Article

Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sens Environ

Institute of Biology, University of Tromsø, N-9037 Tromsø, Norway
Remote Sensing of Environment (Impact Factor: 6.39). 02/2006; 100(3):321-334. DOI: 10.1016/j.rse.2005.10.021

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.

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    • "The presence of healthy vegetation, and continuous vegetative cover, is essential to training land availability and access as it reduces soil erosion, prevents formation of dangerous gullies, and provides natural cover for soldiers and vehicles during training exercises. Degradation of vegetation health results from gradual or abrupt changes in the level of vegetation activity over time (de Jong et al., 2011), which can be routinely monitored by collecting and analyzing time-series Normalized Difference Vegetation Index (NDVI) data from medium and coarse spatial resolution satellite sensors (Beck et al., 2006; Dash et al., 2010; Julien and Sobrino, 2009; Verbesselt et al., 2010a, 2010b). Time series datasets using NDVI products from the Moderate Resolution Imaging Spectrometer (MODIS) sensor have been successfully used to quantify vegetation activity and vegetation dynamics (Alh et al., 2006; Jacquin et al., 2010; Zhang et al., 2003). "
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    • "The EVI data from the MODIS instruments mounted on the EOS TERRA and AQUA satellite platforms (launched in 1999 and 2002, respectively) have been the primary data source for LSP characterizations since 2000. LSP is often characterized by fitting mathematical curves such as double logistic models (Beck et al., 2006; Eklundh and J€ onsson, 2010; Zhang et al., 2003, 2006), asymmetric Gaussian models (Beck et al., 2006; Eklundh and J€ onsson, 2010) or Fourier and Wavelet transformations (Moody and Johnson, 2001) to vegetation index greenness time series. A limiting factor in the application of existing techniques on the Australian continent is that the majority of LSP algorithms have been developed to characterize ecosystems in the mid and high latitudes of the Northern Hemisphere where LSP cycles reoccur annually (de Beurs and Henebry, 2010; Eklundh and J€ onsson, 2010). "
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