Ecosystem dynamics and ecological disturbances manifest as breakpoints in long-term multispectral remote sensing time series. Typically, these breakpoints are captured using univariate methods applied individually to each band, with subsequent integration of the results. However, multivariate analysis provides a promising way to fully incorporate the multispectral bands into breakpoints detection methods, but it has been rarely applied in monitoring ecosystem dynamics and detecting ecological disturbances. In this research, we developed a multivariate algorithm, named breakpoints-Detection algoRithm using MultivAriate Time series (DRMAT). DRMAT can fully use multispectral bands simultaneously with the consideration of the inter-correlation among bands. It decomposes a multivariate time series into trend, seasonality, and noise, iteratively segmenting the detrended/ de-seasonalized signals. We quantitatively evaluated DRMAT using both simulated multivariate data and randomly sampled real-world data, including subtle land cover changes caused by forest disturbances (depletions) and recovery (return of vegetation), as well as subtle changes over a broad range of land cover types. We also qualitatively assessed DRMAT in mapping real-world disturbances. For simulated data with prescribed breakpoints in both trend and seasonality, DRMAT detected breakpoints in trend with an F1 score of 85.5 % and in seasonality with an F1 score of 91.7 %. For real-world data in forested land cover, DRMAT unveiled both disturbances and subsequent recovery with an F1 score of 95.1 % for disturbances and 77.1 % for recovery. It detected disturbances in broader land cover types with an F1 score of 84.0 %. We demonstrated that using allband data was more accurate than using selected bands in breakpoint detection. The inclusion of vegetation indices as model inputs did not improve accuracy unless the original input bands lacked the specific band information in the vegetation indices. As a multivariate approach, DRMAT leverages the full information in the multispectral data and avoids the necessity of integrating results derived from individual bands.
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