Forest Type Classification Based on Integrated Spectral-Spatial-Temporal Features and Random Forest Algorithm-A Case Study in the Qinling Mountains
文献类型：期刊论文
| 作者 | Cheng, Kai1,2; Wang, Juanle2,3,4 |
| 刊名 | FORESTS
 |
| 出版日期 | 2019-07-01 |
| 卷号 | 10期号:7页码:18 |
| 关键词 | forest type spectral-spatial-temporal features random forest random forest-recursive feature elimination sentinel-2 time series |
| DOI | 10.3390/f10070559 |
| 通讯作者 | Wang, Juanle(wangjl@igsnrr.ac.cn) |
| 英文摘要 | Spectral, spatial, and temporal features play important roles in land cover classification. However, limitations still exist in the integrated application of spectral-spatial-temporal (SST) features for forest type discrimination. This paper proposes a forest type classification framework based on SST features and the random forest (RF) algorithm. The SST features were derived from time-series images using original bands, vegetation index, gray-level correlation matrix, and harmonic analysis. Random forest-recursive feature elimination (RF-RFE) was used to optimize high-dimensional and correlated feature space, and determine the optimal SST feature set. Then, the classification was carried out using an RF classifier and the optimized SST feature set. This method was applied in the Qinling Mountains using Sentinel-2 time-series images. A total of 21 SST features were obtained through the RF-RFE method, and their importance was evaluated using the Gini index. The results indicated that spectral features contribute the most to separating shrubs, spatial features are more suitable for discrimination among evergreen forest types, and temporal features are more useful for evergreen forest, deciduous forest, and shrub types. The forest type map was generated based on the optimal SST feature set and RF algorithm, and evaluated based on an agreement with the validation dataset. The results showed that this integrated method is reliable, with an overall accuracy of 86.88% and kappa coe ffi cient of 0.86, and can support forest type sustainable management and mapping at the local scale. |
| WOS关键词 | TIME-SERIES ; SPECIES COMPOSITION ; LANDSAT 8 ; VEGETATION ; COVER ; COOCCURRENCE ; TEXTURE ; NDVI |
| 资助项目 | Chinese Academy of Sciences[XDA19040501] ; Chinese Academy of Sciences[XXH13505-07] ; Construction Project of China Knowledge Center for Engineering Sciences and Technology[CKCEST-2019-3-6] |
| WOS研究方向 | Forestry |
| 语种 | 英语 |
| WOS记录号 | WOS:000482080800059 |
| 出版者 | MDPI |
| 资助机构 | Chinese Academy of Sciences ; Construction Project of China Knowledge Center for Engineering Sciences and Technology |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/68984]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Juanle |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, 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.Shandong Univ Technol, Sch Civil & Architectural Engn, Zibo 255049, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cheng, Kai,Wang, Juanle. Forest Type Classification Based on Integrated Spectral-Spatial-Temporal Features and Random Forest Algorithm-A Case Study in the Qinling Mountains[J]. FORESTS,2019,10(7):18. |
| APA | Cheng, Kai,&Wang, Juanle.(2019).Forest Type Classification Based on Integrated Spectral-Spatial-Temporal Features and Random Forest Algorithm-A Case Study in the Qinling Mountains.FORESTS,10(7),18. |
| MLA | Cheng, Kai,et al."Forest Type Classification Based on Integrated Spectral-Spatial-Temporal Features and Random Forest Algorithm-A Case Study in the Qinling Mountains".FORESTS 10.7(2019):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
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