首页 | 本学科首页   官方微博 | 高级检索  
     检索      

基于Landsat-8 OLI影像的植被信息提取方法研究
引用本文:赵冰雪,章勇.基于Landsat-8 OLI影像的植被信息提取方法研究[J].测绘与空间地理信息,2018(1):79-82,85.
作者姓名:赵冰雪  章勇
作者单位:池州学院 资源环境学院,安徽 池州,247000
基金项目:池州学院教学研究基金项目,池州学院研究中心基金项目
摘    要:植被是地理环境的重要组成部分,在城市生态环境系统中扮演着非常重要的角色。本文以池州市2014年OLI遥感影像为基础,结合30 m空间分辨率的DEM数据,在ENVI 5.1和ArcGIS 10.2软件的支撑下,对该区植被信息进行提取。通过对比原始波段组合、主成分分量组合和衍生波段组合的分类精度,确定植被信息提取的最佳波段组合,并对植被提取结果进行精度验证。结果表明:考虑NDVI、绿度指数和第一主成分的衍生波段组合植被提取精度最高,与该区已知的土地利用类型中的植被覆盖度进行比较,精度达到89.16%。这说明该波段组合方案对于Landsat-8影像提取植被信息效果较好,可以为其他地区植被信息的提取提供参考。

关 键 词:OLI影像  植被提取  最佳波段组合  监督分类  OLI  Images  vegetation  information  extraction  best  band  combination  supervised  classification

Study on Vegetation Information Extraction Method Based on Landsat-8 OLI Images
ZHAO Bingxue,ZHANG Yong.Study on Vegetation Information Extraction Method Based on Landsat-8 OLI Images[J].Geomatics & Spatial Information Technology,2018(1):79-82,85.
Authors:ZHAO Bingxue  ZHANG Yong
Abstract:Vegetation is an important part of geographical environment, which plays a very significant role in the urban ecological envi-ronment system. This paper used OLI remote sensing images of Chizhou in 2014 as the foundation, combined with the DEM data of 30m, under the support of ENVI5. 1 and ArcGIS10. 2 software, the vegetation information was extracted. By comparing the original band combination, the combination of principal component and the derivative band combination, this article determined the best band combination of vegetation information extraction, and verified the accuracy of vegetation extraction results. The results show that:de-rivative band combination which considering NDVI, green index and the first principal component has the highest interpretation preci-sion, comparing with the vegetation coverage is known of land use types, the extraction accuracy was 89. 16%. Indicate that the band combination scheme for Landsat-8 images has the highest effective in extract vegetation information;it can provide reference of vege-tation information extraction for other areas.
Keywords:
本文献已被 CNKI 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号