Abstract

This study evaluates the ability to track grassland growth phenology in the Swiss Alps with NOAA-16 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time series. Three growth parameters from 15 alpine and subalpine grassland sites were investigated between 2001 and 2005: Melt-Out (MO), Start Of Growth (SOG), and End Of Growth (EOG).We tried to estimate these phenological dates from yearly NDVI time series by identifying dates, where certain fractions (thresholds) of the maximum annual NDVI amplitude were crossed for the first time. For this purpose, the NDVI time series were smoothed using two commonly used approaches (Fourier adjustment or alternatively Savitzky-Golay filtering). Moreover, AVHRR NDVI time series were compared against data from the newer generation sensors SPOT VEGETATION and TERRA MODIS. All remote sensing NDVI time series were highly correlated with single point ground measurements and therefore accurately represented growth dynamics of alpine grassland. The newer generation sensors VGT and MODIS performed better than AVHRR, however, differences were minor. Thresholds for the determination of MO, SOG, and EOG were similar across sensors and smoothing methods, which demonstrated the robustness of the results. For our purpose, the Fourier adjustment algorithm created better NDVI time series than the Savitzky-Golay filter, since latter appeared to be more sensitive to noisy NDVI time series. Findings show that the application of various thresholds to NDVI time series allows the observation of the temporal progression of vegetation growth at the selected sites with high consistency. Hence, we believe that our study helps to better understand largescale vegetation growth dynamics above the tree line in the European Alps.

Highlights

  • The capability of National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time series to track alpine grassland phenology was investigated with regard to the analysis of our 20 year AVHRR archive

  • Even though correlations were lower for AVHRR compared to the newer generation sensors, results for AVHRR were encouraging with regard to long-term vegetation dynamics analysis in the Swiss Alps

  • The ability of the tested Fourier adjustment algorithm to minimize undesirable noise from the NDVI time series at the selected sites must be rated higher compared to the Savitzky-Golay product, since the Fourier product is less susceptible to noisy composite time series

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Summary

Introduction

The effects of climate variability on ecosystems have in recent decades become increasingly important within the global climate change discussion [1]: earlier start of spring and extended autumn conditions are reflected in phenological time series and result in prolonged growing seasons. This has already been demonstrated in phenological ground observations In order to assure the comparability of remote sensing and ground phenological data sets, Fisher and Mustard [7] suggest that the phenological metric to be investigated should ideally be identifiable from ground and space and it should represent the same phenological event from both perspectives. The same authors stress the importance of topography in comparative studies such that small-scale phenological heterogeneity due to highly variable topography may lead to discrepancies between remote sensing and ground observations

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