Abstract Anthropogenically induced climate change is expected to increase the intensity and frequency of natural disturbances like floods and droughts, with serious potential consequences for wetlands. While there have been some studies that have investigated the impact of hurricanes (tropical cyclones) on coastal wetlands, there are gaps in spatially quantifying the impacts of other extreme meteorological events on inland wetlands. We addressed this gap by studying the impacts of severe flooding, caused by a deep cut-off low system in September 2023, on wetlands in southern Africa. This study aimed to spatially quantify biogeomorphic changes in the wetlands following the flood event, using freely available remote sensing data from before and after the event, and to establish the best method for this analysis. Three methods were applied to detect changes, namely digitization, index analysis and change detection. For the index analysis, the effectiveness of the Normalized Difference Vegetation Index (NDVI) and Bare Soil Index (BSI) was explored. From the results of all methods, we found that bare ground increased significantly (t=-6.35, df = 8, p < 0.01) by a mean of 70 000 m 2 (8%) per wetland, while greenness significantly decreased (t = 5.61, df = 8, p < 0.01) with a mean loss of vegetation of 40 000 m 2 following the floods. The most accurate of the automated approaches to mapping post-flood impact on palmiet wetland systems was change detection (89%). Leveraging applications in remote sensing using freely available imagery presents a critical opportunity to study and interrogate tipping points in wetlands in data-constrained regions.
Maketa et al. (Tue,) studied this question.