Wetlands are one of the world’s most important and productive ecosystem types, playing a vital role in climate change mitigation (Medugu, 2015), hydrological and biogeochemical cycles (Junk et al., 2013) and maintaining livelihoods (Hu et al., 2017; Wilen & Bates, 2015). The southern part of Nigeria contains many wetlands which are thought to consist mainly of marshes, mangroves and freshwater swamps (Ayanlade & Proske, 2016; Olalekan et al., 2014). However, great environmental pressure has been exerted on these ecosystems as result of land reclamation for agriculture and industrialization (e.g. Niger delta; Chidumeje et al., 2015), urbanization (e.g. Lekki lagoon of Lagos; Obiefuna et al., 2013) and contamination from pollution (e.g. oil spills; Igu & Marchant, 2017; Ohimain, 1996). The regional extent of existing wetlands that need protecting, and the extent of wetland loss and degradation, has thus far only been quantified at coarse resolution. Although there are some global wetland maps, such as Global Land Cover GLC250-2010 (250 m pixels) and the Global Lakes and Wetlands Database (GLWD-3, 1 km pixels), studies by Gumbricht et al., (2017), Hu et al., (2017) and Xu et al., (2018) show inconsistencies between them due to differences in methods, data sources, and validation. Many wetland maps rely on data that can be decades old and, particularly in developing countries, with very limited ground truth data. It is therefore important to improve maps of these ecosystems, using a range of techniques, to get a complete picture of wetland area and to establish the range and extent of different wetland types and their fragmentation. Comprehensive wetland maps and an understanding of the nature of their fragmentation are needed to build economic assessments of wetland ecosystem service provision and to support decision-making by regional and international bodies seeking to protect wetland systems as well as for inclusion in coupled land-surface - climatic models (e.g. JULES / QUEST: Clark et al., 2011; Dadson et al., 2010). The latter is crucial since wetlands are important for land-atmosphere carbon dynamics, greenhouse gas exchange, and the water cycle.
Southern Nigeria is a low-lying region covering ~ 147,000 km² (between 4° 00` and 7° 00`N, and 3° 00` and 9° 00`E, Figure 1) and is thought to have the most extensive wetlands in west Africa (Gumbricht et al., 2017; Uloacha, 2004). The only wetland maps that currently span all of southern Nigeria are from global projects (e.g., GLWD-3) and have relatively low resolution (1 km). However, there are some small-scale studies that have mapped a few local wetlands in the region using satellite imagery (e.g. Ayanlade & Proske, 2016; Obiefuna et al., 2013; Taiwo & Areola, 2009; locations shown in Figure 1). The accuracy of these small-scale studies has yet to be assessed due to absence of suitable ground truthing data. Furthermore, the techniques used in these studies are not suitable for region or country-scale wetland mapping.
1.1 Wetland definitions
Generally, wetlands can be classified on the basis of hydrology, soil type and vegetation. They include marshes (freshwater or saline waterlogged land areas that are periodically flooded, dominated by herbaceous plants), swamps (mineral soil wetlands dominated by trees with seasonal flooding), bogs (rain-fed peatlands, which can be with or without trees) and fens (groundwater-fed peatlands, which can be with or without trees) (Mitsch & Gosselink, 2015). In this study, we consider swamps, marshes, shallow water (including human-made wetlands and lakes) and the swamp subtype of mangroves (coastal, characterised by salt-tolerant trees and shrubs), and attempt to distinguish between these categories in our mapping.
Gradual transitions between wetland types can make clear demarcation challenging and a wetland type can furthermore have different characteristics in different parts of the world. Choice of wetland types in mapping is therefore a pragmatic decision depending both on local wetland characteristics and the separability of different wetland types in observations. The presence of peatlands (fens) across the southern region of Nigeria has been suggested by other mapping studies (e.g. CIFOR, 2016). The Nigerian government, however, suggested that the areas mapped by CIFOR as peatland are more likely to be mangrove/swamps (FREL, 2019). One potential source of confusion is that tropical ‘peat swamps’ are often referred to in the literature as there is a lack of an agreed tropical peatland classification system. Some swamps can have organic peat deposits while others may have a mineral substrate. To avoid confusion, we strictly classify swamps for our control points as tree-dominated mineral soil wetland systems which may have minimal peat cover. Given this definition, peatland and swamp may in some cases still have similar Earth Observation signatures but would not be confused if ground-truthed.
1.2 Identification of wetlands from satellite imagery
Satellite images have been used successfully to identify and map different wetland types around the world (Fei et al., 2011; Guo et al., 2017; Klemas, 2011; Kuenzer et al., 2011; Mahdianpari et al., 2019). Interpretation of multi-temporal imagery in particular can aid classification of dynamic wetlands and their separation from other ecosystems ( Mahdianpari et al., 2018, Ozesmi & Bauer, 2002). Many wetlands have seasonal characteristics based on changes in water level and vegetation that can assist their detection using remote sensing. For example, marshes experience drying of vegetation and a decrease in water level during the dry season or low tide periods (Hudson et al., 2006). This can be observed using optical images from a decrease in the reflectivity in the near infrared and a slight increase in reflectivity to the red band due to suspended particles settling out at low water levels (Hudson et al., 2015). Marsh plants are often annuals and their characteristics change seasonally. Swamps are characterized by both saturated soils during the growing season and seasonal standing water, with a major decrease in swamp water level only in very dry seasons (Schlaffer et al., 2016). Mangroves do not tend to possess obvious seasonal features but can be distinguished based on the presence of mangrove vegetation and permanent standing water.
The availability of vast amounts of open access satellite data, and the growth of advanced machine learning tools integrated with robust cloud computing resources has recently made multi-temporal datasets more accessible (Mahdianpari et al., 2018). Previous studies have used multi-temporal Landsat imagery to classify wetlands both with unsupervised classification algorithms (e.g. K-means and ISODATA; Mwita et al., 2012; Ramsey & Laine, 1997) and with supervised classification schemes (Bwangoy et al., 2010; Wright & Gallant, 2007). The most commonly used approach has involved use of optical indices such as Normalized Differential Vegetation Index (NDVI), Land Surface Water Index (LSWI), Normalized Differential Water Index (NDWI), Tasseled Cap Wetness Index (TCWI), and Modified Soil Adjusted Vegetation Index (MSAVI2) (Chatziantoniou et al., 2017; Kaplan & Avdan, 2017; Mahdianpari et al., 2019; Dong et al., 2014; Xing et al., 2018). However, Synthetic Aperture Radar (SAR) C-band multi polarization radar data has also been used with supervised classification to discriminate between wetland types (Baghdadi et al., 2010), with cross polarization (HV, VH) providing better discrimination between some wetland classes. SAR can penetrate cloud cover, is independent of solar radiation, and is sensitive to structural, textural, and dielectric characteristics of surface features (Bwangoy et al., 2010; Mahdianpari et al., 2018; Mahdianpari et al., 2017; Moser et al., 2016), whereas optical sensors are sensitive to the reflective and spectral characteristics of the surface (Mahdianpari et al., 2018). Combining multiple optical and SAR indices to classify different wetland types therefore has great potential for wetland classification (Kaplan, et al., 2019; Mahdavi et al., 2018; Salehi et al., 2018).
Here, we map the extent of wetlands and categorize the different wetland types for southern Nigeria with a 10 m pixel size, leveraging the open access SAR and optical images acquired from Sentinel-1 and Sentinel-2 and exploiting cloud computing through Google Earth Engine (GEE). We hypothesized that we would find a greater spatial coverage of wetlands compared to earlier estimates due to high resolution mapping of small wetlands that were previously undetected. We also hypothesized that wetland fragmentation would be greatest near urban areas.