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Influence of land-use types and climatic variables on seasonal patterns of NDVI in Mediterranean Iberian ecosystems
Authors:P Durante  C Oyonarte  F Valladares
Institution:1. Departamento de Edafología, CITE II-B, Universidad Almería, 04120 Almería, Spain

E-mail coyonart@ual.es;2. Instituto de Recursos Naturales, Centro de Ciencias Medioambientales, CSIC, Serrano 115, 28006 Madrid, Spain

Departamento de Biología y Geología, Escuela Superior de Ciencias Experimentales y Tecnológicas, Universidad Rey Juan Carlos, c/Tulipán, 28933 Móstoles, Spain

E-mail valladares@ccma.csic.es

Abstract:Question: What is the influence of management on the functioning of vegetation over time in Mediterranean ecosystems under different climate conditions? Location: Mediterranean shrublands and forests in SE Iberia (Andalusia). Methods: We evaluated the Normalized Difference Vegetation Index (NDVI) for the 1997-2002 time series to determine phenological vegetation patterns under different historical management regimes. Three altitudinal ranges were considered within each area to explore climate × management interactions. Each phenological pattern was analysed using time series statistics, together with precipitation (monthly and cumulative) and temperature. Results: NDVI time series were significantly different under different management regimes, particularly in highly transformed areas, which showed the lowest NDVI, weakest annual seasonality and a more immediate phenological response to precipitation. The NDVI relationship with precipitation was strongest in the summer-autumn period, when precipitation is the main plant growth-limiting factor. Conclusions: NDVI time series analyses elucidated complex influences of land use and climate on ecosystem functioning in these Mediterranean ecosystems. We demonstrated that NDVI time series analyses are a useful tool for monitoring programmes because of their sensitivity to changes, ease of use and applicability to large-scale studies.
Keywords:Autocorrelation  Cross-correlation  Human influence  Time series
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