Online Forest Disturbance Detection at the Sub-Annual Scale Using Spatial Context From Sparse Landsat Time Series
文献类型：期刊论文
| 作者 | Wu, Ling2; Liu, Xiangnan2; Liu, Meiling2; Yang, Jinghui2; Zhu, Lihong2; Zhou, Botian1
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| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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| 出版日期 | 2022 |
| 卷号 | 60页码:14 |
| ISSN号 | 0196-2892 |
| 关键词 | Disturbance detection exponentially weighted moving average t chart (EWMA-t chart) sparse Landsat time series spatial context |
| DOI | 10.1109/TGRS.2022.3145675 |
| 通讯作者 | Liu, Xiangnan(liuxn@cugb.edu.cn) |
| 英文摘要 | Mapping forest disturbances using dense time series can timely identify disturbances at the subannual scale. However, these change detection methods using dense time series may be infeasible when not enough temporal observations are available. In this article, an online change detection algorithm that identifies forest disturbances at a subannual scale using spatial context from the sparse Landsat time series was proposed. First, the spatial normalized index that removed forest seasonality was prepared for establishing a simplified model instead of the harmonic model, thereby reducing the requirements for a high temporal frequency of clear observations for model initialization. Second, by using the spatial errors model to establish the simplified model, the normally distributed residual time series that removed the spatial autocorrelation were obtained. Third, the spatial statistic t time series transformed from residual time series within a 3 x 3 spatial window were subsequently subjected to the exponentially weighted moving average t chart (EWMA-t), which is a statistical process control chart for a short cycle corresponding to sparse Landsat time series. Fourth, disturbed pixels were labeled if the chart values persistently deviated from the control limits of the chart. The proposed algorithm was applied to a subtropical forest with low Landsat data availability and yielded an overall accuracy of 86% in the spatial domain and temporal accuracy of 93.7%, achieving accurate and timely identification of forest disturbances. The proposed method called the EWMA-t change detection (EWMATCD) algorithm provides an alternative for disturbance detection at the subannual scale in regions with low data availability. |
| 资助项目 | National Natural Science Foundation of China[41871223] ; National Natural Science Foundation of China[62001434] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000770659900022 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/15621]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Liu, Xiangnan |
| 作者单位 | 1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China 2.China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Ling,Liu, Xiangnan,Liu, Meiling,et al. Online Forest Disturbance Detection at the Sub-Annual Scale Using Spatial Context From Sparse Landsat Time Series[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:14. |
| APA | Wu, Ling,Liu, Xiangnan,Liu, Meiling,Yang, Jinghui,Zhu, Lihong,&Zhou, Botian.(2022).Online Forest Disturbance Detection at the Sub-Annual Scale Using Spatial Context From Sparse Landsat Time Series.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,14. |
| MLA | Wu, Ling,et al."Online Forest Disturbance Detection at the Sub-Annual Scale Using Spatial Context From Sparse Landsat Time Series".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):14. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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