Analyzing Agricultural Suitability Using Geographic Information System (Gis) and Remote Sensing

This study addresses the persistent gap in localized and fine-scale agricultural suitability assessments in the Philippines by integrating Geographic Information System (GIS) and remote sensing techniques with a multi-criteria decision framework. While previous studies have explored spatial planning using limited biophysical parameters, this research introduces a novel integration of geomorphological, hydrological, climatic, and infrastructural variables—including slope, elevation, soil type, rainfall, topographic wetness index (TWI), land use/land cover (LULC), temperature, and proximity to roads and streams—using the Analytical Hierarchy Process (AHP) and Weighted Multi-Criteria Analysis (WMCA). Applied in Southern Leyte, the study reveals that 52.59% of the province's land is of very low suitability due to poor soil quality, extreme temperature variations, and limited water access, while only 2.04% is classified as very highly suitable. This fine-scale, evidence-based spatial model offers practical value: farmers can optimize crop selection and field layout; land use planners can align zoning strategies with biophysical constraints; and policymakers can prioritize irrigation and climate-adaptive interventions in vulnerable areas. The findings contribute not only to enhancing agricultural productivity and resource efficiency but also to advancing sustainable land management, mitigating climate risks, and supporting national targets under SDG 2 (Zero Hunger) and SDG 15 (Life on Land). By providing an empirically grounded, locally tailored decision support tool, this study helps bridge the gap between geospatial research and strategic agricultural development planning.