A GIS AND REMOTE SENSING APPROACH TO IDENTIFY SUITABLE SITES FOR IRRIGATION: A CASE STUDY OF KASUNGU DISTRICT
Abstract
This study investigates the integration of Geographic Information Systems (GIS) and Remote Sensing (RS) technologies for identifying suitable sites for irrigation development in Kasungu District, Malawi, a region increasingly affected by climate variability, declining rainfall, and agricultural challenges. With Malawi’s agricultural sector largely reliant on rain-fed systems, the frequent occurrence of droughts and crop failures has underscored the urgency of adopting sustainable, data-driven planning tools. The research applied a Multi-Criteria Decision Analysis (MCDA) framework combined with the Analytical Hierarchy Process (AHP) to evaluate and weight biophysical factors critical to irrigation planning, including slope, soil texture, drainage, depth, land use/land cover, and proximity to water sources and road infrastructure. Supervised classification was performed using the Random Forest algorithm within Google Earth Engine to derive accurate land cover data, while Digital Elevation Models (DEMs) and detailed soil datasets were analysed using ArcGIS to assess terrain and soil suitability. Criteria weights were determined based on expert judgment, ensuring that highly influential factors such as slope and water access were emphasized in the final model. The study produced a spatially explicit irrigation suitability map that classified land into five categories ranging from highly suitable to highly unsuitable. Results indicated that large portions of Kasungu, particularly areas with flat terrain, deep well-drained soils, and close proximity to perennial rivers like Dwangwa and Bua, were ideal for irrigation development. Conversely, steep, flood-prone, or environmentally sensitive zones were deemed unsuitable. The research not only provides a practical framework for targeted investment in irrigation infrastructure but also highlights the scalability of the model for application in other regions with similar agro-ecological conditions. By leveraging cloud-based RS data and advanced GIS modelling techniques, the study offers a replicable and cost-effective approach for land use planning, supporting national efforts toward food security, climate resilience, and sustainable development. It further recommends piloting irrigation schemes in high-suitability zones, building local capacity in spatial technologies, and institutionalizing GIS-based tools for agricultural planning. This work aligns with Malawi’s Vision 2063 and the Sustainable Development Goals (SDG’s)
Research summary
This study investigates the integration of Geographic Information Systems (GIS) and Remote Sensing (RS) technologies for identifying suitable sites for irrigation development in Kasungu District, Malawi, a region increasingly affected by climate variability, declining rainfall, and agricultural challenges. The research applied a Multi-Criteria Decision Analysis (MCDA) framework combined with the Analytical Hierarchy Process (AHP) to evaluate and weight biophysical factors critical to irrigation planning, including slope, soil texture, drainage, depth, land use/land cover, and proximity to water sources and road infrastructure. Supervised classification was performed using the Random Forest algorithm within Google Earth Engine to derive accurate land cover data, while Digital Elevation Models (DEMs) and detailed soil datasets were analysed using ArcGIS to assess terrain and soil suitability. By leveraging cloud-based RS data and advanced GIS modelling techniques, the study offers a replicable and cost-effective approach for land use planning, supporting national efforts toward food security, climate resilience, and sustainable development.