Integrating GIS-MCDA and Machine Learning Approach to Identify Marginal and Underutilized Lands in Leon County, Florida

Tewodros A. Simret, Sewunet A. Natae, Gang Chen, Victor Ibeanusi, Hubert Hirwa

This study developed an integrated Geographical Information System (GIS) based Multi-Criteria Decision Analysis (GIS-MCDA) framework based on hydrological marginality, soil suitability, land cover marginality, slope, social marginality, and contaminated land indicators to identify and classify marginal and underutilized lands in Leon County, Florida, using the analytic hierarchy process (AHP) and weighted overlay analysis. The AHP results assigned the highest weights to hydrological conditions (36%) and soil suitability (24%), indicating their dominant influence on land marginality. The final marginality model classified the study area as low marginality/highly suitable (24.19%), slightly marginal (39.17%), moderately marginal (2.48%), high marginality (30.66%), and very high marginality and underutilization potential (3.5%). According to sensitivity analysis, hydrology, soil, and social emphasis models showed results that were relatively stable compared to the AHP-weighted baseline model. However, an equal-weight model yielded a lower estimate. An exploratory 2035 urban-expansion scenario was developed using a standardized 2-mile (3.2 km) buffer around existing developed areas. Under this scenario, the combined area classified as high and very high marginality increased from approximately 152,771 acres (34.14%) under baseline conditions to approximately 176,534 acres (39.45%), representing a net increase of about 23,763 acres. These findings show that combining environmental and socioeconomic indicators can improve the identification of marginal and underutilized land and support sustainable land-use planning, restoration targeting, and future agricultural or bioenergy suitability assessments.