This work presents a very simple but robust, synthetic aperture radar (SAR) and optical, data fusion framework for land use/cover change detection. The fusion was done with two indicators, namely the normalized difference ratio (NDR) and normalized difference vegetation index difference (NDVI difference) developed from multi-temporal SAR and optical images respectively. A statistical analysis shows that the NDR and the NDVI difference have a consistent pattern in major land use/cover change classes. Thus, based on this pattern, a fusion approach was developed without altering the behavior of NDR with different types of changes. The effectiveness of the proposed fusion approach was evaluated through the change mapping with a manual trial and error thresholding approach. The results were compared with the results obtained from the optical and SAR images independently. The improvement of the results by making use of the the unique information from both, optical and SAR imagery, can be easily identified with a simple visual inspection. The accuracy assessment showed a significant improvement in overall detectability with the substantial decrease in false and missing alarms.