Sugar Cane Modeling Using GIS And Remote Sensing For Perambalur District

Abstract This study addresses land evaluation for sugar cane suitability, and demonstrates the usefulness of integrating both legacy cartographic and contemporary data to help solve assessment problems. Land evaluation techniques have proved useful for supporting rational management of land resources and sustainable development across many sectors. A Geographical Information System (GIS) and Remote Sensing (RS) were used to identify suitable lands for growing sugar cane at 2 sites in perambalur sugar mills district.The basic FAO land evaluation framework was adopted, using readily available data including terrain and soil. Satellite data were utilised to derive several thematic maps to help identify areas with the required potentials. A GIS-based suitability analysis was conducted using the ESRI ArcGIS software, and the input datasets reclassified to assign categories that could be integrated in one model. A weighted overlay method was used, along with a traditional boolean raster method, to allow comparison of results from each method. The weighted overlay method areas demarked more land as ‘suitable' than did the traditional boolean method. This could derive from the assignment of differing weightings in the weighted overlay, making it a more flexible operation when compared to the strict “true or false” assessment of the boolean method. Across the selected study area, an estimated 75% of the land was classified as being ‘moderately suitable' for sugar cane. One future means to fully differentiate these areas would be the introduction of precision farming techniques to enable continuous management of the crop and to obtain improved yield production.