Multi-scale object-based measurement of arid plant community structure
文献类型：期刊论文
| 作者 | Zhang, Lei1; Li, Xiaosong1; Lu, Shanlong1; Jia, Kun1 |
| 刊名 | International Journal of Remote Sensing
 |
| 出版日期 | 2016 |
| 卷号 | 37期号:10页码:2168-2179 |
| 关键词 | NEWLY CREATED WETLAND CARBON SEQUESTRATION MULTISPECTRAL IMAGES IMPERVIOUS SURFACES HYPERSPECTRAL DATA COASTAL WETLANDS ANALYSIS MESMA BURN SEVERITY NEW-MEXICO VEGETATION |
| 通讯作者 | Lu, Shanlong (lusl@radi.ac.cn) |
| 英文摘要 | The measurement of plant community structure provides an extensive understanding of its function, succession and ecological process. The detection of plant community boundary is rather a challenge despite in situ work. Recent advances in object-based image analysis (OBIA) and machine learning algorithms offer new opportunities to address this challenge. This study presents a multi-scale segmentation approach to accurately identify the boundaries of each vegetation and plant community for mapping plant community structure. Initially, a very high resolution (VHR) Worldview-2 image of a desert area is hierarchically segmented from scale parameter 2 to 500. Afterward, the peak values of the standard deviation of brightness and normalized difference vegetation index (NDVI) across the segmentation scales are detected to determine the optimal segmentation scales of homogeneous single plant and plant community boundaries. A multi-scale classification of vegetation characterization with features of multiple bands, NDVI, grey-level co-occurrence matrix (GLCM) entropy and shape index is performed to identify dryland vegetation types. Finally, the four vegetation structural features on the type, diversity, object size and shape are calculated within the plant community boundaries and composed to plant community structure categories. Comparing the results with the object fitting index (FI) of the reference data, the validation indicates that the optimal segmentations of tree, shrub and plant communities are consistent with the identified peak values. © 2016 Informa UK Limited, trading as Taylor & Francis Group. |
| 学科主题 | Remote Sensing; Imaging Science & Photographic Technology |
| 类目[WOS] | Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20162902623067 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39326]  |
| 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. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China 2. State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, China |
| 推荐引用方式 GB/T 7714 | Zhang, Lei,Li, Xiaosong,Lu, Shanlong,et al. Multi-scale object-based measurement of arid plant community structure[J]. International Journal of Remote Sensing,2016,37(10):2168-2179. |
| APA | Zhang, Lei,Li, Xiaosong,Lu, Shanlong,&Jia, Kun.(2016).Multi-scale object-based measurement of arid plant community structure.International Journal of Remote Sensing,37(10),2168-2179. |
| MLA | Zhang, Lei,et al."Multi-scale object-based measurement of arid plant community structure".International Journal of Remote Sensing 37.10(2016):2168-2179. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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