The potential for remote sensing and hydrologic modelling to assess the spatio-temporal dynamics of ponds in the Ferlo Region (Senegal)

Abstract

Abstract. In the Ferlo Region in Senegal, livestock depend on temporary ponds for water but are exposed to the Rift Valley Fever (RVF), a disease transmitted to herds by mosquitoes which develop in these ponds. Mosquito abundance is related to the emptying and filling phases of the ponds, and in order to study the epidemiology of RVF, pond modelling is required. In the context of a data scarce region, a simple hydrologic model which makes use of remote sensing data was developed to simulate pond water dynamics from daily rainfall. Two sets of ponds were considered: those located in the main stream of the Ferlo Valley whose hydrological dynamics are essentially due to runoff, and the ponds located outside, which are smaller and whose filling mechanisms are mainly due to direct rainfall. Separate calibrations and validations were made for each set of ponds. Calibration was performed from daily field data (rainfall, water level) collected during the 2001 and 2002 rainy seasons and from three different sources of remote sensing data: 1) very high spatial resolution optical satellite images to access pond location and surface area at given dates, 2) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM) data to estimate pond catchment Correspondence to: V. Soti (vs.nev@ntropic.fr) area and 3) Tropical Rainfall Measuring Mission (TRMM) data for rainfall estimates. The model was applied to all ponds of the study area, the results were validated and a sensitivity analysis was performed. Water height simulations using gauge rainfall as input were compared to water level measurements from four ponds and Nash coefficients >0.7 were obtained. Comparison with simulations using TRMM rainfall data gave mixed results, with poor water height simulations for the year 2001 and good estimations for the year 2002. A pond map derived from a Quickbird satellite image was used to assess model accuracy for simulating pond water areas for all the ponds of the study area. The validation showed that modelled water areas were mostly underestimated but significantly correlated, particularly for the larger ponds. The results of the sensitivity analysis showed that parameters relative to pond shape and catchment area estimation have less effects on model simulation than parameters relative to soil properties (rainfall threshold causing runoff in dry soils and the coefficient expressing soil moisture decrease with time) or the water loss coefficient. Overall, our results demonstrate the possibility of using a simple hydrologic model with remote sensing data to track pond water heights and water areas in a homogeneous arid area.

Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study

“…vegetation or water, to enhance classification results. Based on information gathered from other studies on water bodies (Lillesand et al, 2004;Soti et al, 2009Soti et al, , 2010Wolf, 2010), the following indices were created and explored in view of SWB delineation: the normalised difference water index (NDWI) = (coastal blue -NIR2) / (coastal blue + NIR2), the standing water index (SWI) = (blue -NIR1)/(blue + NIR1) and the normalized difference vegetation index (NDVI) = (NIR1 -red) / NIR1 + red). These indices, together with the hue (red, green, blue) transformation, were incorporated in the analysis.…”

“…In previous reports, monitoring SWBs with VHR optical data mostly relied on the contrast between a SWB pixel and its surroundings in terms of spectral properties (Haas et al, 2009;Soti et al, 2010). Spectral analysis of the image allows for the detection of SWBs on a per pixel approach.…”

“…spatial resolution of <5 m, has made it possible to acquire spatial accuracy at sub-meter scales thereby allowing the detection of water bodies that cover only a few m 2 . However, few initiatives have been undertaken to monitor SWBs using VHR satellite imagery (Dambach et al, 2009;Soti et al, 2009;Soti et al, 2010). Many studies use the freely available Landsat imagery (about 30-m resolution) and medium (20 m to 100 m) or coarse (>100 m) resolution imagery (Fuentes et al, 2001;Malone et al, 2001;Guo et al, 2005;Daniel et al, 2006;de Castro et al, 2006;Estallo et al, 2012).…”

A breeding site model for regional, dynamical malaria simulations evaluated using in situ temporary ponds observations

“…This model (hereafter referred to as Hayashi model) is a diagnostic model of water bodies that relates the volume, area and depth. This Hayashi model has been used extensively to study permanent and semi-permanent pond dynamics in Senegal relevant for vectors of rift valley fever that have spatial scales of tens of metres (Soti et al, 2010). In addition, the model has also been evaluated for regional terrain (Brooks and Hayashi, 2002;Minke et al, 2010).…”

Monitoring Seasonal Hydrological Dynamics of Minerotrophic Peatlands Using Multi-Date GeoEye-1 Very High Resolution Imagery and Object-Based Classification

“…These images provide an attractive alternative to aerial photographs due to short satellite return intervals and digital data formats easily integrated into geographic information systems [13]. Recent studies have shown that VHR imagery is suitable for the detailed mapping of temporary water bodies at a local scale [26,27]. Dissanska et al [26] developed a semi-automated approach for assessing the spatiotemporal development of terrestrial and aquatic compartments in patterned peatlands of the La Grande River watershed based on very high resolution panchromatic images.…”