Fine-Scale Classification of Dominant Vegetation Communities in Coastal Wetlands Using Color-Enhanced Aerial Images
文献类型：期刊论文
| 作者 | Liu, Yixian4,7; Zhang, Yiheng4,7; Zhang, Xin5; Che, Chunguang2; Huang, Chong7; Li, He7; Peng, Yu6; Li, Zishen6; Liu, Qingsheng1,3,7 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025-08-15 |
| 卷号 | 17期号:16页码:2848 |
| 关键词 | coastal wetlands aerial images vegetation classification object-oriented color enhancement |
| DOI | 10.3390/rs17162848 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Monitoring salt marsh vegetation in the Yellow River Delta (YRD) wetland is the basis of wetland research, which is of great significance for the further protection and restoration of wetland ecological functions. In the existing remote sensing technologies for wetland salt marsh vegetation classification, the object-oriented classification method effectively produces landscape patches similar to wetland vegetation and improves the spatial consistency and accuracy of the classification. However, the vegetation classes of the YRD are mixed with uneven distribution, irregular texture, and significant color variation. In order to solve the problem, this study proposes a fine-scale classification of dominant vegetation communities using color-enhanced aerial images. The color information is used to extract the color features of the image. Various features including spectral features, texture features and vegetation features are extracted from the image objects and used as inputs for four machine learning classifiers: random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN) and maximum likelihood (MLC). The results showed that the accuracy of the four classifiers in classifying vegetation communities was significantly improved by adding color features. RF had the highest OA and Kappa coefficients of 96.69% and 0.9603. This shows that the classification method based on color enhancement can effectively distinguish between vegetation and non-vegetation and extract each vegetation type, which provides an effective technical route for wetland vegetation classification in aerial imagery. |
| URL标识 | 查看原文 |
| WOS关键词 | SUPPORT VECTOR MACHINE ; RANDOM FOREST ; SEGMENTATION ; DELTA ; SEDIMENT ; INDEXES ; IMPACT ; SOIL |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001558505100001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216158]  |
| 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 | Liu, Qingsheng |
| 作者单位 | 1.Key Lab Nat Resource Coupling Proc & Effects, Beijing 100055, Peoples R China; 2.Yellow River Mouth Management Stn Yellow River Del, Dongying 257500, Peoples R China; 3.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 5.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; 6.Qilu Aerosp Informat Res Inst, Jinan 250132, Peoples R China; 7.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Liu, Yixian,Zhang, Yiheng,Zhang, Xin,et al. Fine-Scale Classification of Dominant Vegetation Communities in Coastal Wetlands Using Color-Enhanced Aerial Images[J]. REMOTE SENSING,2025,17(16):2848. |
| APA | Liu, Yixian.,Zhang, Yiheng.,Zhang, Xin.,Che, Chunguang.,Huang, Chong.,...&Liu, Qingsheng.(2025).Fine-Scale Classification of Dominant Vegetation Communities in Coastal Wetlands Using Color-Enhanced Aerial Images.REMOTE SENSING,17(16),2848. |
| MLA | Liu, Yixian,et al."Fine-Scale Classification of Dominant Vegetation Communities in Coastal Wetlands Using Color-Enhanced Aerial Images".REMOTE SENSING 17.16(2025):2848. |
入库方式： OAI收割
来源：地理科学与资源研究所
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