sta-package: Statistical Trend Analysis (STA) for Time Series of Satellite...

In sta: Seasonal Trend Analysis for Time Series Imagery in R

Statistical Trend Analysis (STA) for Time Series of Satellite Imagery

Description

STA applies the Mann-Kendall test for trend to the so-called shape parameters of periodic time series. STA estimates shape parameters via harmonic regression. STA can handle numeric time series and RasterStack of satellite images.

Details

Shape parameters is the term used in vegetation monitoring to refer to the amplitudes and phase angles resulting from fitting a harmonic regression model to time series of vegetation indices derived from satellite images. Regardless of its origin, STA can be applied to any periodic time series which makes this package potentially useful to other disciplines such as hydrology, climatology and econometrics.

With sta (the main function of this package) it is possible to perform the Mann-Kendall test for trend on time series of the three most commonly used shape parameters: mean, annual and semiannual. These parameters are the estimated amplitude coefficients of the aforementioned harmonic regresion model. This function allows parallel processing to handle large satellite time series imagery.

STA includes the following graphical methods:

• plot.staNumeric: generic plot displaying sta's output for numeric time series.

• plot.staMatrix: maps of mapview-class displaying sta's output for RasterStack.

STA include the following datasets:

• marismas: numeric vector containing 10-day Composite NDMI values from 2000 to 2018.

• ndmi: RasterStack containing 612 spatial subsets of 10-day Composite NDMI images acquired from 2001 to 2017.

Author(s)

Tecuapetla-Gómez, I. [e-mail]

References

Eastman, R., Sangermano, F., Ghimire, B., Zhu, H., Chen, H., Neeti, N., Cai, Y., Machado, E., Crema, S. (2009). Seasonal trend analysis of image time series, International Journal of Remote Sensing 30(10), 2721–2726.

sta documentation built on May 30, 2022, 9:08 a.m.