Mapping crop classification & agricultural land in Jeli, Kelantan using satellite imagery and gis techniques

This study focuses on the classification of crop types and mapping of agricultural land in Jeli, Kelantan using satellite imagery and Geographic Information System (GIS) techniques. Landsat 8 imagery from the year 2024 was analyzed using supervised classification methods Maximum Likelihood Classification (MLC) and Minimum Distance Classification (MDC). The results demonstrated that MLC produced higher accuracy with an overall accuracy of 94.25% and a Kappa coefficient of 0.8646, outperforming MDC, which achieved 77.19% accuracy and a Kappa coefficient of 0.5597. Ground-truth data from 10 reference points confirmed MLC’s effectiveness, especially in identifying complex crop types such as oil palm, rubber, and mixed agriculture. Vegetation indices such as NDVI and EVI were also used to enhance vegetation analysis. The land cover analysis revealed that 64.30% of Jeli is covered by forest, while 31.81% is agricultural land. The study successfully developed a reliable, cost-effective geospatial methodology for ongoing crop monitoring and land-use planning. The approach is scalable and applicable to other agricultural regions, supporting sustainable resource management and agricultural development.