A ground-validated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola peninsula

International Journal of Remote Sensing (Impact Factor: 1.65). 01/2007; 28.
Source: OAI


An NDVI dataset covering Fennoscandia and the Kola peninsula was created for vegetation and climate studies, using Moderate Resolution Imaging Spectroradiometer 16-day maximum value composite data from 2000 to 2005. To create the dataset, (1) the influence of the polar night and snow on the NDVI values was removed by replacing NDVI values in winter with a pixel-specific NDVI value representing the NDVI outside the growing season when the pixel is free of snow; and (2) yearly NDVI time series were modelled for each pixel using a double logistic function defined by six parameters. Estimates of the onset of spring and the end of autumn were then mapped using the modelled dataset and compared with ground observations of the onset of leafing and the end of leaf fall in birch, respectively. Missing and poor-quality data prevented estimates from being produced for all pixels in the study area. Applying a 5 km×5 km mean filter increased the number of modelled pixels without decreasing the accuracy of the predictions. The comparison shows good agreement between the modelled and observed dates (root mean square error = 12 days, n = 108 for spring; root mean square error = 10 days, n = 26, for autumn). Fennoscandia shows a range in the onset of spring of more than 2 months within a single year and locally the onset of spring varies with up to one month between years. The end of autumn varies by one and a half months across the region. While continued validation with ground data is needed, this new dataset facilitates the detailed monitoring of vegetation activity in Fennoscandia and the Kola peninsula.

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    • "Beck and co-workers [45] recently proposed that a double logistic function was appropriate for describing vegetation dynamics at high latitudes based on MODIS time series data of vegetation. This method captures well the timing of bud-burst in the study region [46] and among-year variation in vegetation phenology [47]. For each pixel, we fitted the double logistic function proposed in [45] to the annual time series of EVI, but rather than imputing minimum EVI observed in October-November for the dark season, when no images were available, we imputed EVI = 0 because this more accurately describes the transition from snow on ground to green vegetation. "
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    ABSTRACT: Global warming is expected to cause earlier springs and increased primary productivity in the Arctic. These changes may improve food availability for Arctic herbivores, but may also have negative effects by generating a mismatch between the surge of high quality food in the spring and the timing of reproduction. We analyzed a 10 year dataset of satellite derived measures of vegetation green-up, population densities, calf body masses and female reproductive success in 19 reindeer () populations in Northern Norway. An early onset of spring and high peak plant productivity had positive effects on calf autumn body masses and female reproductive success. In addition, body masses and reproductive success were both negatively related to population density. The quantity of food available, as determined by the onset of vegetation green-up and plant productivity over the summer were the main drivers of body mass growth and reproductive success. We found no evidence for an effect of the speed of spring green-up. Nor did we detect a negative mismatch between early springs and subsequent recruitment. Effects of global warming on plant productivity and onset of spring is likely to positively affect sub-Arctic reindeer.
    PLoS ONE 02/2013; 8(2):e56450. DOI:10.1371/journal.pone.0056450 · 3.23 Impact Factor
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    • "Cultivated land occurs particularly in the lower part of the study area, mostly on the west coast and along the fjords (Moen 1999). The study area covers large climatic gradient with substantial variation in both onset of spring (ranging from early-April to June; Beck et al. 2007) and yearly maximum snow depth (ranging from < 25 to > 400 cm;, accessed October 2011). "
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    • "We also investigate the sensitivity of the EMEP CTM model results to the choice of method, in order to quantify the impacts of changes in growing season on a few illustrative metrics of air quality. There are several different definitions for SGS, including start of budburst (Duchemin et al., 1999), start of leaf unfolding (Beck et al., 2007; Kross et al., 2011; O'Connor et al., 2012), or cambial growth after winter dormancy (Krepkowski et al., 2011; Jyske et al., 2012), and it is often unclear which definition is used in different studies. In this study, we define SGS as the start time of leaf unfolding by the plants. "
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    ABSTRACT: Chemical transport models (CTMs), used for the prediction of, for example, nitrogen deposition or air quality changes, require estimates of the growing season of plants for a number of reasons. Typically, the growing seasons are defined in a very simplified way in CTMs, using fixed dates or simple functions. In order to explore the importance of more realistic growing season estimates, we have developed a new and simple method (the T5 method) for calculating the start of the growing season (SGS) of birch (which we use as a surrogate for deciduous trees), suitable for use in CTMs and other modelling systems. We developed the T5 method from observations, and here we compare with these and other methodologies, and show that with just two parameters T5 captures well the spatial variation in SGS across Europe. We use the EMEP MSC-W chemical transport model to il-lustrate the importance of improved SGS estimates for ozone and two metrics associated with ozone damage to vegetation. This study shows that although inclusion of more realistic growing seasons has only small effects on annual average concentrations of pollutants such as ozone, the metrics asso-ciated with vegetation risk from ozone are significantly af-fected. This work demonstrates a strong need to include more re-alistic treatments of growing seasons in CTMs. The method used here could also be suitable for other types of models that require information on vegetation cover, such as meteo-rological and regional climate models. In future work, the T5 and other methods will be further evaluated for other forest species, as well as for agricultural and grassland land covers, which are important for emissions and deposition of reactive nitrogen compounds.
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