Agroforestry Land Suitability Assessment by pixel and sub-pixel classification using Geospatial Technology: A Multi-Criteria Decision Analysis Approach Mohd Hussnain * and Daulat Ram Bhardwaj

Agroforestry has emerged as a vital solution for harmonizing agriculture production and ecological sustainability. This study develops a comprehensive framework for assessing agroforestry land suitability using Geographic Information System (GIS) and multi-criteria decision analysis (MCDA) techniques. The research employed five key criteria namely climate, soil, topography, ecology, and socioeconomic factors by encompassing 14 variables to evaluate land suitability across Una district, in Western Himalayan Region. Climate variables included temperature and precipitation data from the Climatic Research Unit (CRU TS 4.01) dataset (1990–2020). Topographic parameters were derived from GTOPO30 digital elevation model, while soil characteristics were obtained from the Harmonised World Soil Database (HWSD). Ecological factors incorporated Normalized Difference Vegetation Index (NDVI) and Land Use/Land Cover (LULC) data. The weighted overlay method integrated these criteria using expert-assigned weights to generate suitability maps classified into four categories: highly suitable (S1), moderately suitable (S2), marginally suitable (S3), and not suitable (NS). Results indicate that 43.19% of the study area is highly suitable for agroforestry, 12.70% is moderately suitable, 1.03% is marginally suitable, and 1.85% is unsuitable, with 41.2% restricted due to existing forest cover. Sub-pixel classification analysis revealed 155.70 km² (10.09%) of total area under various agroforestry systems, with overall classification accuracy of 94.31% and Kappa statistic of 0.93. The methodology provides a robust framework for identifying optimal locations for agroforestry interventions, supporting sustainable land management and climate change mitigation strategies.