Mapping African wetlands for 2020 using multiple spectral, geo-ecological features and Google Earth Engine

Abstract

Wetlands provide numerous ecosystem services essential for biodiversity conservation, climate change mitigation, and human well-being. Nevertheless, there are still no complete and uniform wetland maps providing information on the location, distribution, size, and changing status of wetlands in Africa. In this study, we used 130,000 Landsat satellite images and 270,000 sampling points evenly distributed across Africa to classify wetlands with a random forest classifier based on the Google Earth Engine (GEE) cloud platform. This resulted in the first systematic and comprehensive map of existing wetlands in Africa that quantified the area and distribution of these wetlands at a spatial resolution of 30 m, with an overall wetland producer accuracy of 90.22 % and user accuracy of 95.74 %. To address the seasonal variation in African wetlands, we used a dynamic threshold segmentation method based on the Otsu method, which can extract the extent of wetlands under different periods more accurately based on Landsat time series and African phenology data. Our results show that the total African existing wetlands extent from 2019 to 2021 (here in 2020), was approximately 1,341,500 km2, excluding shallow marine waters, comprises 3.98 % of Africa's total land area. Of these wetlands, inland wetlands account for 81 %, and coastal wetlands account for 19 %. The wetlands in Africa were symmetrically distributed around the equator and decreased with increasing latitude, accounting for 97 % of the total wetland area being within the 20°N and 20°S latitudes. African marshes were highly dynamic, expanding by 92.22 % from the dry to wet season. Our study demonstrates the substantial potential of large data samples, multiple wetland classification features (spectral-temporal, topographic, and hydrological features), and random forest classification in GEE for large-scale wetland mapping. The resulting wetland maps provide new baseline data for large-scale monitoring of seasonal dynamics and long-term trends in African wetlands.