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5 October 2017 Characterisation of macrophyte phenology in the Doñana marshland using MODIS NDVI time series from 2000 to 2015
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The study of the interaction between vegetation development and climate factors is paramount for the management of protected natural areas. Data provided by remote-sensing satellites and derivative products, such as vegetation indices, permit the extraction of basic information regarding the functioning of vegetation masses and their interaction with certain environmental factors. This paper carries out an approach regarding the behaviour of radiation intercepted by aquatic macrophytes present in the Doñana National Park marshland, represented by the plant association Bolboschoenetum maritimi. Based on MODIS NDVI (Normalised Difference Vegetation Index) data, the temporal dynamics of these vegetation masses were studied over a 16-year period (2000–2015), as was their typical annual behaviour, thereby deriving different indicators for seasonal dynamics (NDVI-I, RREL, MAX, MIN, MMAX and MMIN), which help to understand the basic functional characteristics for this type of vegetation. Afterwards, different regression analyses were performed between precipitation and the indicators derived from the NDVI time series. The obtained results indicated that the examined association has a strong dependence on the marshland's flooding processes, requiring a minimum annual precipitation volume (350 mm/year) for proper flooding and vegetation growth development. Furthermore, a strong correlation (r2 =0.70; <;0.05) was found between seasonal nature of the vegetation masses, measured via RREL, and precipitation, as well as slightly weaker relationships between precipitation and other indicators, such as the maximum and minimum annual NDVI (r2 =0.43 and r2 =0.61; p<0.05 and p<0.05, respectively).
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A. Fernandez-Carrillo, V. F. Rodriguez-Galiano, and E. Sanchez-Rodriguez "Characterisation of macrophyte phenology in the Doñana marshland using MODIS NDVI time series from 2000 to 2015", Proc. SPIE 10428, Earth Resources and Environmental Remote Sensing/GIS Applications VIII, 104281O (5 October 2017);

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