Demystifying LandTrendr and CCDC temporal segmentation
Valerie J. Pasquarella, Paulo Arévalo, Kelsee H. Bratley, Eric L. Bullock, Noel Gorelick, Zhiqiang Yang, Robert E. Kennedy
Abstract
Improved access to remotely sensed imagery and time series algorithms in combination with increased availability of cloud computing resources and platforms such as Google Earth Engine have significantly expanded the community of users processing and analyzing time series of satellite observations. Though individual time series analysis methods and their applications tend to be well-documented, comparisons of different approaches are beneficial to new users faced with the choice of different algorithms and parameterizations. We review two temporal segmentation approaches that have become increasingly prevalent in land cover mapping and monitoring: LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) and CCDC (Continuous Change Detection and Classification). We examine differences in the way these approaches use the temporal and spectral domains and compare model specifications and outputs. This review highlights previous work and applications, current limitations, ongoing challenges, and opportunities for future integration and comparison of methods and map products, and is expected to benefit both user and developer communities.