Limits...
Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data.

Scharlemann JP, Benz D, Hay SI, Purse BV, Tatem AJ, Wint GR, Rogers DJ - PLoS ONE (2008)

Bottom Line: MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis.Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics.The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling.

View Article: PubMed Central - PubMed

Affiliation: Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom.

ABSTRACT

Background: Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics.

Methodology/principal findings: We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005.

Conclusions/significance: Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling.

Show MeSH

Related in: MedlinePlus

An example of temporal Fourier analysis.TFA of NDVI from a pixel in the Yorkshire Dales, England (2°W, 54°N) for the years 2001–2005. (a) shows the observed NDVI time series (squares), the resampled cubic spline-fitted data (crosses and line), the five year mean synoptic annual series (thick black line, displaced by −0.1 to ease viewing), and the Fourier fit (grey line), i.e. the sum of the annual, bi-annual and tri-annual harmonics of the TFA. Drop-out values are shown as −0.2. Details of the annual (solid line), bi-annual (dashed) and tri-annual (dotted) harmonics are shown in (b). The sum of these three harmonics is shown in grey for 1.5 years, as in (a). The horizontal line represents the overall mean and the vertical lines indicate the phase (timing) of the first peak of each of the Fourier harmonics in year 2001. The inset magnifies one year.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2171368&req=5

pone-0001408-g005: An example of temporal Fourier analysis.TFA of NDVI from a pixel in the Yorkshire Dales, England (2°W, 54°N) for the years 2001–2005. (a) shows the observed NDVI time series (squares), the resampled cubic spline-fitted data (crosses and line), the five year mean synoptic annual series (thick black line, displaced by −0.1 to ease viewing), and the Fourier fit (grey line), i.e. the sum of the annual, bi-annual and tri-annual harmonics of the TFA. Drop-out values are shown as −0.2. Details of the annual (solid line), bi-annual (dashed) and tri-annual (dotted) harmonics are shown in (b). The sum of these three harmonics is shown in grey for 1.5 years, as in (a). The horizontal line represents the overall mean and the vertical lines indicate the phase (timing) of the first peak of each of the Fourier harmonics in year 2001. The inset magnifies one year.

Mentions: An example of the TFA of an NDVI time series for a single pixel in northern Europe is shown in Figure 5. The Fourier-fitted series, consisting of the summed annual, bi-annual and tri-annual harmonics, provided a good fit to the mean seasonal variation of the observed data (Figure 5a). The annual harmonic dominates the annual cycle of vegetation growth and has a large amplitude, indicating a major change between summer and winter NDVI values (Figure 5b). The second and third harmonics contribute less to the overall fit, but perform the important function of modulating the simple sinusoidal annual cycle. Figure 5b shows that the amplitude and phase of the tri-annual harmonic bring about a flattening and widening of the peak of the annual cycle, and thus improve the fit to the observed signal. The dominance of the annual cycle in Figure 5 is not surprising in a northern temperate habitat. Nearer the equator, and with other vegetation types, the bi-annual and tri-annual harmonics may modulate the annual cycle to a greater extent.


Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data.

Scharlemann JP, Benz D, Hay SI, Purse BV, Tatem AJ, Wint GR, Rogers DJ - PLoS ONE (2008)

An example of temporal Fourier analysis.TFA of NDVI from a pixel in the Yorkshire Dales, England (2°W, 54°N) for the years 2001–2005. (a) shows the observed NDVI time series (squares), the resampled cubic spline-fitted data (crosses and line), the five year mean synoptic annual series (thick black line, displaced by −0.1 to ease viewing), and the Fourier fit (grey line), i.e. the sum of the annual, bi-annual and tri-annual harmonics of the TFA. Drop-out values are shown as −0.2. Details of the annual (solid line), bi-annual (dashed) and tri-annual (dotted) harmonics are shown in (b). The sum of these three harmonics is shown in grey for 1.5 years, as in (a). The horizontal line represents the overall mean and the vertical lines indicate the phase (timing) of the first peak of each of the Fourier harmonics in year 2001. The inset magnifies one year.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2171368&req=5

pone-0001408-g005: An example of temporal Fourier analysis.TFA of NDVI from a pixel in the Yorkshire Dales, England (2°W, 54°N) for the years 2001–2005. (a) shows the observed NDVI time series (squares), the resampled cubic spline-fitted data (crosses and line), the five year mean synoptic annual series (thick black line, displaced by −0.1 to ease viewing), and the Fourier fit (grey line), i.e. the sum of the annual, bi-annual and tri-annual harmonics of the TFA. Drop-out values are shown as −0.2. Details of the annual (solid line), bi-annual (dashed) and tri-annual (dotted) harmonics are shown in (b). The sum of these three harmonics is shown in grey for 1.5 years, as in (a). The horizontal line represents the overall mean and the vertical lines indicate the phase (timing) of the first peak of each of the Fourier harmonics in year 2001. The inset magnifies one year.
Mentions: An example of the TFA of an NDVI time series for a single pixel in northern Europe is shown in Figure 5. The Fourier-fitted series, consisting of the summed annual, bi-annual and tri-annual harmonics, provided a good fit to the mean seasonal variation of the observed data (Figure 5a). The annual harmonic dominates the annual cycle of vegetation growth and has a large amplitude, indicating a major change between summer and winter NDVI values (Figure 5b). The second and third harmonics contribute less to the overall fit, but perform the important function of modulating the simple sinusoidal annual cycle. Figure 5b shows that the amplitude and phase of the tri-annual harmonic bring about a flattening and widening of the peak of the annual cycle, and thus improve the fit to the observed signal. The dominance of the annual cycle in Figure 5 is not surprising in a northern temperate habitat. Nearer the equator, and with other vegetation types, the bi-annual and tri-annual harmonics may modulate the annual cycle to a greater extent.

Bottom Line: MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis.Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics.The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling.

View Article: PubMed Central - PubMed

Affiliation: Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom.

ABSTRACT

Background: Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics.

Methodology/principal findings: We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005.

Conclusions/significance: Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling.

Show MeSH
Related in: MedlinePlus