Journal article icon

Journal article

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

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 multitemporal data sets, mean that these data may be used with greater confidence in species’ distribution modelling.
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Zoology
Research group:
Spatial Ecology and Epidemiology
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Zoology
Research group:
Spatial Ecology and Epidemiology
Role:
Author
More by this author
Institution:
University of Oxford
Research group:
Spatial Ecology and Epidemiology
Department:
Malaria Public Health and Epidemiology Group,Centre for Geographic Medicine,Kenya Medical Research Institute (KEMRI)
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Zoology
Research group:
Spatial Ecology and Epidemiology
Role:
Author
More by this author
Institution:
University of Oxford
Research group:
Spatial Ecology and Epidemiology
Department:
Malaria Public Health and Epidemiology Group,Centre for Geographic Medicine,Kenya Medical Research Institute (KEMRI)
Role:
Author


More from this funder
Funding agency for:
Rogers, D
Grant:
"SE4104", "GOCE-2003-010284 EDEN"
More from this funder
Funding agency for:
Benz, D
Grant:
GOCE-2003-010284 EDEN
More from this funder
Funding agency for:
Hay, S
Grant:
079091
More from this funder
Funding agency for:
Scharlemann, J
Grant:
SE4105
More from this funder
Funding agency for:
Wint, G
Grant:
"SE3229", "GOCE-2003-010284 EDEN"


Publisher:
Public Library of Science
Journal:
PLoS ONE More from this journal
Volume:
3
Issue:
1
Article number:
e1408
Publication date:
2008-01-01
Edition:
Publisher's version
DOI:
EISSN:
1932-6203


Language:
English
Keywords:
Subjects:
UUID:
uuid:4e725ce4-6cfb-40fa-85f1-ba18328be56b
Local pid:
ora:2821
Deposit date:
2009-06-04

Terms of use



Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP