Delineating the Distribution Outline of Populus euphratica in the Mainstream Area of the Tarim River Using Multi-Source Thematic Classification Data
文献类型：期刊论文
| 作者 | Li, Hao2; Zou, Jiawei3; Zhao, Qinyu4; Hu, Jiacong2; Liu, Suhong5; Shi, Qingdong4; Cheng, Weiming1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2026-01-03 |
| 卷号 | 18期号:1页码:157 |
| 关键词 | Populus euphratica distribution vector data alpha-shape algorithm Gaussian filtering recognition Populus euphratica mapping |
| DOI | 10.3390/rs18010157 |
| 产权排序 | 5 |
| 文献子类 | Article |
| 英文摘要 | Highlights What are the main findings? We proposed a method integrating morphological operations on raster imagery and vector point density information to eliminate misclassified pixels and outliers for optimizing the original data. We introduced a density-based hierarchical scheme using the alpha-shape algorithm (alpha = 0.02 and alpha = 0.006) for precise contour identification of both dense and sparse areas. What are the implications of the main findings? Populus euphraticaThe study demonstrated that Gaussian filtering outperformed other smoothing algorithms in refining distribution outlines, effectively reducing the saw-tooth phenomenon. We established an automated technical framework integrating multi-source data, density-based outlier removal, and morphological processing for vector mapping of desert vegetation distribution.Highlights What are the main findings? We proposed a method integrating morphological operations on raster imagery and vector point density information to eliminate misclassified pixels and outliers for optimizing the original data. We introduced a density-based hierarchical scheme using the alpha-shape algorithm (alpha = 0.02 and alpha = 0.006) for precise contour identification of both dense and sparse areas. What are the implications of the main findings? Populus euphraticaThe study demonstrated that Gaussian filtering outperformed other smoothing algorithms in refining distribution outlines, effectively reducing the saw-tooth phenomenon. We established an automated technical framework integrating multi-source data, density-based outlier removal, and morphological processing for vector mapping of desert vegetation distribution.Abstract Populus euphratica is a key constructive species in desert ecosystems and plays a vital role in maintaining their stability. However, effective automated methods for accurately delineating its distribution outlines are currently lacking. This study used the mainstream area of the Tarim River as a case study and proposed a technical solution for identifying the distribution outline of Populus euphratica using multi-source thematic classification data. First, cropland thematic data were used to optimize the accuracy of the Populus euphratica classification raster data. Discrete points were removed based on density to reduce their impact on boundary identification. Then, a hierarchical identification scheme was constructed using the alpha-shape algorithm to identify the boundaries of high- and low-density Populus euphratica distribution areas separately. Finally, the outlines of the Populus euphratica distribution polygons were smoothed, and the final distribution outline data were obtained after spatial merging. The results showed the following: (1) Applying a closing operation to the cropland thematic classification data to obtain the distribution range of shelterbelts effectively eliminated misclassified pixels. Using the kd-tree algorithm to remove sparse discrete points based on density, with a removal ratio of 5%, helped suppress the interference of outlier point sets on the Populus euphratica outline identification. (2) Constructing a hierarchical identification scheme based on differences in Populus euphratica density is critical for accurately delineating its distribution contours. Using the alpha-shape algorithm with parameters set to alpha = 0.02 and alpha = 0.006, the reconstructed geometries effectively covered both densely and sparsely distributed Populus euphratica areas. (3) In the morphological processing stage, a combination of three methods-Gaussian filtering, equidistant expansion, and gap filling-effectively ensured the accuracy of the Populus euphratica outline. Among the various smoothing algorithms, Gaussian filtering yielded the best results. The equidistant expansion method reduced the impact of elongated cavities, thereby contributing to boundary accuracy. This study enhances the automation of Populus euphratica vector data mapping and holds significant value for the scientific management and research of desert vegetation. |
| URL标识 | 查看原文 |
| WOS关键词 | SYSTEM |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001657665800001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219638]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Hu, Jiacong |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Beijing Normal Univ, Expt Teaching Platform, Zhuhai 519000, Peoples R China; 3.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China; 4.Xinjiang Univ, Coll Geog & Remote Sensing Sci, Urumqi 830017, Peoples R China; 5.Beijing Normal Univ, Fac Arts & Sci, Dept Geog Sci, Zhuhai 519000, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Li, Hao,Zou, Jiawei,Zhao, Qinyu,et al. Delineating the Distribution Outline of Populus euphratica in the Mainstream Area of the Tarim River Using Multi-Source Thematic Classification Data[J]. REMOTE SENSING,2026,18(1):157. |
| APA | Li, Hao.,Zou, Jiawei.,Zhao, Qinyu.,Hu, Jiacong.,Liu, Suhong.,...&Cheng, Weiming.(2026).Delineating the Distribution Outline of Populus euphratica in the Mainstream Area of the Tarim River Using Multi-Source Thematic Classification Data.REMOTE SENSING,18(1),157. |
| MLA | Li, Hao,et al."Delineating the Distribution Outline of Populus euphratica in the Mainstream Area of the Tarim River Using Multi-Source Thematic Classification Data".REMOTE SENSING 18.1(2026):157. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。