Land cover classification is the basis for geoscience and global change studies.It can provide essential information for modelling and understanding the complex interactions between human activities and global change.Remote sensing has been widely recognized as the most economic and feasible approach to derive land cover information on a large regional scale.Landsat satellite data are commonly used remote sensing data for land cover classification.The object-oriented classification method,which takes full advantage of the spectral,geometrical and textural information of remote sensing images and considers the spatial distribution characteristics and correlations of geographical objects,can mitigate the deficiency associated with the pixel-based approach.The purpose of this study is to deepen the application of object-oriented classification method that is utilized to extract land cover information automatically and quickly from the satellite imagery.Taking the Kunyu Mountain of Jiaodong peninsula in Shandong province as the study area,land cover classification was conducted by using the object-oriented classification method on eCognition software platform,with Landsat 8 OLI satellite image in 2015 and digital elevation model (DEM) as data sources.Firstly,Landsat 8 OLI data of high quality was selected,and preprocessed by radiometric calibration,atmospheric correction,accurate geometric correction,image registration and fusion.Feature parameters including spectral (normalized difference vegetation index (NDVI),band brightness),shape (area,roundness,rectangular fit),and topographic (DEM,slope) characteristics were calculated.Then,the land cover information was classified into cropland,grassland,needleleaf forest,broadleaf forest,built-up land,water bodies,and barren land by the object-oriented method following the steps of multi-resolution image segmentation,object feature extraction,and classification rule set construction.Finally,the accuracy of this method was evaluated and compared with that of the pixel-based supervised classification method and ground validation sampling points.The results indicate that land cover information extracted by the object-oriented classification method using Landsat 8 OLI data is well consistent with the true condition on distribution and range of each land cover type in the Kunyu Mountain.The dominant type of land cover is needleleaf forests,with the area of 1546.81 km~2.The overall accuracy and Kappa coefficient of the method are 91.5% and 0.88,respectively.The production accuracy is higher than 87% for needleleaf forests,grassland,water bodies,and built-up land.By comparison with the maximum likelihood supervised classification method,the overall classification accuracy and Kappa coefficient of the proposed method in this study are increased by 14.7% and 0.17,respectively.This means the moderate resolution Landsat 8 OLI image,combined with the object-oriented classification method can effectively improve the accuracy of land cover information extraction in the typical vegetation areas.This study will provide a credible approach and valuable example for extracting and monitoring regional land cover type,and broaden the application vision and the scope of ecological remote sensing investigation in terrestrial ecosystem.