Water ponds detections for effective mosquito control in specific agricultural areas of Sudan using remote sensing.

The study focuses on water pond detection for effective mosquito control in agricultural regions of Sudan. Mosquito-borne diseases such as malaria, dengue fever, and Rift Valley fever pose a significant threat to public health and socioeconomic development in Sudan. The rainy season in Sudan increases the risk of mosquito-borne diseases due to the proliferation of mosquitoes in stagnant water left by rainfall. Controlling mosquitoes during this period presents unique challenges due to the extensive water accumulation, limited resources, and transient nature of breeding sites. The study focuses on four areas in Sudan, These areas were chosen due to their high mosquito-borne disease burden, agricultural significance, and densely populated neighborhoods. Satellite imagery and remote sensing techniques, specifically the use of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Pond Index (NDPI), are proposed to detect water ponds.The Historical morphological data, are integrated with satellite imagery to enhance water pond detection and monitor changes over time.The integration and analysis of NDVI, NDPI, and historical morphological data improve the accuracy and reliability of water pond detection. Change detection algorithms and spatial analysis techniques enable the identification of new water ponds, tracking their expansion or contraction.