V2I12P48

A Remote Sensing and GIS-Based Multi-Criteria Approach to Agricultural Land Zoning For Sustainable Development in Odukpani LGA, Nigeria

David Mkpanam Nyong^1*, Segun Ezekiel Akorede², Tagbo Sunday Onanalo³

Abstract

This study develops a scientific framework for agricultural land zoning to inform sustainable land use planning in Odukpani Local Government Area, Nigeria. As global pressures on land resources intensify, evidence-based zoning is critical for balancing agricultural production, environmental protection, and development needs. Using Remote sensing and Geographic Information Systems (GIS)-based Multi-Criteria Decision Analysis (MCDA), five key biophysical and land-use criteria were integrated: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), land-use/land-cover, and slope. Each criterion was reclassified into suitability classes, assigned a weight based on expert knowledge and literature (NDVI: 0.30, Land-Use: 0.25, EVI: 0.20, NDWI: 0.15, Slope: 0.10), and combined via weighted overlay at a 10-meter resolution. The results classify the study area (1,350.94 km²) into three suitability zones: Highly Suitable (39.9%; 539.90 km²), Suitable (39.4%; 531.65 km²), and Not Suitable (20.7%; 279.39 km²). The Not Suitable zones correctly align with built-up areas, water bodies, and steep slopes, validating the model’s logic for sustainable planning. The Highly Suitable zones, characterized by optimal vegetation vigor and favourable topography, are priority areas for agricultural protection and investment. This research provides a transparent, spatially explicit tool for policymakers to safeguard prime agricultural land, guide sustainable intensification on suitable lands, and prevent encroachment onto ecologically sensitive areas, thereby promoting a balance between food security and environmental sustainability.

Keywords:

Agricultural Land Suitability; Geographic Information Systems (GIS); Multi-Criteria Decision Analysis (MCDA); Remote Sensing; Sustainable Land Management; Land Use Planning; Odukpani; Weighted Overlay