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| 基于Landsat 8 OLI数据与面向对象分类的昆嵛山地区土地覆盖信息提取 | |
| 引用本文： | 张春华,李修楠,吴孟泉,秦伟山,张筠.基于Landsat 8 OLI数据与面向对象分类的昆嵛山地区土地覆盖信息提取[J].地理科学,2018,38(11):1904-1913. |
| 作者姓名： | 张春华 李修楠 吴孟泉 秦伟山 张筠 |
| 作者单位： | 1.鲁东大学资源与环境工程学院,山东 烟台 264025 2.国家海洋局第二海洋研究所卫星海洋环境动力学国家重点实验室,浙江 杭州 310012 |
| 基金项目： | 山东省自然科学基金项目（ZR2016DP05）、国家自然科学基金项目（41601054, 41501129）资助 |
| 摘 要： | 利用2015年Landsat 8 OLI遥感影像和DEM作为分类数据源,结合野外调查数据,采用面向对象的分类方法对昆嵛山地区土地覆盖信息进行提取,并对分类结果进行精度评价与比较分析。研究表明：面向对象分类方法提取的各地类连续且边界清晰,分类效果与实际情况基本吻合。昆嵛山地区占主导地位的土地覆盖类型是针叶林,面积为1 546.81 km2。研究区土地覆盖分类的总体精度和Kappa系数分别为91.5%和0.88,其中针叶林、草地、水体和建设用地的生产者精度均达到87%以上。相对于监督分类方法,本研究提出的土地覆盖信息提取方法的总体分类精度和Kappa系数分别提高14.7%和0.17。基于面向对象的中分辨率遥感影像,能够获取较高精度的土地覆盖信息,为大范围土地覆盖分类研究提供方法参考。 |
| 关 键 词： | 土地覆盖分类 面向对象方法 Landsat 8 OLI DEM 昆嵛山 |
| 收稿时间： | 2017-11-26 |
| 修稿时间： | 2018-06-14 |
Object-oriented Classification of Land Cover Based on Landsat 8 OLI Image Data in the Kunyu Mountain |
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| Chunhua Zhang,Xiunan Li,Mengquan Wu,Weishan Qin,Jun Zhang.Object-oriented Classification of Land Cover Based on Landsat 8 OLI Image Data in the Kunyu Mountain[J].Scientia Geographica Sinica,2018,38(11):1904-1913. | |
| Authors: | Chunhua Zhang Xiunan Li Mengquan Wu Weishan Qin Jun Zhang |
| Institution: | 1.School of Resources and Environmental Engineering, Ludong University, Yantai 264025, Shandong, China 2.State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, Zhejiang, China |
| Abstract: | 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 km2. 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. |
| Keywords: | land cover classification object-oriented method Landsat 8 OLI DEM the Kunyu Mountain |
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