Landsat satellite images are extensively used in land-use studies due to their relatively high spatial resolution. However, the number of usable data sets is limited by the relatively long revisit interval and phenology effects can significantly reduce classification accuracy. Moderate Resolution Imaging Spectroradiometer (MODIS) images have higher temporal frequency and can provide extra time-series information. However, they are limited in their capability to classify heterogeneous landscapes due to their coarse spatial resolution. Fusion of different data sources is a potential solution for improving land-cover classification. This paper proposes a fusion scheme to combine Landsat and MODIS remote sensing data at the decision level. First, multiresolution segmentations on the two kinds of remote sensing data are performed to identify the landscape objects and are used as fusion units in subsequent steps. Then, fuzzy classifications are applied to each of the two different resolution data sets and the classification accuracies are evaluated. According to the performance of the two data sets in classification evaluation, a simple weight assignment technique based on the weighted sum of the membership of imaged objects is implemented in the final classification decision. The weighting factors are calculated based on a confusion matrix and the heterogeneity of detected land cover. The algorithm is capable of integrating the time-series spectral information of MODIS data with spatial contexts extracted from Landsat data, thus improving the land-cover classification accuracy. The overall classification accuracy using the fusion technique increased by 7.43% and 10.46% compared with the results from the individual Landsat and MODIS data, respectively.