Abstract
BackgroundRemotely-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 FindingsWe 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/SignificanceGlobal 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.
Highlights
Environmental variables, such as temperature and vegetation greenness, are important determinants of the distributions of many species [1]
This assumption affected the estimated values of amplitudes and phases, so that the fitted values did not capture the signal satisfactorily. This would occur if only one year’s worth of MODerate-resolution Imaging Spectroradiometer (MODIS)-type data were analysed by Temporal Fourier analysis (TFA) since the method assumes the time series continues, as measured, forever
The processing chain presented here provides a powerful method for reliably and accurately capturing the synoptic seasonal dynamics of several environmental variables derived from the MODIS sensor
Summary
Environmental variables, such as temperature and vegetation greenness, are important determinants of the distributions of many species [1]. The presence or absence of a species in any area is often distinguished by the absolute levels of climate or vegetation values, and by subtle differences in the seasonality of these variables [2], which can only be captured by repeated measurements over time Such time series may be derived from ground-based meteorological records, but acquiring spatially continuous, global records of these environmental variables is only practical using remotely sensed data from Earth-orbiting satellites. The National Oceanographic and Atmospheric Administration (NOAA) series of satellites carrying the Advanced Very High Resolution Radiometer (AVHRR) have provided time series of global imagery more or less continuously since 1981 [3,4,5] These time series have been used to produce, among others, images of Land Surface Temperature (LST) [6] and of the Normalised Difference Vegetation Index (NDVI), a correlate of vegetation productivity, biomass and climatic conditions [7]. 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
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
