Mapping shallow lakes in a large South American floodplain: A frequency approach on multitemporal Landsat TM/ETM data

- Paraná River floodplain is a complex mosaic of marshes dotted with shallow lakes.
- We performed a multi-decadal frequency analysis of NDVI derived from Landsat images.
- A selected threshold value of NDVI allowed the separation of standing water pixels.
- We identified and mapped the shallow lakes of the Lower Paraná River floodplain.

SummaryWe propose a methodology to identify and map shallow lakes (SL) in the Paraná River floodplain, the largest freshwater wetland ecosystem in temperate South America. The presence and number of SL offer various ecosystem services and habitats for wildlife biodiversity. Our approach involved a frequency analysis over a 1987-2010 time series of the Normalized Difference Vegetation Index (NDVI), derived from Landsat 5 and 7 TM/ETM data. Through descriptive statistics of samples of pixels and field work in different types of SL, we established an NDVI threshold of 0.34 below which we assumed the presence of water in each pixel. The standard deviation of the estimated SL area decreases with the number of images in the analysis, being less than 10% when at least 30 images are used. The mean SL area for the whole period was 112,691 ha (10.9% of the study area). The influence of the hydrological conditions on the resulting SL map was evaluated by analyzing twelve sets of images, which were selected to span the whole period and different time frames according to multiannual dry and wet periods and to relative water level within each period. The Kappa index was then calculated between pairs of resulting SL maps. We compared our maps with the available national and international cartographic documents and with other published maps that used one or a few Landsat images. Landsat images time series provide an accurate spatial and temporal resolution for SL identification in floodplains, particularly in temperate zones with a good provision of cloud free images. The method evaluated in this paper considers the dynamics of SL and reduces the uncertainties of the fuzzy boundaries. Thus, it provides a robust database of SL and its temporal behavior to establish future monitoring programs based on the recent launch of Landsat 8 satellite.