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Trend, seasonality, and abrupt change detection method for land surface temperature time-series analysis: Evaluation and improvement

文献类型：期刊论文

作者Li, Jing1,2,3; Li, Zhao-Liang2; Wu, Hua1,3; You, Nanshan1,3
刊名REMOTE SENSING OF ENVIRONMENT
出版日期2022-10-01
卷号280页码:20
ISSN号0034-4257
关键词Change detection methods Time -series analysis Land surface temperature Evaluation
DOI10.1016/j.rse.2022.113222
英文摘要Long-term land surface temperature (LST) variation is vital for the study of climate change and environmental monitoring. Change detection methods provide access to recovery trajectories of trend and seasonality and detect abrupt changes in LST time series, but a comprehensive evaluation of the published methods is lacking. In this study, simulated LST data with a temporal resolution of 8 days under different scenarios were used to evaluate the performance of three commonly used methods: Detecting Breakpoints and Estimating Segments in Trend (DBEST), Breaks for Additive Seasonal and Trend (BFAST), and Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST). The results obtained using the simulated data indicated that BEAST was the best method for decomposing LST time series into trend and seasonality (mean RMSEs were 0.28 K and 0.27 K, respectively) and for detecting abrupt changes in these two components (mean F1 scores were 0.83 and 0.95, respectively). BFAST was less robust to high-complexity data (F1: 0.56 and 0.52, RMSE: 1.34 K and 1.46 K). 0.91 K and 1.29 K). DBEST is recommended to capture component details because it yields the least generalized output (F1 for trend: 0.37, RMSE: 0.64 K and 1.37 K). Both BFAST and DBEST exhibited reduced accuracy when the time-series data has long-lasting continuous missing data. An application using the 20-year MODIS LST time series supports the results obtained using the simulated data. BEAST exhibited the highest detection accuracy for land cover change (13 correct detections among 15 true changes), followed by DBEST (9) and BFAST (7). All three methods were ineffective for detecting low-magnitude disturbances: wildfires, heatwaves, and cold spells due to their low intensity or short duration. To reduce the non-negligible commission error of BEAST, this study proposes an improved BEAST, which eliminates the false breakpoints in BEAST using a set of thresholds. Compared with BEAST, the user accuracy of the improved BEAST was significantly increased by 13.9% in the simulated data, resulting in an F1 increase of 0.04, and 15 false breakpoints were eliminated among 53 detected disturbances in the MODIS LST time series. This study outlines commonly used change detection methods and offers guidance for choosing the optimal method to detect changes in LST time series. Furthermore, suggestions on the determination of parameters and false breakpoints elimination in the improved BEAST enable it more practical.
WOS关键词CLASSIFICATION ; COMPONENT ; PATTERNS ; AREAS ; CYCLE ; LST
资助项目National Natural Science Foundation of China[41921001] ; National Natural Science Foundation of China[41871267] ; National Natural Science Foundation of China[42071331] ; China Scholar Council (CSC)
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者ELSEVIER SCIENCE INC
WOS记录号WOS:000863323900001
资助机构National Natural Science Foundation of China ; China Scholar Council (CSC)
源URL[http://ir.igsnrr.ac.cn/handle/311030/185720]  
专题资源与环境信息系统国家重点实验室_外文论文
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Li, Jing,Li, Zhao-Liang,Wu, Hua,et al. Trend, seasonality, and abrupt change detection method for land surface temperature time-series analysis: Evaluation and improvement[J]. REMOTE SENSING OF ENVIRONMENT,2022,280:20.
APA Li, Jing,Li, Zhao-Liang,Wu, Hua,&You, Nanshan.(2022).Trend, seasonality, and abrupt change detection method for land surface temperature time-series analysis: Evaluation and improvement.REMOTE SENSING OF ENVIRONMENT,280,20.
MLA Li, Jing,et al."Trend, seasonality, and abrupt change detection method for land surface temperature time-series analysis: Evaluation and improvement".REMOTE SENSING OF ENVIRONMENT 280(2022):20.

入库方式： OAI收割

来源：地理科学与资源研究所

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