Mapping Panax Notoginseng Plantations by Using an Integrated Pixel- and Object-Based (IPOB) Approach and ZY-3 Imagery
文献类型：期刊论文
| 作者 | Yang, Zhiqi3,4; Dong, Jinwei3; Kou, Weili1; Qin, Yuanwei2; Xiao, Xiangming2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2021-06-01 |
| 卷号 | 13期号:11页码:22 |
| 关键词 | Random Forest integrated pixel- and object-based (IPOB) approach feature selection segmentation Panax notoginseng |
| DOI | 10.3390/rs13112184 |
| 通讯作者 | Dong, Jinwei(dongjw@igsnrr.ac.cn) |
| 英文摘要 | Plantations of Panax notoginseng (PN), traditional herbal medicine for the prevention and treatment of vascular diseases, are expanding rapidly in China, especially in the Yunnan province of China, due to its increasing demands and prices and causing dramatic environmental concerns. However, existing information on its planting area and spatial distribution are limited. Here, we mapped the PN planting area by using a new integrated pixel- and object-based (IPOB) approach, the Random Forest (RF) classifier, and the high-resolution ZiYuan-3 (ZY-3) imagery. We improved the procedures of classification in three aspects: (1) a new spectral index-Normalized Difference PN Index (NDPI)-was proposed, (2) the efficiency and scale of segmentation were optimized by using the Bi-level Scale-sets Model (BSM), and (3) feature variables were selected through an iteration analysis from 99 feature variables (spectral, textural, geometric, and geographic). Compared with the pixel- and the object-based methods, the IPOB has the highest F1 score of 0.98 and also has high robustness in terms of user and producer accuracies (97% and 99%, respectively), following by the object-based method (F1 = 0.94) and the pixel-based method (F1 = 0.93). The high accuracy was expected since the target class has very distinctive spectral and textural characteristics. Although all three approaches showed reasonably high accuracies due to the application of the NDPI and optimized procedures, the result showed the outperformance of the proposed IPOB approach. The framework established in this study expects to apply for regional or national PN surveys extensively. The information on the area and spatial distribution of PN can guide the government on policy making for the planting and exporting of traditional Chinese medicine resources. |
| WOS关键词 | RANDOM FOREST CLASSIFICATION ; LAND-COVER ; LIDAR DATA ; SCALE ; TREE ; SEGMENTATION ; ALGORITHMS ; SELECTION ; ACCURACY ; REGION |
| 资助项目 | Key Research Program of Frontier Sciences[QYZDB-SSWDQC005] ; Chinese Academy of Sciences (CAS)[XDA19040301] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000660609100001 |
| 出版者 | MDPI |
| 资助机构 | Key Research Program of Frontier Sciences ; Chinese Academy of Sciences (CAS) |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/164178]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Dong, Jinwei |
| 作者单位 | 1.Southwest Forestry Univ, Coll Big Data & Intelligence Engn, Kunming 650224, Yunnan, Peoples R China 2.Univ Oklahoma, Dept Microbiol & Plant Biol, Norman, OK 73019 USA 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Zhiqi,Dong, Jinwei,Kou, Weili,et al. Mapping Panax Notoginseng Plantations by Using an Integrated Pixel- and Object-Based (IPOB) Approach and ZY-3 Imagery[J]. REMOTE SENSING,2021,13(11):22. |
| APA | Yang, Zhiqi,Dong, Jinwei,Kou, Weili,Qin, Yuanwei,&Xiao, Xiangming.(2021).Mapping Panax Notoginseng Plantations by Using an Integrated Pixel- and Object-Based (IPOB) Approach and ZY-3 Imagery.REMOTE SENSING,13(11),22. |
| MLA | Yang, Zhiqi,et al."Mapping Panax Notoginseng Plantations by Using an Integrated Pixel- and Object-Based (IPOB) Approach and ZY-3 Imagery".REMOTE SENSING 13.11(2021):22. |
入库方式： OAI收割
来源：地理科学与资源研究所
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