**1. Introduction**

Deltas are economic and environmental hot spots [1]. They take up less than 1% of the Earth's surface but are home to more than ca. 7% of the global population—a density more than 10 times the average [2]. Deltas are able to support such high human populations thanks to the high productivity, biodiversity, and the ability to use the waterways for transport. They are key contributors to the production of agricultural goods and are, therefore, highly important in the fight against global food

insecurity [3]. However, these important systems are highly delicate and vulnerable. Tropical delta regions, in specific, are under risk of numerous threats, including sea level rise, extreme floods, storm surges, erosion, subsidence, and salinity intrusion, amongs<sup>t</sup> others, which are expected to increase both in frequency and magnitude with the climate crisis [4]. These problems have been proven to increase out-migration rates and human security risks in developing regions, often inhabited by some of the poorest populations in the world [3]. Given the importance and the vulnerability of tropical deltas, monitoring and understanding the land cover dynamics in these regions is vital for achieving efficient policy planning and progress toward achieving the Sustainable Development Goals [5].

The Niger River Delta (NRD) is the largest river delta in Africa [6] and home to a rapidly increasing human population. It features the largest mangrove forest in Africa, estimated to be ~5% of the global mangrove coverage and the fifth largest mangrove forest in the world [7]. It is recognised as a highly important resource for the local communities, as it is utilised for fisheries, fuelwood, construction material, flood protection, medicinal purposes, recreation, and tourism, and holds an important spiritual value [8–13]. Substantial oil and gas deposits are found under the mangrove ecosystem of the NRD. Over the last decades, this highly significant ecosystem is under threat of loss or degradation, mainly due to oil and gas exploration activities, the overexploitation of the mangroves for fuelwood, urbanisation, and the invasion of the Nipa palm species (*Nypa fruticans*) [11,14–19]. Climate change [13,20], sea level rise [21], and coastal erosion [22] are also threats to the mangrove system. Despite the importance of the NDR resources, and the perceived degradation from anthropogenic and environmental pressures, reliable information on land cover dynamics and, particularly, on the extent and condition of the mangrove forest, is still lacking.

Assessing land cover dynamics over large areas is only possible via Earth Observation technologies, which is commonly done with multi-temporal Landsat data. The Landsat archive is truly invaluable as it constitutes the only global medium-scale data available for ~50 years. More 'traditional' approaches have used image mosaics or single images from single-sensor data to map two (before and after) dates and assess change from these [9,23–27]. However, over certain parts of the world, e.g., western and eastern Africa, the data archive has significant gaps [27,28]. Moreover, the use of optical data for accurately mapping and monitoring land cover dynamics over the tropics can be problematic due to the extensive cloud contamination, which renders the creation of image mosaics over large areas an unachievable task [29–31].

Recent advances in data availability, computing power, cloud computing, and algorithm development (e.g., machine and deep learning) have given rise to new approaches to multi-temporal assessments of land cover, e.g., image compositing [32], and spectral-temporal metrics [33,34]. The combination of optical and radar data has also been hailed as an important advancement in regional-scale land cover mapping as certain land cover types, such as mangroves and savannah woody vegetation, are mapped successfully using radar backscatter data, taking advantage of their ability to 'see' through cloud [19,35–41]. Over the last decade, object-based image analysis (OBIA) approaches have also been tested to successfully separate mangrove species from other coastal vegetation [42], to map the Amazonian mangrove belt [38], and to assess long-term variations of forest loss, fragmentation, and degradation using a combination of OBIA and spatial autocorrelation indicators [43].

There has been a limited number of studies that mapped land cover dynamics in the NDR[9,23,27,44] as the area is one of the most a ffected worldwide from the gaps in the Landsat archive and a consistent cloud contamination. With the exception of Nwobi et al. [19], these have employed 'traditional' remote sensing approaches and results have been contradictory. Even fewer studies have attempted to estimate the spatial extent of the degraded mangrove cover. Kuenzer et al. [27] used mosaics of Landsat images to map land cover change in the NDR over three dates but reported low per class classification accuracies for both the "tall mangrove" and the "degraded mangrove" classes, making area calculations unreliable. Salami et al. [45] compared the accuracies achieved by using Landsat ETM+, ASTER and NigeriaSat-1 data to map the six main land cover classes. For the mapping of degraded mangrove,

they reported high accuracies for all three platforms. However, their study covered a small fraction of the NRD.

Based on the initial assessment of land cover transitions and dynamics, land cover change studies often move on to explain the changes in terms of explanatory variables (i.e., land use change drivers) or to forecast spatial patterns of future land cover under di fferent scenarios (i.e., land use change models) [46–51]. The success of these next stages greatly depends on the ability to carry out an accurate initial assessment of the dynamics. Moreover, apart from the need to map land cover accurately, there is also a requirement to understand the dynamics more fully. For example, a simple comparison among the land cover maps does not determine whether the observed changes derive from processes that are systematically more intensive than random processes. Over the last years, new approaches have been suggested for characterising land cover change patterns quantitatively so that any potential subsequent analyses can focus more e fficiently on the important patterns and processes of change, such as the intensity analysis proposed by Aldwaik and Pontius [52]. Other studies, with a specific interest on the fragmentation of habitats for example, have focused on the calculation of landscape metrics from the initial assessment of land cover. These studies have shown that the fragmentation of forests has detrimental e ffects for the health of the ecosystem and the services that it is able to provide [50,53,54]. A number of indices have been created to quantify landscape structure and spatial heterogeneity based on the composition and configuration of the landscape [55–58].

To date, no study related with the assessment of land cover change in the NDR has incorporated recent analytical approaches (e.g., intensity and fragmentation analyses) and the technological and algorithmic achievements (e.g., multi-sensor data, machine learning algorithms) to improve classification accuracies and our understanding of the land cover dynamics. Therefore, there is a need for a comprehensive study of land cover change in the region. In this paper, we aim to accurately assess the land cover dynamics in the NDR over the last decades, and improve our understanding of the extent of the degradation of the delta's mangrove forest. We will do so by:


## **2. Study Area**

The Niger Delta is a flat alluvial plain located in Nigeria on the Gulf of Guinea (Figure 1). It is the largest river delta in Africa formed primarily by sediment deposition. It has a coastline of 470 km and consists of a number of ecological zones, including mangrove swamps, freshwater swamps, forests, and lowland rain forests. The Delta has two distinct seasons (wet and dry) with an average temperature of 27 ◦C throughout the year and annual rainfall of 3000 to 4500 mm [13]. The Niger Delta Region covers an area of 56,000 km<sup>2</sup> that consists of 7 administrative states (Abia, Akwa Ibom, Anambra, Bayelsa, Delta, Imo, and Rivers) and is home to more than 33 million inhabitants (265 people per km2; [59]). More than 70% of these people depend on the natural environment for their livelihoods.

The NDR is considered a hot spot for biodiversity in the world with 3 sites designated as Ramsar Wetlands of International Importance [60]. It is a hub for oil and gas exploration, home to 80% of the refineries in Nigeria and extensive infrastructure (e.g., c. 900 oil wells, c. 100 flow stations and gas plants, c. 1500 km trunk lines, and c. 45,000 km flow lines) [61]. Nigeria's GDP, which rose from ~292 billion USD in 2009 to over 448 billion USD in 2019 [62], is mainly generated by the oil and gas sector. Yet, the NDR remains under-developed and its inhabitants impoverished. The Nigerian Land Use Act excludes the ownership of oil minerals by the state. This is perceived by many as socially inequitable, and has resulted in continuous instability in the region [63]. Additionally, more than 220 oil spills and 17 billion cubic metres of gas flares per year, together with the impacts of the human population explosion, have led to the degradation of the Niger Delta ecosystem [9,10,19,27,64].

**Figure 1.** (**a**) Our delineation of the Niger Delta Region (comprising of the states of Abia, Akwa Ibom, Anambra, Bayelsa, Delta, Imo, and Rivers), and its location within (**b**) West Africa and (**c**) Nigeria.

#### **3. Materials and Methods**

We mapped the main land cover types in three epochs centred around 1988, 2000, and 2013, and assessed land cover change and change intensity in the two respective periods. The chosen classes were: Water, urban (i.e., built-up), woodland (i.e., lowland rainforest and freshwater forest), bareland, agricultural land, grassland, mangroves, and degraded mangroves. The choice of the classes was based on our knowledge of the area, the nomenclature used by ESA's 20 m land cover data for Africa and the 30 m-pixel Landsat-based GlobeCover30, and our desire to separate healthy mangroves from degraded ones. By definition, degraded is the land that has temporarily or permanently undergone a lowering of its capacity to deliver ecosystem services [65]. In the case of mangroves, the degraded forest has less biomass and tree cover, and is unable to provide a number of services at the same level as the healthy system, e.g., support for local livelihoods, carbon sequestration, erosion protection, provision of habitat for numerous fauna species, amongs<sup>t</sup> others [66]. We also assessed the fragmentation of the mangrove forest during these two periods. Additionally, we tested the performance of the classifier when radar data are added to the optical. Figure 2 is a flowchart of our methodological framework.
