Paper
2 October 2008 Satellite remote sensing as a tool for monitoring vegetation seasonality
Brian A. O'Connor, Ned Dwyer, Fiona Cawkwell
Author Affiliations +
Proceedings Volume 7104, Remote Sensing for Agriculture, Ecosystems, and Hydrology X; 71040A (2008) https://doi.org/10.1117/12.799725
Event: SPIE Remote Sensing, 2008, Cardiff, Wales, United Kingdom
Abstract
An increase in average air temperature across the island of Ireland has resulted in a change in the seasonality of vegetation. Current ground-based methods of monitoring seasonality are species-specific and limited to a few point locations across the country. Medium resolution satellite data, e.g. MERIS, provide a means of acquiring multi-year time series of imagery that can be used to capture the spatio-temporal dynamics in vegetation seasonality over the whole island. For this study, a geophysical measure of vegetation growth, the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), derived from MERIS Global Vegetation Index (MGVI) data is being used to determine seasonality. Tiles, extracted from a rectangular global grid, covering the island of Ireland have been processed through the European Space Agency's (ESA) Grid Processing on Demand (GPOD) service. Initial analysis of the imagery has consisted of defining an optimal time composite period in order to minimise cloud effects for daily MGVI values using ancillary cloud data from a meteorological observatory. Methods of in-situ observation of seasonality in mixed woodland have also been explored. Initial findings suggest that a 10-day composite period should be optimal for Ireland given the high occurrence of cloud cover.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Brian A. O'Connor, Ned Dwyer, and Fiona Cawkwell "Satellite remote sensing as a tool for monitoring vegetation seasonality", Proc. SPIE 7104, Remote Sensing for Agriculture, Ecosystems, and Hydrology X, 71040A (2 October 2008); https://doi.org/10.1117/12.799725
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Cited by 4 scholarly publications.
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KEYWORDS
Clouds

Vegetation

Composites

Satellites

Satellite imaging

Near infrared

Remote sensing

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