A ground-validated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola peninsula
ABSTRACT 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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ABSTRACT: Question: Is there, at the macro-habitat scale, a relationship between the fraction of evergreen forests and the presence probability of epiphyllous liverworts? Can these two parameters be estimated and mapped using an NDVI-based indicator that is derived from time-series of SPOT-VGT imagery? Location: Southern China. Methods: Applying the ISODATA algorithm, we classified SPOT-VGT NDVI time-series imagery and produced an NDVI map at 1-km² resolution containing 128 NDVI classes. That map and the National Land Cover Map of China (2000) were used to prepare a scheme for field sampling. For 537 1-km² areas, located in 19 blocks of 100 km × 100 km, field data were collected. They represented 51 preselected NDVI classes that were assumed to contain evergreen forests. Data on the fractions of forest and evergreen forest, the fraction of evergreen forest in forest present and presence probability of epiphyllous liverworts were regressed against different NDVI class-specific indicators by means of weighted least-squares regression (WLSR). Results: The SPOT-VGT NDVI for March was found to best explain the variation between 1-km² areas in the presence probability of epiphyllous liverworts (R² = 0.64; linear relationship; RMSE = 0.38) as the area fraction (%) of evergreen forest (R² = 0.90; exponential relationship; RMSE = 6.1%). Epiphyllous liverworts were only found within 1-km² areas when the SPOT-VGT NDVI value for March was more than 0.50 (model estimate: 0.43 ± 0.20) and the fraction of evergreen forest in 1 km² was above 14% (model estimate: 5 ± 35%). The estimation errors (with 95% confidence interval) of these two relationships were calculated using a bootstrap resampling method with 1000 replications; they were, respectively, 0.49—0.76 and 0.85—0.94 for R², and 0.11—0.23 and 5.2— 11.9% for RMSE. Other positive relationships were found between the presence probability of epiphyllous liverworts and the fraction of evergreen forest (R² = 0.64, linear relationship; RMSE = 0.38) and between the fraction of evergreen forest and forest within 1-km² areas (R² = 0.80, linear relationship; RMSE = 29%). Conclusion: We show that for southern China, the fraction of evergreen forest and the presence probability of epiphyllous liverworts can directly be inferred by making use of SPOT-VGT NDVI imagery. The findings are fully consistent with earlier reported hotspots for epiphyllous liverworts. At the macro-habitat scale, the presence probability of epiphyllous liverworts proved quantitatively related to the fraction of evergreen forest. This suggests causal relationships with patch size and/or rainfall patterns. We believe that the derived maps may serve as a foundation for a range of further studies.Journal of Vegetation Science 01/2013; 24:214-226. · 2.82 Impact Factor
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ABSTRACT: Animal movements are the primary behavioural adaptation to spatiotemporal heterogeneity in resource availability. Depending on their spatiotemporal scale, movements have been categorized into distinct functional groups (e.g. foraging movements, dispersal, migration), and have been studied using different methodologies. We suggest striving towards the development of a coherent framework based on the ultimate function of all movement types, which is to increase individual fitness through an optimal exploitation of resources varying in space and time. We developed a novel approach to simultaneously study movements at different spatiotemporal scales based on the following proposed theory: the length and frequency of animal movements are determined by the interaction between temporal autocorrelation in resource availability and spatial autocorrelation in changes in resource availability. We hypothesized that for each time interval the spatiotemporal scales of moose Alces alces movements correspond to the spatiotemporal scales of variation in the gains derived from resource exploitation when taking into account the costs of movements (represented by their proxies, forage availability NDVI and snow depth respectively). The scales of change in NDVI and snow were quantified using wave theory, and were related to the scale of moose movement using linear mixed models. In support of the proposed theory we found that frequent, smaller scale movements were triggered by fast, small-scale ripples of changes, whereas infrequent, larger scale movements matched slow, large-scale waves of change in resource availability. Similarly, moose inhabiting ranges characterized by larger scale waves of change in the onset of spring migrated longer distances. We showed that the scales of movements are driven by the scales of changes in the net profitability of trophic resources. Our approach can be extended to include drivers of movements other than trophic resources (e.g. population density, density of related individuals, predation risk) and may facilitate the assessment of the impact of environmental changes on community dynamics and conservation.Journal of Animal Ecology 02/2013; · 4.84 Impact Factor
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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.Biogeosciences 11/2012; 9:5161-5179. · 3.75 Impact Factor