The use of satellite imagery has supported the identification of crops, crop monitoring and agricultural management. Cash crop monitoring is a substantial agricultural management practice for commodity purposes in Thailand. Our study has applied a hierarchical rule-based classification schema combined with feature selection and membership functions to identify tobacco plantations in Sukhothai province, Thailand using Landsat 8 imagery. The practical classification approach was used on imagery spanning from 2014 – 2017. The overall accuracy under our rule-based classification is 85.61% with a Kappa index of 0.73 in 2017 imagery in Sukhothai. The average area of tobacco plantations from 2014 – 2017 is 4,740 hectares, representing 0.72% of the entire province. The accuracy assessment of our rule-based schema could be improved with more feature selections based on a larger sample size. However, our study successfully utilized 15m resolution satellite data in identifying current tobacco plantations to further advance the prospects of precision agriculture in Thailand. Overall, the rule-based classification approach significantly reduces the time dedicated to in field work manually identifying the area of tobacco plantations.