Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal

Lacaux, Jean‐Pierre; Tourre, Yves M.; Vignolles, Cécile; Ndione, Jacques André; Lafaye, Murielle

doi:10.1016/j.rse.2006.07.012

Cited by 361 publications

(237 citation statements)

References 15 publications

Supporting

Mentioning

215

Contrasting

Unclassified

Order By: Relevance

“…), and noted unusually high morbidity and mortality in their flocks consistent with RVF. [5][6][7][8][19][20][21] These facts were common knowledge among livestock owners well in advance of the detection of RVF by veterinary service surveillance systems. They also noted human cases consistent with RVF well in advance of detection by the public health surveillance system.…”

Section: Discussionmentioning

confidence: 99%

Epidemiological Assessment of the Rift Valley Fever Outbreak in Kenya and Tanzania in 2006 and 2007

Jost

Nzietchueng²,

Kihu³

et al. 2010

The American Journal of Tropical Medicine and Hygiene

105

125

View full text Add to dashboard Cite

Abstract. To capture lessons from the 2007 Rift Valley fever (RVF) outbreak, epidemiological studies were carried out in Kenya and Tanzania. Somali pastoralists proved to be adept at recognizing symptoms of RVF and risk factors such as heavy rainfall and mosquito swarms. Sandik, which means "bloody nose," was used by Somalis to denote disease consistent with RVF. Somalis reported that sandik was previously seen in 1997/98, the period of the last RVF epidemic. Pastoralists communicated valuable epidemiological information for surveillance and early warning systems that was observed before international warnings. The results indicate that an all or none approach to decision making contributed to the delay in response. In the future, a phased approach balancing actions against increasing risk of an outbreak would be more effective. Given the time required to mobilize large vaccine stocks, emergency vaccination did not contribute to the mitigation of explosive outbreaks of RVF.* Address correspondence to Christine C. Jost, International Livestock Research Institute, P.O. Box 30709, Nairobi, 00100 Kenya. E-mail: c.jost@cgiar.org 66 JOST AND OTHERS approach, participants were asked to focus on the period when the RVF outbreak was observed and to divide the individual piles of counters previously used to rank the livestock species by numbers into two sub-groups: those that developed RVF and those that did not. For those that had RVF, the counters were further subdivided into the proportion that died and the proportion that recovered. This method provided an estimate of the incidence of RVF in each species during the outbreak, as well as the outbreak case fatality rates and the overall mortality rate during the outbreak.Abortions attributable to RVF. Using the results of the proportional piling exercise for the relative numbers of each livestock species as the starting point, participants were asked to allocate the counters into two groups in proportion to those livestock that were pregnant before the RVF outbreak and those that were not. For the pregnant group, participants next divided the counters in proportion to those animals that aborted because of RVF and those that carried their pregnancies to full term. The pregnant pile was then restored, and participants asked to divide the counters to represent the proportions that would have been expected to abort in a normal year (with no RVF outbreak) and those that would have carried to full term. Supplementary questioning probed the causes of abortion other than RVF.Disease impact matrix score. For each livestock species a matrix was constructed on the ground, with benefits derived from that species along the y axis and diseases on the x axis. Participants were given 100 counters and asked to allocate them among the livestock-associated benefits according to the relative importance of each benefit, with the most important benefit receiving the highest number of counters. The counters for each benefit were then sub-allocated to each disease to show the relative negative ...

show abstract

Section: Discussionmentioning

confidence: 99%

Epidemiological Assessment of the Rift Valley Fever Outbreak in Kenya and Tanzania in 2006 and 2007

Jost

Nzietchueng²,

Kihu³

et al. 2010

The American Journal of Tropical Medicine and Hygiene

105

125

View full text Add to dashboard Cite

show abstract

“…Extensive work has also been carried out to evaluate the minimum spatial resolution to detect small features [26,[28][29][30]. For example, SPOT-5 imagery at 10 m was found suitable to map small ponds in Senegal [31]. Similarly, SPOT-5 resolution allowed the monitoring of reed ecosystem in Southern France and in turn provided potential distribution map of species that are relevant to ecosystem functioning.…”

Section: Introductionmentioning

confidence: 99%

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

et al. 2016

View full text Add to dashboard Cite

Land cover and land use maps derived from satellite remote sensing imagery are critical to support biodiversity and conservation, especially over large areas. With its 10 m to 20 m spatial resolution, Sentinel-2 is a promising sensor for the detection of a variety of landscape features of ecological relevance. However, many components of the ecological network are still smaller than the 10 m pixel, i.e., they are sub-pixel targets that stretch the sensor’s resolution to its limit. This paper proposes a framework to empirically estimate the minimum object size for an accurate detection of a set of structuring landscape foreground/background pairs. The developed method combines a spectral separability analysis and an empirical point spread function estimation for Sentinel-2. The same approach was also applied to Landsat-8 and SPOT-5 (Take 5), which can be considered as similar in terms of spectral definition and spatial resolution, respectively. Results show that Sentinel-2 performs consistently on both aspects. A large number of indices have been tested along with the individual spectral bands and target discrimination was possible in all but one case. Overall, results for Sentinel-2 highlight the critical importance of a good compromise between the spatial and spectral resolution. For instance, the Sentinel-2 roads detection limit was of 3 m and small water bodies are separable with a diameter larger than 11 m. In addition, the analysis of spectral mixtures draws attention to the uneven sensitivity of a variety of spectral indices. The proposed framework could be implemented to assess the fitness for purpose of future sensors within a large range of applications.

show abstract

“…Nevertheless, in the Sahel region, the spatial resolution of those sensors is inappropriate (Soti et al, 2009) for identifying water bodies with a surface area of less than 170 000 m 2 (Soti et al, 2009), which is the case for most of the ponds there. Recently, it was shown that the new generation of high and very high spatial resolution remote sensing data (Landsat Enhanced Thematic Mapper, SPOT5 and Quickbird images) is suitable for the detailed mapping of temporary water bodies at a local scale (Liebe et al, 2005;Lacaux et al, 2007;Soti et al, 2009). The potential of radar satellite images (Annor et al, 2009;Di Baldassarre et al, 2009;) for water body inventory have also been improved, with the advantage of being independent of cloud cover (Horritt et al, 2001;Herold et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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

Soti

Puech²,

Seen

et al. 2010

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal

Cited by 361 publications

References 15 publications

Epidemiological Assessment of the Rift Valley Fever Outbreak in Kenya and Tanzania in 2006 and 2007

Epidemiological Assessment of the Rift Valley Fever Outbreak in Kenya and Tanzania in 2006 and 2007

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

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

Contact Info

Product

Resources

About