Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data
Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data
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.
algorithms, ecology, epidemiology, fourier analysis
e1408-[13pp]
Scharlemann, J.P.
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Benz, D.
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Hay, S.I.
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Purse, B.V.
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Tatem, A.J.
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Wint, G.R.
87c2588c-cf55-457f-9534-fa161d0c47ce
Rogers, D.J.
90d6a7d6-f68c-4056-8ac9-3f52ced25f30
9 January 2008
Scharlemann, J.P.
9c9b3244-e738-408b-8c3a-795b5af9973c
Benz, D.
51619703-bd2f-4841-a865-969c038d531f
Hay, S.I.
18d621e0-2813-4c05-b2b7-09df3f24aca7
Purse, B.V.
fb7f7422-5ed3-4caf-9c66-b7196c19260d
Tatem, A.J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Wint, G.R.
87c2588c-cf55-457f-9534-fa161d0c47ce
Rogers, D.J.
90d6a7d6-f68c-4056-8ac9-3f52ced25f30
Scharlemann, J.P., Benz, D., Hay, S.I., Purse, B.V., Tatem, A.J., Wint, G.R. and Rogers, D.J.
(2008)
Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data.
PLoS ONE, 3 (1), .
(doi:10.1371/journal.pone.0001408).
(PMID:18183289)
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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Published date: 9 January 2008
Keywords:
algorithms, ecology, epidemiology, fourier analysis
Organisations:
Geography & Environment, PHEW – S (Spatial analysis and modelling), Population, Health & Wellbeing (PHeW)
Identifiers
Local EPrints ID: 344447
URI: http://eprints.soton.ac.uk/id/eprint/344447
ISSN: 1932-6203
PURE UUID: ce6b9e0b-2d09-499d-8d7f-89c528a6ed44
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Date deposited: 05 Nov 2012 11:30
Last modified: 15 Mar 2024 03:43
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Contributors
Author:
J.P. Scharlemann
Author:
D. Benz
Author:
S.I. Hay
Author:
B.V. Purse
Author:
G.R. Wint
Author:
D.J. Rogers
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