Abstract: 【Objective】This study was conducted to improve tropical forests protection by assessing remote sensing-based classification technology based on remote sensing. 【Methods】Using an object-oriented classification method, tropical forests was extracted based on SPOT-6 high-resolution remote sensing images. Specifically, the estimation of scale parameter(ESP)multi-scale segmentation model, coupled with experts’ knowledge, was used to determine the optimal segmentation scale parameters. Through the analysis of the spectral, shape and texture features of the image objects, a reasonable set of these features was established. As a consequence, an object-oriented multi-scale classification method was created to map the distribution of tropical forests using classification rules. 【Results】The results showed that the proposed object-oriented multi-scale classification method had the ability to extract information about the distribution of tropical forests. This method had an overall classification accuracy of 84.46%, which was an improvement over that of single-scale classification. Moreover, less time was needed and accuracy was improved with this method, compared with the traditional segmentation method. 【Conclusion】Our object-oriented multi-scale classification method provides a solid technical reference for mapping tropical forests mapping, which is fundamental for monitoring and protection of tropical forests resources.