Search

Mapping the spatial and temporal dynamics of tropical herbaceous wetlands is vital for a wide range of applications. Inundated vegetation can account for over three-quarters of the total inundated area, yet widely used EO mapping approaches are limited to the detection of open water bodies. This paper presents a new wetland mapping approach, RadWet, that automatically defines open water and inundated vegetation training data using a novel mixture of radar, terrain, and optical imagery. Training data samples are then used to classify serial Sentinel-1 radar imagery using an ensemble machine learning classification routine, providing information on the spatial and temporal dynamics of inundation every 12 days at a resolution of 30 m. The approach was evaluated over the period 2017–2022, covering a range of conditions (dry season to wet season) for two sites: (1) the Barotseland Floodplain, Zambia (31,172 km2) and (2) the Upper Rupununi Wetlands in Guyana (11,745 km2). Good agreement was found at both sites using random stratified accuracy assessment data (n = 28,223) with a median overall accuracy of 89% in Barotseland and 80% in the Upper Rupununi, outperforming existing approaches. The results revealed fine-scale hydrological processes driving inundation patterns as well as temporal patterns in seasonal flood pulse timing and magnitude. Inundated vegetation dominated wet season wetland extent, accounting for a mean 80% of total inundation. RadWet offers a new way in which tropical wetlands can be routinely monitored and characterised. This can provide significant benefits for a range of application areas, including flood hazard management, wetland inventories, monitoring natural greenhouse gas emissions and disease vector control.

Drones have the potential to revolutionize malaria vector control initiatives through rapid and accurate mapping of potential malarial mosquito larval habitats to help direct field Larval Source Management (LSM) efforts. However, there are no clear recommendations on how these habitats can be extracted from drone imagery in an operational context. This paper compares the results of two mapping approaches: supervised image classification using machine learning and Technology-Assisted Digitising (TAD) mapping that employs a new region growing tool suitable for non-experts. These approaches were applied concurrently to drone imagery acquired at seven sites in Zanzibar, United Republic of Tanzania. Whilst the two approaches were similar in processing time, the TAD approach significantly outperformed the supervised classification approach at all sites (t = 5.1, p < 0.01). Overall accuracy scores (mean overall accuracy 62%) suggest that a supervised classification approach is unsuitable for mapping potential malarial mosquito larval habitats in Zanzibar, whereas the TAD approach offers a simple and accurate (mean overall accuracy 96%) means of mapping these complex features. We recommend that this approach be used alongside targeted ground-based surveying (i.e., in areas inappropriate for drone surveying) for generating precise and accurate spatial intelligence to support operational LSM programmes.

Knowledge of the location and extent of surface water and inundated vegetation is vital for a range of applications including flood risk management, biodiversity monitoring, quantifying greenhouse gas emissions, and mapping water-borne disease risk. Here, we present a new tool, TropWet, which enables users of all abilities to map wetlands in herbaceous dominated regions based on simple unmixing of optical Landsat satellite imagery in the Google Earth Engine. The results demonstrate transferability throughout the African continent with a high degree of accuracy (mean 91% accuracy, st. dev 2.6%, n = 10,800). TropWet demonstrated considerable improvements over existing globally available surface water datasets for mapping the extent of important wetlands like the Okavango, Botswana. TropWet was able to provide frequency inundation maps as an indicator of malarial mosquito aquatic habitat extent and persistence in Barotseland, Zambia. TropWet was able to map flood extent comparable to operational flood risk mapping products in the Zambezi Region, Namibia. Finally, TropWet was able to quantify the effects of the El Niño/Southern Oscillation (ENSO) events on the extent of photosynthetic vegetation and wetland extent across Southern Africa. These examples demonstrate the potential for TropWet to provide policy makers with crucial information to help make national, regional, or continental scale decisions regarding wetland conservation, flood/disease hazard mapping, or mitigation against the impacts of ENSO.