【Objective】 By studying changes in urban land types, we can determine the impact of urban evolution on environmental climate, urban development and government decision-making. 【Method】 Using the NWPU-RESISC45 standard dataset with a resolution of 15-30 m alongside Landsat 8 remote sensing images of the Harbin urban area as experimental data, we created a remote sensing image dataset that included four land types: urban buildings and roads, water bodies, vegetation and bare land. Texture information was added to the experimental data to extract SIFT (the scale-invariant feature transform) feature points. A visual dictionary containing a large amount of semantic information was also obtained using the K-means clustering algorithm to construct a bag of visual words (BOVW). The feature points extracted using BOVW were then combined with a support vector machine (SVM) to classify the dataset. Finally, using Landsat8 images of the same seasons in 2013 and 2019 and using those of the Songbei District of Harbin City as examples, the location and area change information for each land type were calculated. 【Result】 The classification results based on BOVW and SVM were compared with five single classification models and three “feature extraction + classifier” models. When using a visual dictionary of 550 words, the classification and change-monitoring accuracies of the model were 79.40% and 79.29%, respectively. The monitoring results, combined with the specific data of Harbin City, also showed that the coverage area of urban buildings and roads type, vegetation types in the Songbei District of Harbin decreased significantly, whilst the coverage area of water bodies and bare land types increased between 2013 and 2019. This change was in line with the five-year plan for the environmental protection successively launched by the Harbin municipal government in recent years, alongside the relevant policy requirements for the reasonable control of the urban scale in the master plan. 【Conclusion】 For Landsat remote sensing images with a long time-span and a low resolution, the change-monitoring models built using BOVW and SVM were both effective in monitoring land type changes. To a certain extent, this could improve the accuracy of classification and change monitoring, whilst providing a reference for the land type change monitoring.