Integrating Artificial Intelligence, Remote Sensing, and GIS for Sustainable Agro-Forestry Management and Land Resource Optimization

Abstract

The increasing pressure on land resources due to climate change, population growth, and unsustainable land-use practices necessitates advanced approaches for sustainable agro-forestry management. This study proposes an integrated framework that combines Artificial Intelligence (AI), Remote Sensing (RS), and Geographic Information Systems (GIS) to support land suitability analysis and resource optimization. Multi-source geospatial data, including satellite imagery, environmental variables, and topographic information, are processed through a structured pipeline involving pre-processing, feature extraction, AI-based modelling, and GIS-driven spatial analysis. Machine learning and deep learning models are employed to perform land use classification and predict agro-forestry suitability, while multi-criteria decision analysis (MCDA) is used to integrate environmental factors and generate suitability maps. The results indicate that a significant portion of the study area falls within highly and moderately suitable categories, demonstrating strong potential for agro-forestry development. Model evaluation using 5-fold cross-validation shows stable performance across all models, with deep learning approaches providing slightly higher accuracy, while traditional models remain computationally efficient. The integration of NDVI-based environmental analysis with spatial modelling further validates the reliability of the framework. The resulting decision support outputs enable the identification of priority zones and optimized land-use strategies. Overall, the proposed framework offers a scalable and data-driven solution for sustainable land resource management and supports informed decision-making for agro-forestry planning.