Assessment of seasonal variations of teal carbon in palustrine wetlands of the grassland biome : a case study of Chrissiesmeer, Mpumalanga Lakes District, Mpumalanga Province, South Africa

Abstract

Wetlands are recognised as the important natural ecosystems in the world. The above-ground biomass (AGB) of wetland vegetation is essential for providing ecosystem services related to global climate change due to its crucial role in sequestrating anthropogenic carbon emissions. Seasonal AGB estimation could help to understand carbon changes in wetlands and how vegetation in these ecosystems differs across seasons at regional scales. Remote sensing technology offers time-effective and cost-efficient ways to improve the monitoring of wetlands and understanding of the spatial carbon changes in wetland vegetation. This study aimed to use seasonal derived AGB of palustrine herbaceous vegetation to determine the differences in teal carbon, using active and passive remote sensing data across the summer and winter seasons. The study was carried out in the Chrissiesmeer catchment in the temperate Grassland Biome of the Mpumalanga Province of South Africa. The objectives were to (1) derive different season-specific modelling scenarios from Sentinel-1 and Sentinel-2 imagery to assess the optimal model for estimating AGB of palustrine wetland vegetation AGB, (2) assess the performance of Random Forest (RF) and Support Vector Regression (SVR) in predicting seasonal AGB of wetland vegetation, (3) map the seasonal spatial patterns of teal carbon from the estimated AGB of wetland vegetation, and (4) assess the seasonal variation in the predicted teal carbon. RF and SVR algorithms were used as regression-based algorithms with important variable selection to develop an optimal model from the modelling scenarios, which also incorporated field-measured Leaf Area Index (LAI). The results showed that the combination of Sentinel-1 GLCMs and backscatter channels yielded higher accuracy for the estimation of the AGB of palustrine herbaceous vegetation attaining coefficient of determination (R2 ) = 0.735, root mean squared error (RMSE) = 39.848 g·m-2 , and relative RMSE (relRMSE) = 17.286% compared to a combination of reflectance bands, vegetation indices and red-edge bands (R2 = 0.753, RMSE = 49.268 g·m-2 , and relRMSE = 20.009%) in the summer season. For the estimation of AGB in the winter season, Sentinel-1-derived GLCMS textures obtained higher accuracy (R2 = 0.785, RMSE = 67.582 g·m-2 , and relRMSE = 20.885%) compared to the combination of reflectance bands, vegetation indices and red-edge bands of optical data (R2 = 0.749, RMSE= 69.634 g·m-2 and relRMSE = 21.248%). xv These findings suggested that Sentinel-1 sensor-derived models performed better than the optical models in both seasons. Furthermore, the addition of SAR textural measurements improved the accuracy of modelling AGB and RF model performed better than SVR in estimating the AGB of wetland vegetation. The study observed that there was a significant difference between the summer (77.527 g C/m-2 DM) and winter (57.918 g C/m-2 DM) seasonal mean carbon ranges (p < 0.05), and Tevredenpan wetland vegetation communities stored higher levels of carbon in the AGB vegetation in summer than in winter. The study showed that vegetation of palustrine wetlands is significant for carbon storage and fluctuates significantly between summer and winter. Estimating carbon stock in the AGB vegetation can aid in conserving grasslands and wetlands and notably optimise research on biomass estimation with remote sensing and machine learning systems.