Geospatial Multi-Criteria Land Suitability Analysis

Geospatial Multi-Criteria Land Suitability Analysis is an interdisciplinary methodology that combines spatial data, environmental modelling and decision-making techniques to assess the capacity of land parcels for specific uses. By integrating datasets on climate, soil properties, terrain, hydrology and socio-economic factors within a Geographic Information System (GIS), practitioners can derive composite suitability maps that inform sustainable agriculture, urban planning, ecosystem restoration and infrastructure development. Core steps include the selection and standardisation of criteria, assignment of relative weights through structured methods, overlay analysis to generate suitability indices, and validation against field observations or remote sensing metrics. Recent advances such as high-resolution satellite imagery, machine learning algorithms and fuzzy-logic approaches have enhanced the resolution and reliability of suitability estimates. This framework supports global targets in food security, climate adaptation and biodiversity conservation by guiding optimal land allocation, reducing resource conflicts and promoting evidence-based policy decisions.

Research from Nature Portfolio

High-resolution evaluation of agricultural potential in a major Middle Eastern country employed very fine-scale soil, topographic and climatic datasets to classify national land into six suitability categories. The study revealed that only a small fraction of territory is optimally suited for cropping and highlighted extensive cultivation on marginal lands, underlining the need for cropland redistribution and improved resource management. In a semi-arid terrestrial ecosystem study, a fuzzy-logic-enhanced Multi-Criteria Decision Analysis was integrated with GIS and geostatistical interpolation to appraise agricultural prospects across roughly 195 000 km². Using nine terrain and soil criteria weighted by a fuzzy Analytic Hierarchy Process, the work identified about 30 percent of the region as highly suitable for cultivation, demonstrating the method’s sensitivity and its potential to inform land-use strategies under data uncertainty.

Geospatial Multi-Criteria Land Suitability Analysis publication trend

The graph below shows the total number of articles in geospatial multi-criteria land suitability analysis across all publications each year (not limited to Nature Index journals).

Technical terms

Multi-Criteria Decision Analysis (MCDA): A framework for evaluating multiple, often conflicting criteria by standardising inputs, assigning weights and aggregating results into a single decision metric.

Geographic Information System (GIS): A computer-based tool for capturing, storing, analysing and visualising spatial or geographical data in layered formats.

Analytic Hierarchy Process (AHP): A structured MCDA technique that derives criteria weights through pairwise comparisons and consistency checks, facilitating transparent decision-making.

Fuzzy Logic: An approach that allows incorporation of imprecise or uncertain data by assigning degrees of membership to criteria, rather than binary classifications.

Remote Sensing Indices (e.g. NDVI): Quantitative measures derived from satellite or aerial imagery that indicate biophysical properties, such as vegetation health and water content.

References

1. Iran’s Land Suitability for Agriculture. Scientific Reports (2017).
2. Site suitability analysis for potential agricultural land with spatial fuzzy multi-criteria decision analysis in regional scale under semi-arid terrestrial ecosystem. Scientific Reports (2020).
3. Modelling agricultural land suitability for vegetable crops farming using RS and GIS in conjunction with bivariate techniques in the Uttar Dinajpur district of Eastern India. Green Technologies and Sustainability (2023).
4. Assessing land suitability for leguminous crops in the okavango river basin: A multicriteria and machine learning approach. International Journal of Applied Earth Observation and Geoinformation (2024).