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• Fine resolution IRS P6 LISS-IV satellite datasets are useful for LSA . • MCE and AHP techniques are useful to detect suitable lands for agriculture . • Ranks of criterion are based on experts’ opinion and correlation analysis. • Assigned scores of sub-criterion are based on literature survey and field work . • Reviewed lands are highly, moderately, marginally and not suitable for agriculture. Physiographic components play a fundamental role in agriculture in hilly zone. Slope, soil depth, erosion, moisture, water holding capacities, texture and availability of nutrients have affect on agricultural production. Land suitability analysis can help to formulate the strategies for improvement in agricultural productivity. GIS based multi-criterion decision making approach using IRS P6 LISS-IV dataset was used to analyze land suitability for agriculture in hilly zone. The experts’ opinions and correlation analyses were used to decide the ranks of influencing criterion whereas pairwise comparison matrix in ‘Comparison for Super Decision Software’ used to determine the weights. The scores for sub-parameters showing internal variations within the criteria assigned based on field work and reported norms in published literature. About 17% (7326 ha) of reviewed area are classified in the class ‘highly suitable’, 29% (12,372 ha) in ‘moderately suitable’, 16% (6514 ha) in ‘marginally suitable’ and 38% (15,798 ha) in ‘not suitable’ for agriculture. The land suitability classes i.e. ‘highly suitable’ and ‘not suitable’ in suitability map are precisely estimated than the classes ‘moderately suitable’ and ‘marginally suitable’ both in producer’s and user’s point of view. The methodology, techniques and findings of the study can be useful to assess the land suitability for agriculture in hilly zones.
Zolekar et al. (Wed,) studied this question.