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Open AccessArticle

On the Performances of Trend and Change-Point Detection Methods for Remote Sensing Data

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Department of Statistics, Computer Science and Mathematics, Public University of Navarre, 31006 Pamplona, Spain
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Institute of Advanced Materials and Mathematics (InaMat2), Public University of Navarre, 31006 Pamplona, Spain
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Department of Mathematics, UNED Pamplona, 31006 Pamplona, Spain
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1008; https://doi.org/10.3390/rs12061008
Received: 11 February 2020 / Revised: 6 March 2020 / Accepted: 17 March 2020 / Published: 21 March 2020
Detecting change-points and trends are common tasks in the analysis of remote sensing data. Over the years, many different methods have been proposed for those purposes, including (modified) Mann–Kendall and Cox–Stuart tests for detecting trends; and Pettitt, Buishand range, Buishand U, standard normal homogeneity (Snh), Meanvar, structure change (Strucchange), breaks for additive season and trend (BFAST), and hierarchical divisive (E.divisive) for detecting change-points. In this paper, we describe a simulation study based on including different artificial, abrupt changes at different time-periods of image time series to assess the performances of such methods. The power of the test, type I error probability, and mean absolute error (MAE) were used as performance criteria, although MAE was only calculated for change-point detection methods. The study reveals that if the magnitude of change (or trend slope) is high, and/or the change does not occur in the first or last time-periods, the methods generally have a high power and a low MAE. However, in the presence of temporal autocorrelation, MAE raises, and the probability of introducing false positives increases noticeably. The modified versions of the Mann–Kendall method for autocorrelated data reduce/moderate its type I error probability, but this reduction comes with an important power diminution. In conclusion, taking a trade-off between the power of the test and type I error probability, we conclude that the original Mann–Kendall test is generally the preferable choice. Although Mann–Kendall is not able to identify the time-period of abrupt changes, it is more reliable than other methods when detecting the existence of such changes. Finally, we look for trend/change-points in land surface temperature (LST), day and night, via monthly MODIS images in Navarre, Spain, from January 2001 to December 2018. View Full-Text
Keywords: land surface temperature; Mann–Kendall test; power of the test; spatio-temporal data; time series; type I error probability land surface temperature; Mann–Kendall test; power of the test; spatio-temporal data; time series; type I error probability
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MDPI and ACS Style

Militino, A.F.; Moradi, M.; Ugarte, M.D. On the Performances of Trend and Change-Point Detection Methods for Remote Sensing Data. Remote Sens. 2020, 12, 1008.

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