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Online Forest Disturbance Detection at the Sub-Annual Scale Using Spatial Context From Sparse Landsat Time Series
Wu, Ling1; Liu, Xiangnan1; Liu, Meiling1; Yang, Jinghui1; Zhu, Lihong1; Zhou, Botian2
2022
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.
关键词Disturbance detection exponentially weighted moving average t chart (EWMA-t chart) sparse Landsat time series spatial context
DOI10.1109/TGRS.2022.3145675
发表期刊IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
卷号60页码:14
通讯作者Liu, Xiangnan(liuxn@cugb.edu.cn)
收录类别SCI
WOS记录号WOS:000770659900022
语种英语