A Near-Real-Time Approach for Monitoring Forest Disturbance Using Landsat Time Series: Stochastic Continuous Change Detection

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Su Ye , John Rogan, Zhe Zhu, J. Ronald Eastman


Forest disturbances greatly affect the ecological functioning of natural forests. Timely information regarding extent, timing and magnitude of forest disturbance events is crucial for effective disturbance management strategies. Yet, we still lack an acute, near-real-time and high-performance remote sensing tools for monitoring abrupt and subtle forest disturbances. This study presents a new approach called ‘Stochastic Continuous Change Detection (S-CCD)’ using a dense Landsat data time series. S-CCD improves upon the ‘COntinuous monitoring of Land Disturbance (COLD)’ approach by incorporating a mathematical tool called the ‘state space model’, which treats trends and seasonality as stochastic processes, allowing for modeling temporal dynamics of satellite observations in a recursive way. The accuracy assessment is evaluated based on 3,782 Landsat-based disturbance reference plots (30 m) from a probability sampling distributed throughout the Conterminous United States. Validation results show that the best F1 score of S-CCD is 0.793 with 20% omission error and 21% commission error, slightly higher than that of COLD (0.789). In addition, two disturbance sites respectively associated with fire and insect disturbances are used for qualitative map-based analysis. Both quantitative and qualitative analysis indicate that S-CCD can achieve noticeably less omission errors than COLD for detecting those disturbances with subtle/gradual spectral change such as insect attack and drought stress. S-CCD enables complete real-time monitoring, and up to ~4.4 times speedup for computation. This research addresses the need for near-real-time monitoring and large-scale mapping of forest health, and offers a new approach for operationally performing change detection tasks from long-term and dense Landsat-based time series.




Earth Sciences, Engineering, Other Earth Sciences, Physical Sciences and Mathematics


time series analysis, Landsat, Forest disturbance, Kalman filter, Near real-time, State space model


Published: 2020-05-12 23:58

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GNU Lesser General Public License (LGPL) 2.1

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