高分辨率遥感影像植被纹理特征对三维绿量的表达研究

项目名称： 高分辨率遥感影像植被纹理特征对三维绿量的表达研究

项目编号： No.31270745

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 农业科学

项目作者： 费鲜芸

作者单位： 淮海工学院

项目金额： 83万元

中文摘要： 绿地三维绿量客观地反映了绿地的质量和生态效益。在高分辨率遥感影像中，纹理能清晰地表现植被的结构、冠径、密度和阴影，这些信息与三维绿量密切相关。目前，纹理分析技术发展较好，通过有针对性的改进、结合，可以较好地提取纹理特征；同时三维激光扫描技术能够为树木三维测量研究提供支持。本研究将利用包括绿量测量当年近10年内的两期不同城区高分辨率遥感影像，制作绿地植被纹理库；利用三维激光扫描仪，对纹理库中的植被以单株或群落为单位进行三维重建、计算三维绿量，在此基础上建立植被纹理三维可视化图库。利用统计方法或二进制方法与其它方法相结合，提取图库中植被纹理特征。利用数理统计方法，分析绿量测量当年影像植被纹理特征与三维绿量相关关系，定量研究植被纹理特征对三维绿量的表达能力；选择生长缓慢树木，分析两期影像纹理特征的变化规律，定量研究分辨率、时相、成像条件等对纹理特征的影响；建立纹理特征和三维绿量的有效对应。

中文关键词： 城市绿地植被；高分辨率遥感影像；三维绿量；纹理特征；植被结构分类

英文摘要： 3D green biomass could reflect the quality and eco-efficiency of urban green space objectively. On high spatial resolution RS image, the texture structure shows well the vegetation formation, diameter, density, shadow; the information is connected with 3D green biomass. Now, the texture analysis technology has been developed well and the texture features could be extracted successfully by improving and combining of different methods according to specific application. In the same time, 3D laser scanning technology could support 3D green biomass field surveying for this study. In this study, two times of last 10 years high spatial resolution images of chosen region, including the year of biomass field surveying would be used to set up the vegetation texture image database.3D model and green biomass of the field vegetation corresponding to sample images would be surveyed by 3D laser scanning instrument as the unit of individual tree or community structure. Based on the above study, the 3D visual image database of vegetation texture would be established. The vegetation texture features of image database would be extracted by statistical or a local binary pattern (LBP) approaches combining with other algorithms. The correlation between the vegetation texture features on the images mapped in the year of biomass field

英文关键词： urban green vegetation；high spatial resolution images；3D green biomass；texture features；vegetation type classification