Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI

P.S.A. Beck, C. Atzberger, K.A. Hogda, B. Johansen, A.K. Skidmore

Research output: Contribution to journalArticleAcademicpeer-review

501 Citations (Scopus)

Abstract

Current models of vegetation dynamics using the normalized vegetation index (NDVI) time series perform poorly for high-latitude environments. This is due partly to specific attributes of these environments, such as short growing season, long periods of darkness in winter, persistence of snow cover, and dominance of evergreen species, but also to the design of the models. We present a new method for monitoring vegetation activity at high latitudes, using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI. It estimates the NDVI of the vegetation during winter and applies a double logistic function, which is uniquely defined by six parameters that describe the yearly NDVI time series. Using NDVI data from 2000 to 2004, we illustrate the performance of this method for an area in northern Scandinavia (35 × 162 km2, 68° N 23° E) and compare it to existing methods based on Fourier series and asymmetric Gaussian functions. The double logistic functions describe the NDVI data better than both the Fourier series and the asymmetric Gaussian functions, as quantified by the root mean square errors. Compared with the method based on Fourier series, the new method does not overestimate the duration of the growing season. In addition, it handles outliers effectively and estimates parameters that are related to phenological events, such as the timing of spring and autumn. This makes the method most suitable for both estimating biophysical parameters and monitoring vegetation phenology.
Original languageEnglish
Pages (from-to)321-334
JournalRemote Sensing of Environment
Volume100
Issue number3
DOIs
Publication statusPublished - 2006

Keywords

  • terrestrial biophysical parameters
  • multitemporal avhrr data
  • coarse resolution data
  • satellite sensor data
  • leaf-area index
  • time-series
  • fourier-analysis
  • data set
  • interannual variability
  • atmospheric correction

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