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Abstract
Satellite data offer the opportunity for monitoring the temporal flooding dynamics of seasonal wetlands, a parameter that is essential for the ecosystem services these areas provide. This study introduces an unsupervised approach to estimate the extent of flooded areas in a satellite image relying on the physics of light interaction with water, vegetation and their combination. The approach detects automatically thresholds on the Short-Wave Infrared (SWIR) band and on a Modified-Normalized Difference Vegetation Index (MNDVI), derived from radiometrically-corrected Sentinel-2 data. Then, it combines them in a meaningful way based on a knowledge base coming out of an iterative trial and error process. Classes of interest concern water and non-water areas. The water class is comprised of the open-water and water-vegetation subclasses. In parallel, a supervised approach is implemented mainly for performance comparison reasons. The latter approach performs a random forest classification on a set of bands and indices extracted from Sentinel-2 data. The approaches are able to discriminate the water class in different types of wetlands (marshland, rice-paddies and temporary ponds) existing in the Doñana Biosphere Reserve study area, located in southwest Spain. Both unsupervised and supervised approaches are examined against validation data derived from Landsat satellite inundation time series maps, generated by the local administration and offered as an online service since 1983. Accuracy assessment metrics show that both approaches have similarly high classification performance (e.g., the combined kappa coefficient of the unsupervised and the supervised approach is 0.8827 and 0.9477, and the combined overall accuracy is 97.71% and 98.95, respectively). The unsupervised approach can be used by non-trained personnel with a potential for transferability to sites of, at least, similar characteristics.
Highlights
Wetlands are essential for sustaining life on Earth and are rich in biodiversity
The lower spatial resolution of Landsat images induces an amount of error in the accuracy assessment of the presented approaches, since Landsat inundation maps, which are assumed to be ground truth maps, lack accuracy in the transition zones between water and non-water regions compared to the S2 inundation maps
The accuracy is high for the seasonal marshland and cultivated rice-paddies for the vast majority of the dates, while for the temporary ponds, a significant variation is noticed in the accuracy results, since the limited size of the area covered by the temporary ponds may permit the detection of small water-covered areas in S2 data that cannot be detected in Landsat data
Summary
Wetlands are essential for sustaining life on Earth and are rich in biodiversity They have traditionally provided food and shelter to local human populations, as well as sustaining ecosystem services, like water purification, flood regulation and protection against soil erosion. Nowadays, they provide cultural services to their visitors, such as aesthetic, recreation and a wilderness feeling. The successful management of wetlands requires monitoring of the spatial and temporal variability of the hydrological cycle, especially its expression on the Earth’s surface To this end, hydrological models are a popular and robust solution to generate flood mapping forecasts [3]. The latter have extensive spatial coverage, frequently cover the same area, are of no or low cost and show a high performance for flood mapping through visual analysis methods
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