Multispectral versus texture features from ZiYuan-3 for recognizing on deciduous tree species with cloud and SVM models
文献类型：期刊论文
| 作者 | Liu, Xiao1; Wang, Ling5,6; Liu, Xiaolu1; Li, Langping; Zhu, Xicun5,6; Chang, Chunyan5,6; Lan, Hengxing3,4 |
| 刊名 | SCIENTIFIC REPORTS
 |
| 出版日期 | 2023-05-05 |
| 卷号 | 13期号:1页码:7369 |
| ISSN号 | 2045-2322 |
| DOI | 10.1038/s41598-023-28532-0 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Tree species recognition accuracy greatly affects forest remote sensing mapping and forestry resource monitoring. The multispectral and texture features of the remote sensing images from the ZiYuan-3 (ZY-3) satellite at two phenological phases of autumn and winter (September 29th and December 7th) were selected for constructing and optimizing sensitive spectral indices and texture indices. Multidimensional cloud model and support vector machine (SVM) model were constructed by the screened spectral and texture indices for remote sensing recognition of Quercus acutissima (Q. acutissima) and Robinia pseudoacacia (R. pseudoacacia) on Mount Tai. The results showed that, the correlation intensities of the constructed spectral indices with tree species were preferable in winter than in autumn. The spectral indices constructed by band 4 showed the superior correlation compared with other bands, both in the autumn and winter time phases. The optimal sensitive texture indices for both phases were mean, homogeneity and contrast for Q. acutissima, and contrast, dissimilarity and second moment for R. pseudoacacia. Spectral features were found to have a higher recognition accuracy than textural features for recognizing on both Q. acutissima and R. pseudoacacia, and winter showing superior recognition accuracy than autumn, especially for Q. acutissima. The recognition accuracy of the multidimensional cloud model (89.98%) does not show a superior advantage over the one-dimensional cloud model (90.57%). The highest recognition accuracy derived from a three-dimensional SVM was 84.86%, which was lower than the cloud model (89.98%) in the same dimension. This study is expected to provide technical support for the precise recognition and forestry management on Mount Tai. |
| WOS关键词 | HIGH-RESOLUTION ; CLASSIFICATION ; BIODIVERSITY ; RECOGNITION ; FOREST |
| WOS研究方向 | Science & Technology - Other Topics |
| WOS记录号 | WOS:001022544000001 |
| 出版者 | NATURE PORTFOLIO |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/194412]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Minist Nat Resources, Key Lab Ecol Geol & Disaster Prevent, Xian 710054, Peoples R China 3.Changan Univ, Sch Geol Engn & Geomat, Xian 710064, Peoples R China 4.Shandong Agr Univ, Natl Engn Lab Efficient Utilizat Soil & Fertilizer, Tai An 271018, Peoples R China 5.Shandong Agr Univ, Coll Resources & Environm, Tai An 271018, Peoples R China 6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Xiao,Wang, Ling,Liu, Xiaolu,et al. Multispectral versus texture features from ZiYuan-3 for recognizing on deciduous tree species with cloud and SVM models[J]. SCIENTIFIC REPORTS,2023,13(1):7369. |
| APA | Liu, Xiao.,Wang, Ling.,Liu, Xiaolu.,Li, Langping.,Zhu, Xicun.,...&Lan, Hengxing.(2023).Multispectral versus texture features from ZiYuan-3 for recognizing on deciduous tree species with cloud and SVM models.SCIENTIFIC REPORTS,13(1),7369. |
| MLA | Liu, Xiao,et al."Multispectral versus texture features from ZiYuan-3 for recognizing on deciduous tree species with cloud and SVM models".SCIENTIFIC REPORTS 13.1(2023):7369. |
入库方式： OAI收割
来源：地理科学与资源研究所
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