Journal of Mosquito Research 2024, Vol.14, No.3, 161-171 http://emtoscipublisher.com/index.php/jmr 165 multispectral and LiDAR data significantly improved the accuracy of land cover classification, achieving a Kappa value of 0.88. This enhanced classification allows for better identification and management of mosquito habitats in urban settings, thereby aiding public health efforts to control mosquito-borne diseases (Hartfield et al., 2011). 5.2 Case study 2: rural mosquito monitoring and control In rural areas, the application of drones has shown promise in identifying and managing mosquito larval habitats. A study in Kasungu district, Malawi, utilized high-resolution drone mapping to identify water bodies and aquatic vegetation, which are key mosquito breeding sites. The study employed both manual methods and geographical object-based image analysis (GeoOBIA) to classify these habitats. The GeoOBIA approach demonstrated high accuracy (median accuracy=0.98, median kappa=0.96) but required more processing time and technical expertise. The study found a significant relationship between larval presence and vegetation type, highlighting the potential of drone-acquired imagery to support mosquito control efforts in rural areas where malaria is endemic (Figure 1) (Stanton et al., 2020). Through high-resolution images captured by drones, researchers can analyze the environmental characteristics of mosquito larval habitats in detail, such as types of water bodies and vegetation coverage. This data is crucial for formulating effective mosquito control strategies, especially in rural areas with high incidences of malaria. The application of drone technology in rural areas not only significantly enhances the efficiency and accuracy of identifying mosquito larval habitats but also provides important technical support for mosquito control measures. This research achievement provides strong empirical evidence for using drones in malaria prevention and control. 5.3 Case study 3: integrating GIS with community-based monitoring Integrating GIS with community-based monitoring can enhance the effectiveness of mosquito control programs. In Pos Lenjang, Kuala Lipis, Pahang, Malaysia, a study combined field data with satellite image analysis and GIS techniques to map larval breeding habitats and malaria transmission risk areas. The study digitized geographical features such as rivers, streams, and residential areas, and overlaid them with entomological data. The resulting maps showed that more than 80% of Anopheles maculatus s.s. immature habitats were within 400 m of human settlements. This integration of GIS and community-based monitoring provides a rational basis for strategic planning and management of mosquito control at the national level (Ahmad et al., 2011). 5.4 Lessons learned from case studies The case studies highlight several key lessons in the application of GIS for mosquito monitoring and control. The integration of high-resolution data, such as multispectral and LiDAR, significantly improves the accuracy of habitat classification and mosquito monitoring in urban areas (Hartfield et al., 2011). Drone technology offers a practical solution for identifying mosquito larval habitats in rural settings, although it requires technical expertise and processing time (Stanton et al., 2020; Mukabana et al., 2022). Combining GIS with community-based monitoring can provide valuable insights and enhance the effectiveness of mosquito control programs, particularly in mapping and managing breeding sites (Ahmad et al., 2011; Deleon et al., 2017). GIS-based approaches facilitate strategic planning and resource allocation, making mosquito control efforts more efficient and targeted. These lessons underscore the importance of leveraging advanced technologies and community involvement in developing comprehensive mosquito control strategies. 6 Challenges and Limitations of GIS in Mosquito Monitoring 6.1 Data quality and availability One of the primary challenges in utilizing Geographic Information Systems (GIS) for mosquito monitoring is the quality and availability of data. Accurate and up-to-date data are crucial for effective mapping and analysis. However, data collection can be inconsistent, and the quality of data can vary significantly. For instance, in the study conducted in Pos Lenjang, Malaysia, the integration of field data with satellite images was essential for developing accurate maps of larval breeding habitats and malaria transmission risk areas. Similarly, the review of GIS applications in schistosomiasis control in China highlighted the need for reliable baseline data to predict disease distributions effectively (Fletcher-Lartey and Caprarelli, 2016). The heterogeneity in data sources and the
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