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Original Articles

Monitoring and modelling land surface dynamics in Bermejo River Basin, Argentina: time series analysis of MODIS NDVI data

, , , , &
Pages 5429-5451 | Received 19 Jun 2012, Accepted 21 Mar 2013, Published online: 24 Apr 2013
 

Abstract

The purpose of this work was to monitor and model land surface phenology over the past ten years in the South American Bermejo River basin using the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) product. In order to do this, we evaluated the characteristics of the satellite data and information available on the study area's ecosystem to choose the best model to capture the temporal dynamics of NDVI in local vegetation (sufficiently complex to provide a good fit and simple enough so that each parameter has an immediate ecological meaning). An ecological interpretation of model parameters was provided. Different land surfaces showed distinct fluctuations over time in NDVI values, and this information was used to improve object-oriented classification. A decision tree classification was developed to identify spatial patterns of NDVI functional form and the fluctuations that these patterns presented from 2000 to 2010. We integrated inter-annual information in a final map that distinguishes stable areas from changing sites. Assuming that large inter-annual spatial-scale fluctuations were related to climatic events, we established how vegetated land surfaces within the study area responded to these. Our study was designed to emphasize the interpretation of the spatial and temporal scales of land surface phenology.

Acknowledgements

The authors wish to give special thanks to members of Unidad de Manejo del Sistema Forestal Nacional (UMSEF), Comisión Regional del Río Bermejo (COREBE) and La Administracion Provincial del Agua (APA) for their interaction and sharing of data. This work was developed within the framework of the SACD Aquarius AO ‘La Plata Basin floods and droughts: Contribution of microwave remote sensing in monitoring and prediction’ (MyNCIT-CONAE-CONICET project 12). The authors also wish to thank the anonymous reviewers for their valuable suggestions that significantly improved the paper.

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