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Land cover types provide primary data for various applications and are important input parameters for ecosystem service models; however, achieving accurate acquisition of land cover types is a hot spot and complex area for research at this stage. Although remote sensing technology can make up for the shortcomings of traditional extraction methods of land cover types and is now widely used in land cover type classification studies, there are still significant challenges to inaccurate land cover type acquisition. Therefore, in this study, based on airborne LiDAR data and Sentinel-2 data, by extracting a series of parameters, the random forest algorithm was used to explore the classification results of LiDAR and Sentinel-2 data separately, and the two data were applied synergistically in order to achieve the complementary advantages of multi-source data and thus improve the classification accuracy of land cover types in urban areas. Among the LiDAR single-type parameters, the intensity parameter model has the highest classification accuracy, with an overall classification accuracy of 80.06% and Kappa=0.7370; among the Sentinel-2 single-type parameters, the texture parameter model has the highest classification accuracy, with an overall classification accuracy of 90.34% and Kappa=0.8742; among the dual-type parameter combination models, the intensity and Sentinel-2 models have the highest classification accuracy based on LiDAR The results show that (1) for airborne LiDAR data, the classification result of intensity parameter is better than the classification result of height parameter; (2) for Sentinel-2 data, the classification result of texture information has the best accuracy, followed by the classification result of band information. (2) For Sentinel-2 data, the classification result of texture information has the best accuracy, followed by the classification result of band information, and the classification result of the red-edge vegetation index is better than that of the traditional vegetation index. (3) The synergistic application of LiDAR and Sentinel-2 data can improve the classification accuracy of land cover types, and the synergistic data classification results are better than the classification results of LiDAR and Sentinel-2 single data sources. Therefore, in future research, we should try to apply multiple sources of remote sensing data together in order to achieve data complementarity and thus improve the classification accuracy of land cover types.
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