Owing to its unique physical and socio-economic characteristics, the Saint Louis region stands out as one of the most susceptible areas in Senegal to the adverse impacts of coastal erosion. The dynamics of erosion in this region are significantly influenced by the Langue de Barbarie (LB), a sand spit formed at the mouth of the Senegal River. Initially, in 2003, a 4 m wide artificial breach was strategically introduced to mitigate flooding; however, sediment dynamics expanded it to 6 km by 2020, thereby affecting the entire region. This study delves into the coastline change of the LB, specifically divided into three zones (LB-1, LB-2, and LB-3), spanning the period from 1994 to 2042. Leveraging Geographic Information System (GIS) and remote sensing techniques, our investigation reveals that, prior to the breach’s creation, the average dynamic coastline rates in zones LB-1, LB-2, and LB-3 were estimated at 4.4, 5.9, and 4.4 m/year, respectively. Subsequent to the breach, these rates shifted to −1.2, 8.4, and −2.7 m/year, with the most significant erosion observed alongshore of LB-3 at −6.6 m/year during the period 2002–2012. Projecting into 2032, LB-1 and LB-3 are anticipated to experience erosion rates of −11.5 and −26.8 m/year, respectively, while the LB-2 records an estimated accretion rate of 8.41 m/year. Eroded areas are expected to total 571,458 m², while accumulated areas are expected to total 67,191 m². By 2042, zones LB-1, LB-2, and LB-3 are expected to experience erosion rates of −23 and −53.6 m/year, resulting in the erosion of 1,021,963 m² and the accumulation of 94,930 m² with a dynamic rate of 168.2 m/year in zone LB-3. These results have significant implications for solving the urgent issue of coastal erosion in LB.

In Senegal, despite its high frequency, there is no real program to fight against anemia among infants. This work was carried out in the Dakar suburb from 1st September, 2009 to 27th January, 2010 among apparently healthy children aged 9-15 months at the time of their immunization against yellow fever and measles. They showed no known chronic condition or acute infection at the time of the survey. The objectives were to study the diet, prevalence, type and risk factors of anemia. The questionnaire was about whether the father and the mother were working and about the children’s diet during the first six months of their life. All the children underwent anthropometric measurements (weight and height) and a complete blood count. We considered children as anemic if the hemoglobin rate was below 11g/dl. Of the 245 children, 212 were anemic, which was a prevalence of 86.5%. This anemia, frequently of the microcytic hypochromic type (68. 86%) was significantly (p < 0.0003) observed among the children of housewives compared with those whose mothers were employed. Among anemic children, 60.8% were only taking breast milk with or without cereal porridge as a food supplement. The absence of consumption of protein, vegetables, fruits and dairy products was a risk factor for the occurrence of anemia (p <0.0001). In total, at the time immunization is stopped, almost all Senegalese children, while apparently healthy, still face nutritional anemia. The adverse consequences of anemia on child health require the implementation in developing countries of a specific program of struggle against anemia. The activity of vaccination might be the best opportunity to provide the nutritional education these mothers need.

In the context of increasing climate variability, accurately monitoring the availability of water in temporary ponds in the sylvopastoral zone of Ferlo (Senegal) is crucial for pastoral resilience and food security. This study evaluates the performance of 6 spectral indices derived from Sentinel-2 imagery, MNDWI, NDWI, NDPI, TCW, NDMI, and AWEI_sh, for surface water detection, using ultra-high-resolution drone-based orthophotos (3 cm) as reference data. 10 ponds (8 naturals and 2 artificials) were selected. The orthophotos enabled a detailed classification of Sentinel-2 pixels into three land cover classes (water, bare soil, and vegetation) and allowed the calculation of the proportion of each class at the pixel level. The indices were first calibrated using ROC curves and the minimum distance to the perfect classification point across various water coverage thresholds (2% to 100%). Their performances were then evaluated and compared using confusion matrix metrics and McNemar’s test with the validation dataset. The results highlight three particularly effective indices: (i) AWEI_sh provided the best overall trade-off between completeness and precision ; (ii) TCW showed balanced performance across all cover ranges; (iii) MNDWI performed best under high water coverage conditions. A logistic regression applied to misclassified pixels revealed that vegetation significantly increases the likelihood of false positives (odds ratio > 1; p < 0.005), whereas bare soil had a more moderate effect. These findings underscore the influence of non- water classes on classification errors and highlight the importance of integrating vegetation indices into future water detection models. Study limitations include potential limited reliability of index thresholds and generalizability due to differing sensor platforms used temporally limited baseline constrained by field observations. By combining ultra-high-resolution drone data, rigorous spectral index validation, and statistical modeling of classification errors, this study offers a robust approach for fine-scale mapping of temporary water bodies in the Sahel.

Rice field mapping is essential for effective agricultural and water resource management due to high land pressure. This study aims to map paddy rice by combining segmentation techniques and phenological metrics derived from optical time series. Thus, a crop segmentation-based approach was developed using Sentinel-2 imagery (2018–2019) to assess the paddy rice extent in the Senegal River Delta (SRD). Two super-pixel segmentation algorithms were evaluated to optimize the identification of rice plots by integrating spectral and spatial characteristics from the green, red, and near-infrared (NIR) bands. In this study, the Felzenszwalb outperformed the Quickshift algorithm, achieving a median intersection over union (IoU) of 0.25 compared to 0.20 for the segmentation of rice fields. The analysis of NDVI time series enabled the identification of key stages in the rice phenological cycle. Two machine learning algorithms (i.e., Random Forest and XGBoost) were compared for rice crop detection. Random Forest delivered a better performance (AUC = 0.93, OA = 0.98, F1-score = 0.98) than the XGBoost (AUC = 0.92, OA = 0.98, F1-score = 0.98). Overall, the results indicated that the approach could accurately identify paddy rice fields, and thus improve decision making and support food security management in the region.