Compared with one single image, satellite image time series (SITS) can capture the dynamic changes in land cover types, thus achieving a more comprehensive and accurate land cover classification map. Due to decades of data acquisition and new high temporal resolution sensors, SITS is becoming more available. Corresponding SITS analysis techniques need to be further developed. Most satellite images are multispectral, namely, multivariate. However, multivariate time series analysis techniques are less mature compared with univariate time series. There seems to be a lack of a robust and accurate similarity measure between multivariate time series for SITS clustering. In this paper, we propose a novel method to transform multivariate SITS into univariate SITS while the useful information is kept as much as possible. And then advanced univariate time series similarity measures can be adopted to achieve SITS clustering. The proposed method is tested on Landsat-TM SITS dataset and shows a better clustering result than ordinary multivariate time series similarity measure. In addition, the overall computing time may be reduced due to dimension reduction.
Land cover disturbance is an abrupt ecosystem change that occurs over a short time period, such as flood, fire, drought
and deforestation. It is crucial to monitor disturbances for rapid response. In this paper, we propose a time series analysis
method for monitoring of land-cover disturbance with high confidence level. The method integrates procedures including
(1) modeling of a piece of history time series data with season-trend model and (2) forecasting with the fitted model and
monitoring disturbances based on significance of prediction errors. The method is tested using 16-day MODIS NDVI
time series to monitor abnormally inundated areas of the Tongjiang section of Heilongjiang River of China, where had
extreme floods and bank break in summer 2013. The test results show that the method could detect the time and areas of
disturbances for each image with no detection delay and with high specified confidence level. The method has few
parameters to be specified and less computation complexity so that it could be developed for monitoring of land-cover
disturbance on large scales.
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