Abstract: Southwest Yunnan is enriched in landscapes of cluster bamboo forests and rare but unique resources of bamboo species. The development and utilization of bamboo resources are greatly limited by the uninformed distribution and growing stock of bamboo forests and the lack of proper monitoring techniques. Based on Sentinel2A image data, we compared three machine learning methods: back-propagation neural network, support vector machine, and random forest, for classifying cluster bamboo forests and other land use types in Cangyuan County. Google Earth images and DEM data were used to analyze the spatial and topographic characteristics of the distribution of bamboo forests. The results showed that random forest achieved the best accuracy in classification, with overall accuracy of 90%, Kappa coefficient of 0.87, and user accuracy of 81%. With a total bamboo forest of 138.07 km², bamboo forests in Cangyuan are mainly located in towns and villages, along roads, rivers and cultivated lands. At a resolution of 10 m, Sentinel-2A data is good at characterizing spatially dispersed cluster bamboo forests. Cangyuan’s bamboo forests are mainly located at gentle slopes or incline at altitudes from 900 to 2000 m. Our results provided basis for the development and utilization of bamboo resources in Cangyuan County. The methods used here provided reference for monitoring cluster bamboo forests.