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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.

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Comparisons between actual and calculated annual amplitudes (a,b) and phases (c,d) for artificially generated MODIS data.Amplitudes and phases were either calculated by standard temporal Fourier analysis (TFA) using the TFA module in IDRISI Andes (a,c) or by using the TFA algorithm described here (b,d). Lines represent least-squares regression slopes with the following equations: (a) 0.00393+0.99928x, F1,9898  = 1.159e+07, R2 = 0.9991; (b) −9.398e-06+1.0x, F1,9898 = 2.367e+10, R2 = 1.0; and (d) 7.140e-05+1.0x, F1,9898 = 38.9e+10, R2 = 1.0, all highly statistically significant (P<0.001). IDRISI Andes provides phase estimates in radians which are here re-expressed in terms comparable to the input data. Because the points in the upper left part of (c) strongly influence regression calculations, no regression was fitted to these data. Instead, the line of equality (x = y) is shown in (c) to aid visual comparison of the results.
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pone-0001408-g004: Comparisons between actual and calculated annual amplitudes (a,b) and phases (c,d) for artificially generated MODIS data.Amplitudes and phases were either calculated by standard temporal Fourier analysis (TFA) using the TFA module in IDRISI Andes (a,c) or by using the TFA algorithm described here (b,d). Lines represent least-squares regression slopes with the following equations: (a) 0.00393+0.99928x, F1,9898  = 1.159e+07, R2 = 0.9991; (b) −9.398e-06+1.0x, F1,9898 = 2.367e+10, R2 = 1.0; and (d) 7.140e-05+1.0x, F1,9898 = 38.9e+10, R2 = 1.0, all highly statistically significant (P<0.001). IDRISI Andes provides phase estimates in radians which are here re-expressed in terms comparable to the input data. Because the points in the upper left part of (c) strongly influence regression calculations, no regression was fitted to these data. Instead, the line of equality (x = y) is shown in (c) to aid visual comparison of the results.

Mentions: Figure 4 highlights errors in the calculation of both annual amplitudes (Figure 4a) and annual phases (Figure 4b), found when many artificially generated MODIS time series, such as that shown in Figure 3, were subjected to standard TFA and the input amplitudes and phases compared with the TFA-calculated amplitudes and phases. These errors, both positive and negative, appeared to be approximately constant in their absolute values across the range of input values used, with the consequence that the proportional errors were very much greater at smaller input values of amplitude and phase. Thus Fig. 3a shows that using standard TFA, the annual amplitude may be estimated with an error of as little as about ±2% at high amplitude values, but as high as ±20% at low values. The equivalent values for phase are ±3% and ±30%, respectively.


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)

Comparisons between actual and calculated annual amplitudes (a,b) and phases (c,d) for artificially generated MODIS data.Amplitudes and phases were either calculated by standard temporal Fourier analysis (TFA) using the TFA module in IDRISI Andes (a,c) or by using the TFA algorithm described here (b,d). Lines represent least-squares regression slopes with the following equations: (a) 0.00393+0.99928x, F1,9898  = 1.159e+07, R2 = 0.9991; (b) −9.398e-06+1.0x, F1,9898 = 2.367e+10, R2 = 1.0; and (d) 7.140e-05+1.0x, F1,9898 = 38.9e+10, R2 = 1.0, all highly statistically significant (P<0.001). IDRISI Andes provides phase estimates in radians which are here re-expressed in terms comparable to the input data. Because the points in the upper left part of (c) strongly influence regression calculations, no regression was fitted to these data. Instead, the line of equality (x = y) is shown in (c) to aid visual comparison of the results.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0001408-g004: Comparisons between actual and calculated annual amplitudes (a,b) and phases (c,d) for artificially generated MODIS data.Amplitudes and phases were either calculated by standard temporal Fourier analysis (TFA) using the TFA module in IDRISI Andes (a,c) or by using the TFA algorithm described here (b,d). Lines represent least-squares regression slopes with the following equations: (a) 0.00393+0.99928x, F1,9898  = 1.159e+07, R2 = 0.9991; (b) −9.398e-06+1.0x, F1,9898 = 2.367e+10, R2 = 1.0; and (d) 7.140e-05+1.0x, F1,9898 = 38.9e+10, R2 = 1.0, all highly statistically significant (P<0.001). IDRISI Andes provides phase estimates in radians which are here re-expressed in terms comparable to the input data. Because the points in the upper left part of (c) strongly influence regression calculations, no regression was fitted to these data. Instead, the line of equality (x = y) is shown in (c) to aid visual comparison of the results.
Mentions: Figure 4 highlights errors in the calculation of both annual amplitudes (Figure 4a) and annual phases (Figure 4b), found when many artificially generated MODIS time series, such as that shown in Figure 3, were subjected to standard TFA and the input amplitudes and phases compared with the TFA-calculated amplitudes and phases. These errors, both positive and negative, appeared to be approximately constant in their absolute values across the range of input values used, with the consequence that the proportional errors were very much greater at smaller input values of amplitude and phase. Thus Fig. 3a shows that using standard TFA, the annual amplitude may be estimated with an error of as little as about ±2% at high amplitude values, but as high as ±20% at low values. The equivalent values for phase are ±3% and ±30%, respectively.

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