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A Multivariate Change Vector Analysis System for Unsupervised Detection of Clear-Cuts in Sentinel-2 Time Series of the Indonesian Forest

We propose a system for detecting clear-cuts in Sentinel-2 (S-2) images of the Indonesian forest by means of an adaptive and unsupervised multivariate Change Vector Analysis (CVA) method. By leveraging on the unique spatial and spectral characteristics of the S- 2 mission, the proposed method characterizes a relevant portion of the target change as lying in a Gaussian neighborhood of the spectral stacked bi-temporal domain of the change. The processing system analyzes all the available bi-temporal pairs in the time series, enabling us to: (1) partially recovering lost information due to cloud coverage, and (2) providing a representation of the change evolving in time. The system is fully automated and potentially operational ready, so it can be used to provide accurate information about clear-cuts at the country scale in Indonesia.