specifically, according to our findings, healthy mangroves reported a net loss in both study periods: 292 km<sup>2</sup> in the first and 235 km<sup>2</sup> in the second, while degraded mangroves consistently reported a net gain (21 km<sup>2</sup> in the first and 97 km<sup>2</sup> in the second). Interestingly, our study and the studies by Kuenzer et al. [27] and James et al. [9] found a similar decrease in the overall combined (degraded and non-degraded) mangrove area. According to our results, this area was 270 km2, while, in an almost identical period of study, Kuenzer et al. [27] found that the loss was 239 km2. In the James et al. [9] study between 1987 to 2002, the loss was 213 km2. However, our more accurate

findings identify the total areas covered by the mangrove classes to be very di fferent to the areas in the Kuenzer et al. [27] study: we found that mangroves and degraded mangroves occupied an area between 8697 and 8428 km<sup>2</sup> in the two periods, while Kuenzer et al. [27] claim that these numbers were 10,311 and 10,072 km2, respectively. These figures di ffer by almost a fifth, and can play a significant role in the setting of conservation targets, managemen<sup>t</sup> policies, and sustainability goals. Moreover, our mangrove results compare favourably with three studies that mapped mangroves as one class accurately: the study of Nwobi et al. [19], who found that mangroves occupied an area of 9115 km<sup>2</sup> in 2007 and 8017 km<sup>2</sup> in 2017; the study of Ayanlade and Drake [23] (9965 km<sup>2</sup> in 1987, 9255 km<sup>2</sup> in 2001, and 8430 km<sup>2</sup> in 2011); and the study by James et al. [9] (7037 km<sup>2</sup> in 1987 and 6824 km<sup>2</sup> in 2002).

While it is relatively simple to compare the results on the extent of mangroves between the di fferent studies that mapped land cover change in the NDR, as this class is confined in the coastal belt and is always included within the study area, it is not as straightforward to compare the findings on other land cover types, as the study areas do not match. In the case of woodland, for example, the biggest land cover type in the NDR, our study found that it occupied 23,770 km<sup>2</sup> in 1987 and su ffered net losses in both periods: ~700 km<sup>2</sup> in the first and ~800 km<sup>2</sup> in the second. The study by Ayanlade and Drake [23] also found net losses in both periods for the combined "lowland rainforest" and "freshwater forest" classes but found that these occupied 31,200 km<sup>2</sup> in 1987, 25,400 km<sup>2</sup> in 2001, and 21,470 km<sup>2</sup> in 2011. However, their study area far exceeds the boundaries of our delineation of the NDR. The study by Kuenzer et al. [27] also agrees that "forest" and "swamp forest" experienced net losses in both periods. They report far smaller areas than both our study and the study by Ayanlade and Drake [23]: 18,325 km<sup>2</sup> in 1987 and 15,408 km<sup>2</sup> in 2013. Finally, the Nwobi et al. [19] study also agrees that "tropical forests" were reduced but reported that these occupied 29,000 km<sup>2</sup> in 2007 and 25,500 km<sup>2</sup> in 2017. As all of these studies, including ours, reported high per-class accuracies in the mapping of forests, it is di fficult to ascertain which on is closer to the true figure.

The di fficulty in comparing the findings of di fferent studies remains for the agricultural class, which we found to significantly increase in the first period (from 11,571 to 13,787 km2) and decrease in the second (12,645 km<sup>2</sup> in 2013). An additional issue to the problem of relating to di fferent study areas around the NDR is the choice of land cover nomenclature. Based on our knowledge of the region and on the classification systems of the ESA 20m African land cover data for 2016 and the GlobeLand 30 m data for 2010, we included a grassland class in our mapping e fforts, which were found to decrease in the first period (from 9421 to 7089 km2) and increase in the second (8102 km<sup>2</sup> in 2013). Our figures for the agricultural class are significantly lower to those in Ayanlade and Drake [23], Kuenzer et al. [27], and Nwobi et al. [19]. However, none of these studies included a separate class for grassland but, according to their spatial outputs, appear to have mapped this together with the agricultural class. We recognise that separating these classes poses di fficulties, as the spectral separability between them is low: our user's accuracies for grassland are testament to that (Table 2). However, we strongly believe that it is a shortcoming to map these two classes as one, as this precludes the identification of very important land cover dynamics between either of these classes and, for example, the woodland or urban classes. If summed together, our estimates of agricultural and grassland compare favourably with those of Nwobi et al. [19], who estimated the area covered by "agricultural land" as 21,733 km<sup>2</sup> in 2007 and 24,179 km<sup>2</sup> in 2017.

An important change that occurred in both periods is the expansion of the built-up areas: from 1990 km<sup>2</sup> in 1988, to 2924 km<sup>2</sup> in 2000, to 3728 km<sup>2</sup> in 2013, i.e., an 87% increase. As in the previous land cover types, the di fference in the extent of the study area makes comparison to the other studies di fficult. For example, the Ayanlade and Drake [23] study reports much higher figures, but their study includes the city of Benin, the fourth largest Nigerian city, which lies outside of our delineation of the NDR. Similarities exist between our findings and the Nwobi et al. [19] study: their 'built-up-area' class occupied 3950 km<sup>2</sup> in 2007 and 5938 km<sup>2</sup> in 2017. Their higher estimates can be attributed to the fact that they include the city of Calabar and a number of built-up areas in the northeast of their study area that lie outside our delineation of the NDR.

According to the results of our intensity analysis (Figure 6b), in the first period of study, only mangroves and woodland demonstrated dominant gains, while all the other categories had active gains. Interestingly, only the grassland and bareland types had active annual change intensities, with the former having the largest size of losses in this period (Figure 6a). However, these two are the classes that scored lower user's accuracies and the respective intensity results need to be treated with caution. Notable results from this period are the ~5 times greater annual intensity of mangrove loss than gain and the ~10 times greater annual intensity of urban gain than loss. The intensity of agricultural expansion is also noteworthy, reporting ~2 times greater gain than loss.

In the first period, the land cover class that mangroves 'target' most intensively when they change is degraded mangroves, with a transition intensity of 1.57% of the total area of degraded mangroves in the end of the first period. This is much higher than the estimated uniform change intensity of 0.06%. An area of 535 km<sup>2</sup> of mangroves was degraded by the year 2000. In the second period, this change is even more intense (1.80%, higher than the uniform intensity of 0.08%) and leads to a conversion of a total of 596 km<sup>2</sup> of mangrove to degraded mangrove by 2013. Bareland is also found to be a targeted class for mangroves with an estimated transition intensity of 0.20% (221 km2). Water also targets bareland, as well as mangroves and degraded mangroves, with transition intensities higher than the estimated uniform change intensity. As this is the first paper to undertake an intensity analysis in the NDR, we are unable to compare our findings to existing studies.

#### *5.2. Fragmentation and Degradation of the Niger Delta Mangrove Forest*

The Niger Delta's mangrove forest is a hub for substantial oil and gas deposits. As a consequence, it is highly vulnerable to activities of oil and gas extraction, e.g., land clearing, dredging, construction of flow stations, pipe and seismic lines, well blowouts, leakages or corrosion, equipment failure, error during operation or maintenance, accidents during transportation, sabotage, etc., as well as urbanisation, selective logging, and the proliferation of the invasive Nipa palm species (*Nypa fruticans*) that lead to the forest's destruction, fragmentation, and degradation [9,10,19,27,64].

Our land cover change and intensity analyses showed that degraded mangroves increased in both periods of study and mangroves losses were 5 times more intense than gains. To further assess the condition of the Niger Delta mangrove forest, we carried out the first ever fragmentation analysis of the area. Our fragmentation results show that the 'number of patches' (NP) for the healthy mangroves increased persistently while the 'total percentage of landscape' (PLAND) decreased (Figure 7a,b). The 'largest patch index' (LPI), a measure of dominance (Figure 7c), shows that in the second period, larger patches are on a decrease. The 'area weighted mean shape index' (SHAPE\_AM; Figure 7f) is also decreasing for the healthy mangroves, in both periods: this indicates that changes are happening in the perimeter of patches, uniformly. The 'area weighted mean Euclidean nearest neighbour distance' index (ENN\_AM; Figure 7d) is slightly decreasing, indicating less dispersion of the healthy mangrove patches. The standard deviation of this index (ENN\_SD; Figure 7h) is decreasing but with high values compared to the mean, which indicates a more uneven distribution of patches. The high and steady values of PLADJ (Figure 7g) confirm the ENN results: the healthy mangrove patches remain relatively aggregated throughout the study period. This was expected, as mangroves are very localised within the delta and naturally only occur by the coast.

Figure 7 also shows the change in landscape metrics through time for the degraded mangroves. The size of this class (PLAND; Figure 7a) is constantly increasing but shows some fluctuation in the number of patches (NP; Figure 7b). A divergent pattern is observed in the evolution of the number of patches and the median of patch area metrics (AREA\_MD; Figure 7e): NP increases in the first period and AREA\_MD decreases, while in the second period, this is reversed. The latter means that this class becomes less fragmented, with more patches and lower patch size in the first period. Between 2000 and 2013, there are fewer patches and larger patch sizes, indicating that some of the first period's patches have merged to form larger ones.

A visual examination of the land cover maps and derived change maps from these revealed three areas that demonstrate higher concentrations of degraded mangrove. One such area is in the eastern part of the NDR, around the city of Port-Harcourt and the towns of Bonny, Okrika, and Degema (Figure 8a). Mangrove degradation here can be attributed to the effects of rapid urbanisation and oil extractive activities [14,17], as demonstrated by the overlap with the locations of the oil wells, the pipelines, and the oil spills in Figure 8a. At the central part of the study area, mangrove degradation is mainly due to oil spills resulting from crude oil extractive activities, notably near River Bayelsa and the towns of Nembe, Southern Ijaw, Ekeremor, Brass, and Oloibiri, where oil extraction first began as early as the 1950s (Figure 8b). The highest concentration of degraded mangroves is, however, in the western part of the NDR, in the Delta state (Figure 8c). This area shows widespread degradation, with a notable increase in the second and third date around the towns of Wari South and Wari South West. Several oil spill and gas incidents have been reported in the literature around this area and period [14,15,17,18].

**Figure 8.** Oil wells, pipelines, oil spills, and mangrove degradation hotspots in three parts of the study area: (**a**) the eastern area, around the city of Port-Harcourt; (**b**) the central area, near the river Bayelsa, and (**c**) the western area around the cities of Wari South and Wari South West. U: Urban; M: Mangrove; DM: Degraded Mangrove. (Oil spill data: https://www.nosdra.gov.ng and https://oilspillmonitor.ng. Oil wells and pipeline data: https://www.shell.com.ng).
