Improving forest type classification using the vegetation local difference index
文献类型：期刊论文
| 作者 | Chen, Chenxin1; Bian, Zhao1; Li, Shengyang1; Tang, Ping1; Wu, Honggan1 |
| 刊名 | INTERNATIONAL JOURNAL OF REMOTE SENSING
 |
| 出版日期 | 2015 |
| 卷号 | 36期号:14页码:3144-3169 |
| 通讯作者 | Chen, CX (reprint author), Chinese Acad Sci, Key Lab Space Utilizat, Technol & Engn Ctr Space Utilizat, Beijing, Peoples R China. |
| 英文摘要 | A spatial feature extraction method was applied to increase the accuracy of land-cover classification of forest type information extraction. Traditional spatial feature extraction applications use high-resolution images. However, improving the classification accuracy is difficult when using medium-resolution images, such as a 30 m resolution Enhanced Thematic Mapper Plus (ETM+) image. In this study, we demonstrated a novel method that used the vegetation local difference index (VLDI) derived from the normalized difference vegetation index (NDVI), which were calculated based on the topographically corrected ETM+ image, to delineate spatial features. A simple maximum likelihood classifier and two different ways to use spatial information were introduced in this study as the frameworks to incorporate both spectral and spatial information for analysis. The results of the experiments, where Landsat ETM+ and digital elevation model (DEM) images, together with ground truth data acquired in the study area were used, show that combining the spatial information extracted from medium-resolution images and spectral information improved both classification accuracy and visual qualities. Moreover, the use of spatial information extracted through the proposed method greatly improved the classification performance of particular forest types, such as sparse woodlands. |
| 研究领域[WOS] | Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000358719900008 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38509]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1.[Chen, Chenxin 2.Li, Shengyang] Chinese Acad Sci, Key Lab Space Utilizat, Technol & Engn Ctr Space Utilizat, Beijing, Peoples R China 3.[Bian, Zhao 4.Tang, Ping] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China 5.[Wu, Honggan] Chinese Acad Sci, Res Inst Forest Resource Informat Tech, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Chenxin,Bian, Zhao,Li, Shengyang,et al. Improving forest type classification using the vegetation local difference index[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2015,36(14):3144-3169. |
| APA | Chen, Chenxin,Bian, Zhao,Li, Shengyang,Tang, Ping,&Wu, Honggan.(2015).Improving forest type classification using the vegetation local difference index.INTERNATIONAL JOURNAL OF REMOTE SENSING,36(14),3144-3169. |
| MLA | Chen, Chenxin,et al."Improving forest type classification using the vegetation local difference index".INTERNATIONAL JOURNAL OF REMOTE SENSING 36.14(2015):3144-3169. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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