气候变化领域集成服务门户(CCPortal): A near-real-time approach for monitoring forest disturbance using Landsat time series: stochastic continuous change detection

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DOI	10.1016/j.rse.2020.112167
	A near-real-time approach for monitoring forest disturbance using Landsat time series: stochastic continuous change detection
	Ye S.; Rogan J.; Zhu Z.; Eastman J.R.
发表日期	2021
ISSN	00344257
卷号	252
英文摘要	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 accurate, 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 quantitative accuracy assessment is evaluated based on 3782 Landsat-based disturbance reference plots (30 m) from a probability sampling distributed throughout the Conterminous United States. Validation results show that the overall accuracy (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). Two disturbance sites respectively associated with wildfire and insect disturbances are used for qualitative map-based analysis. Both quantitative and qualitative analyses suggest that S-CCD achieves fewer omission errors than COLD for detecting those disturbances with subtle/gradual spectral change. In addition, S-CCD facilitates a better real-time monitoring, benefited by its complete recursive manner and a shorter lag for confirming disturbance than COLD (126 days vs. 166 days for alerting 50% disturbance events), and reached 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 dense Landsat-based time series. © 2020 The Authors
英文关键词	Forest disturbance; Kalman filter; Landsat; Near real-time; State space model; Time series analysis
语种	英语
scopus关键词	Errors; Forestry; Random processes; Remote sensing; State space methods; Stochastic models; Time series; Continuous monitoring; Disturbance managements; Ecological functioning; Near-real-time monitoring; Quantitative accuracy; Quantitative and qualitative analysis; Satellite observations; State - space models; Stochastic systems; accuracy assessment; environmental disturbance; error analysis; forest health; Landsat; naturalness; seasonality; stochasticity; vegetation cover; wildfire; United States; Hexapoda
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179054
作者单位	Graduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01610, United States; Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, United States; Clark Labs, Clark University, Worcester, MA 01610, United States
推荐引用方式 GB/T 7714	Ye S.,Rogan J.,Zhu Z.,et al. A near-real-time approach for monitoring forest disturbance using Landsat time series: stochastic continuous change detection[J],2021,252.
APA	Ye S.,Rogan J.,Zhu Z.,&Eastman J.R..(2021).A near-real-time approach for monitoring forest disturbance using Landsat time series: stochastic continuous change detection.Remote Sensing of Environment,252.
MLA	Ye S.,et al."A near-real-time approach for monitoring forest disturbance using Landsat time series: stochastic continuous change detection".Remote Sensing of Environment 252(2021).