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Article

An Evaluation and Comparison of Four Dense Time Series Change Detection Methods Using Simulated Data

1
Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth SY23 3DB, UK
2
Environment Systems Ltd., 9 Cefn Llan Science Park, Aberystwyth SY23 3AH, UK
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Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2779; https://doi.org/10.3390/rs11232779
Received: 22 October 2019 / Revised: 19 November 2019 / Accepted: 22 November 2019 / Published: 25 November 2019
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Access to temporally dense time series such as data from the Landsat and Sentinel-2 missions has lead to an increase in methods which aim to monitor land cover change on a per-acquisition rather than a yearly basis. Evaluating the accuracy and limitations of these methods can be difficult because validation data are limited and often rely on human interpretation. Simulated time series offer an objective method for evaluating and comparing between change detection algorithms. A set of simulated time series was used to evaluate four change detection methods: (1) Breaks for Additive and Seasonal Trend (BFAST); (2) BFAST Monitor; (3) Continuous Change Detection and Classification (CCDC); and (4) Exponentially Weighted Moving Average Change Detection (EWMACD). In total, 151,200 simulations were generated to represent a range of abrupt, gradual, and seasonal changes. EWMACD was found to give the best performance overall, correctly identifying the true date of change in 76.6% of cases. CCDC performed worst (51.8%). BFAST performed well overall but correctly identified less than 10% of seasonal changes (changes in amplitude, length of season, or number of seasons). All methods showed some decrease in performance with increased noise and missing data, apart from BFAST Monitor which improved when data were removed. The following recommendations are made as a starting point for future studies: EWMACD should be used for detection of lower magnitude changes and changes in seasonality; CCDC should be used for robust detection of complete land cover class changes; EWMACD and BFAST are suitable for noisy datasets, depending on the application; and CCDC should be used where there are high quantities of missing data. The simulated datasets have been made freely available online as a foundation for future work. View Full-Text
Keywords: change detection; time series; BFAST; BFAST monitor; CCDC; EWMACD; simulation; NDVI change detection; time series; BFAST; BFAST monitor; CCDC; EWMACD; simulation; NDVI
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MDPI and ACS Style

Awty-Carroll, K.; Bunting, P.; Hardy, A.; Bell, G. An Evaluation and Comparison of Four Dense Time Series Change Detection Methods Using Simulated Data. Remote Sens. 2019, 11, 2779. https://doi.org/10.3390/rs11232779

AMA Style

Awty-Carroll K, Bunting P, Hardy A, Bell G. An Evaluation and Comparison of Four Dense Time Series Change Detection Methods Using Simulated Data. Remote Sensing. 2019; 11(23):2779. https://doi.org/10.3390/rs11232779

Chicago/Turabian Style

Awty-Carroll, Katie, Pete Bunting, Andy Hardy, and Gemma Bell. 2019. "An Evaluation and Comparison of Four Dense Time Series Change Detection Methods Using Simulated Data" Remote Sensing 11, no. 23: 2779. https://doi.org/10.3390/rs11232779

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