modelValues: NDVI modelling

Description

Models NDVI values.

Usage

modelValues(x, method, ...)

Arguments

An object of class ‘NDVI’ containing raw and/or corrected NDVI values.

method

Determines which model will be fitted to the corrected NDVI-timeserie (if the corrected timeserie is not available, the raw one out of ‘values’ will be used).

“LinIP”: A linear interpolation is performed. For interpolation, the end of timeserie is connected to the beginning (e.g. after day 365 follows day 1). Applied in Badeck et. al (2004) and Doktor et. al (2009).

“Spline”: A spline interpolation is performed. For interpolation, the end of timeserie is connected to the beginning (e.g. after day 365 follows day 1).

“DSig”: Fits a double sigmoidal function to NDVI values (according to Zhang et. al (2003)).

“DSigC”: Fits another double sigmoidal function (own C implementation) to NDVI values.

“DLogistic”: Fits a double logistic function after Fischer, Alberte (1994) to NDVI values.

“Gauss”: Fits a symmetric or asymmetric (determined by boolean parameter ‘asym’) gaussian function (own C implementation after Press, W.H. (1992)) to NDVI values.

“Growth”: Fits a growth model after Richter et. al (1991) to NDVI values.

“FFT”: Smoothes the corrected or raw NDVI values with fast fourier transfusion (implemented in R). The smoothing intensity can be controlled with parameter ‘filter.threshold’ with default to 3.

“SavGol”: Smoothes the corrected or raw NDVI values with a Savitzky-Golay filter (own C implementation after Press, W.H. (1992)). The smoothing algorithm can be modified with parameters ‘window’ (window size of filter, default to 7), ‘degree’ (degree of fitting polynomial, default to 2) and ‘smoothing’ (repetition quantity, default to 10).

...

Other parameters passed to modelling function. ‘asym’ for method “Gauss”, ‘filter.threshold’ for method “FFT” and ‘degree’, ‘window’ and ‘smoothing’ for method “SavGol”.

Details

Returns an object of type ‘NDVI’ containing raw data and/or corrected NDVI values and modelled NDVI values.

References

Badeck, F.W., Bondeau, A., Boettcher, K., Doktor, D., Lucht, W., Schaber, J. and Sitch, S. (2004). Responses of spring phenology to climate change. New Phytologist, 162, 295-309.

Doktor, D., Bondeau, A., Koslowski, D. and Badeck, F.W. (2009). Influence of heterogeneous landscapes on computed green-up dates based on daily AVHRR NDVI observations. Remote Sensing of Environment, 113, 2618-2632

Fischer, Alberte (1994). A Model for the Seasonal Variations of Vegetation Indices in Coarse Resolution Data and Its Inversion to Extract Crop Parameters. Remote Sensing of Environment, 48, 220-230.

Press, W.H. (1992). Numerical recipes in C: The Art of Scientific Computing, vol. 1. Cambridge University Press, Cambridge, 2nd edn.

Richter, O., Spickermann, U. and Lenz, F. (1991). A new model for plant-growth. Gartenbauwissenschaft, 56, 99-106.

Viovy, N., Arino, O. and Belward, A.S. (1992). The Best Index Slope Extraction (BISE) - a method for reducing noise in NDVI time-series. International Journal of Remote Sensing, 13, 1585-1590.

Zhang, X.Y., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C. and Huete, A. (2003). Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84, 471-475.

Examples

Run this code

	# load data
	data(avhrr)

	# create NDVI object
	ndvi <- new("NDVI", values=avhrr.ndvi/10000, year=as.integer(1995))

	# correct values (bise)
	ndvi <- bise(ndvi, slidingperiod=40)

	#model values
	ndvi <- modelValues(ndvi, method="LinIP")

	# plot
	plot(ndvi)