Evaluating the Potential of Multi-Seasonal CBERS-04 Imagery for Mapping the Quasi-Circular Vegetation Patches in the Yellow River Delta Using Random Forest
文献类型：期刊论文
| 作者 | Liu, Qingsheng2,3; Song, Hongwei1; Liu, Gaohuan2; Huang, Chong2; Li, He2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2019-05-02 |
| 卷号 | 11期号:10页码:24 |
| 关键词 | CBERS-04 multi-seasonal images quasi-circular vegetation patch random forest Yellow River Delta |
| DOI | 10.3390/rs11101216 |
| 通讯作者 | Liu, Qingsheng(liuqs@lreis.ac.cn) |
| 英文摘要 | High-resolution satellite imagery enables decametric-scale quasi-circular vegetation patch (QVP) mapping, which greatly aids the monitoring of vegetation restoration projects and the development of theories in pattern evolution and maintenance research. This study analyzed the potential of employing five seasonal fused 5 m spatial resolution CBERS-04 satellite images to map QVPs in the Yellow River Delta, China, using the Random Forest (RF) classifier. The classification accuracies corresponding to individual and multi-season combined images were compared to understand the seasonal effect and the importance of optimal image timing and acquisition frequency for QVP mapping. For classification based on single season imagery, the early spring March imagery, with an overall accuracy (OA) of 98.1%, was proven to be more adequate than the other four individual seasonal images. The early spring (March) and winter (December) combined dataset produced the most accurate QVP detection results, with a precision rate of 66.3%, a recall rate of 43.9%, and an F measure of 0.528. For larger study areas, the gain in accuracy should be balanced against the increase in processing time and space when including the derived spectral indices in the RF classification model. Future research should focus on applying higher resolution imagery to QVP mapping. |
| WOS关键词 | HIGH-SPATIAL-RESOLUTION ; TREE SPECIES CLASSIFICATION ; LAND-COVER CLASSIFICATION ; REMOTELY-SENSED IMAGERY ; TIME-SERIES ; WORLDVIEW-2 IMAGERY ; GREEN LAI ; SPOT 5 ; CROP ; SOIL |
| 资助项目 | National Natural Science Foundation of China[41671422] ; National Natural Science Foundation of China[41661144030] ; National Natural Science Foundation of China[4151144012] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA20030302] ; Innovation Project of LREIS[O88RA20CYA] ; Innovation Project of LREIS[08R8A010YA] ; National Mountain Flood Disaster Investigation Project[SHZH-IWHR-57] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000480524800074 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Innovation Project of LREIS ; National Mountain Flood Disaster Investigation Project |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/68858]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Qingsheng |
| 作者单位 | 1.Henan Aero Geophys Survey & Remote Sensing Ctr, Zhengzhou 450053, Henan, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Qingsheng,Song, Hongwei,Liu, Gaohuan,et al. Evaluating the Potential of Multi-Seasonal CBERS-04 Imagery for Mapping the Quasi-Circular Vegetation Patches in the Yellow River Delta Using Random Forest[J]. REMOTE SENSING,2019,11(10):24. |
| APA | Liu, Qingsheng,Song, Hongwei,Liu, Gaohuan,Huang, Chong,&Li, He.(2019).Evaluating the Potential of Multi-Seasonal CBERS-04 Imagery for Mapping the Quasi-Circular Vegetation Patches in the Yellow River Delta Using Random Forest.REMOTE SENSING,11(10),24. |
| MLA | Liu, Qingsheng,et al."Evaluating the Potential of Multi-Seasonal CBERS-04 Imagery for Mapping the Quasi-Circular Vegetation Patches in the Yellow River Delta Using Random Forest".REMOTE SENSING 11.10(2019):24. |
入库方式： OAI收割
来源：地理科学与资源研究所
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