Abstract
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.
Highlights
Land use type contributes to anthropogenic climate change by impacting photosynthetic activity, transpiration, and albedo
The focus of many land use change detection studies has shifted towards detecting change on a per-acquisition rather than a yearly basis, with new methods being developed to exploit these temporally dense time series by using season-trend models to account for intra-year variability [9]
There is a clear difference between Change Detection and Classification (CCDC) and CCDC with Cross Validation (CV), with the latter taking on average more than 1 s longer per simulation
Summary
Land use type contributes to anthropogenic climate change by impacting photosynthetic activity, transpiration, and albedo. The launch of the Landsat 8 mission in 2013 [3] and the Sentinel-2 missions in 2015 and 2017 resulted in an increase in available optical satellite data with 5–16 day temporal resolution Such temporally dense time series provide the opportunity to capture the complex seasonal dynamics of many land cover types and to detect land cover change more rapidly than ever before. The opening of the Landsat archive in 2008 provided access to nearly 40 years’ worth of free historical data [4] Methods such as LandTrendr [5], Composite2Change [6], Vegetation Change Tracker [7], and ShapeSelectForest [8] have been developed to exploit the Landsat data archive to examine long-term vegetation trends. Effectively selecting which methods to use or combine requires knowledge of each respective method’s strengths and weaknesses
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