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Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data

Author

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  • Jörn P W Scharlemann
  • David Benz
  • Simon I Hay
  • Bethan V Purse
  • Andrew J Tatem
  • G R William Wint
  • David J Rogers

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.

Suggested Citation

  • Jörn P W Scharlemann & David Benz & Simon I Hay & Bethan V Purse & Andrew J Tatem & G R William Wint & David J Rogers, 2008. "Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data," PLOS ONE, Public Library of Science, vol. 3(1), pages 1-13, January.
    • Handle: RePEc:plo:pone00:0001408
    DOI: 10.1371/journal.pone.0001408
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    References listed on IDEAS

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    1. David J. Rogers & Sarah E. Randolph & Robert W. Snow & Simon I. Hay, 2002. "Satellite imagery in the study and forecast of malaria," Nature, Nature, vol. 415(6872), pages 710-715, February.
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    2. Nicholas A S Hamm & Ricardo J Soares Magalhães & Archie C A Clements, 2015. "Earth Observation, Spatial Data Quality, and Neglected Tropical Diseases," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(12), pages 1-24, December.
    3. Yangyi Chen & Wenfeng Zhan & Zihan Liu & Pan Dong & Huyan Fu & Shiqi Miao & Yingying Ji & Lu Jiang & Sida Jiang, 2023. "Combining Spatiotemporally Global and Local Interpolations Improves Modeling of Annual Land Surface Temperature Cycles," Land, MDPI, vol. 12(2), pages 1-25, January.
    4. Jan C. Semenza, 2015. "Prototype Early Warning Systems for Vector-Borne Diseases in Europe," IJERPH, MDPI, vol. 12(6), pages 1-19, June.
    5. Tomislav Hengl & Jorge Mendes de Jesus & Robert A MacMillan & Niels H Batjes & Gerard B M Heuvelink & Eloi Ribeiro & Alessandro Samuel-Rosa & Bas Kempen & Johan G B Leenaars & Markus G Walsh & Maria R, 2014. "SoilGrids1km — Global Soil Information Based on Automated Mapping," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-17, August.
    6. Blum, Moshe & Nestel, David & Cohen, Yafit & Goldshtein, Eitan & Helman, David & Lensky, Itamar M., 2018. "Predicting Heliothis (Helicoverpa armigera) pest population dynamics with an age-structured insect population model driven by satellite data," Ecological Modelling, Elsevier, vol. 369(C), pages 1-12.
    7. David Fisman & Eleni Patrozou & Yehuda Carmeli & Eli Perencevich & Ashleigh R Tuite & Leonard A Mermel & the Geographical Variability of Bacteremia Study Group, 2014. "Geographical Variability in the Likelihood of Bloodstream Infections Due to Gram-Negative Bacteria: Correlation with Proximity to the Equator and Health Care Expenditure," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-18, December.

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