BACKGROUND
land surface phenology is a key variables for modelling of the terrestrial ecosystems in a global change perspective and as such as input into circulation models   <cit> .
recent models agree that changes in vegetation and soil processes will have net positive feedback on future global warming  <cit> .
further, the way terrestrial ecology is implemented in gcm's will have a strong impact on the ability to predict future climatic changes  <cit>  and knowledge of changes in the vegetation cover and the reasons for these are therefore of major importance.
this has led to a number of publications and assessments on the greening and impact of global climate change in northern high latitudes  <cit>  based on monitoring studies  <cit>  and experimental studies  <cit> .
several of these studies have investigated the effect of temperature, light and fertilization on photosynthesis  <cit>  and found that fertilization and increased temperatures significantly increases the photosynthesis while shading decreases the photosynthesis.
thawing degree-days and time of snow cover melt was found as the dominating controls on the phenology in the subarctic  <cit> .
recently, walker et al.
included warming experiments from  <dig> locations across the tundra biome in a meta-analysis of the response of tundra vegetation to predicted warming  <cit> .
they found a clear response where warming increased height and cover of dwarf shrubs while moss, lichen cover and species diversity decreased.
however, the high arctic sites showed a lack of response believed to be based on the limited ability for the high arctic plants to respond to changes in the growing season temperature and length.
very few studies of the controls on arctic vegetation phenology have been performed in the high arctic.
the most extensive study from high arctic greenland dates back to the 1930'ies where sørensen found that temperature and timing of snow melt were the most important factors for phenological responses  <cit> .
more recently, marchand et al.
used experimental warming on high arctic grasslands, finding that warming enhanced the green cover  <cit> .
a clear control of temperature and photosynthetically active radiation  on the timing of flowering was found by høye et al.
who investigated the phenology and flowering of dryas spp.
hybrids along a snowmelt gradient in northeast greenland  <cit> .
normalised difference vegetation index  is a spectral measure that has been used widely in some of these studies as proxy for biophysical variables like biomass, leaf area index, co <dig> flux etc.
and that can be acquired non-destructively.
vegetation reflects light differently in the red and infrared wavelength resulting in a measure that is highly correlated with the biophysical variables  <cit> .
this enables long-term monitoring of land surface phenology using the same plots instead of traditional destructive methods where plant material is harvested for weighing, leaf area measurements etc.
several authors have tested different phenological models for use with ndvi and land surface phenology including double-logistic functions  <cit> , sigmoid functions  <cit> , quadratic functions  <cit>  and others.
however, hope et al.
used the low-resolution avhrr satellites to investigate trends and possible controls of ndvi on the north slope of alaska   <cit> .
they focussed on the seasonal integrated ndvi  over three different vegetation types and found no relation between meteorological observations and sindvi using standard correlations.
they speculated that the complex interactions between climate and vegetation might require more complex modelling to reveal the relationships between these factors.
karlsen et al.
used a similar integrated measure  for the study of inter-annual changes in land surface phenology  <cit> .
in this paper we present a seven year field study of land surface phenology in the high arctic and use mixed models to investigate the possible controls of the growth dynamics.
the ndvi-fr measurements are obtained from  <dig> individual plots within the zackenberg ecological research operations  area  using a hand held skye instruments sensor at six vegetation types that range over dry, mesic and wet types.
this sensor uses the red and far red bands instead of the traditional red and near-infrared bands.
we investigate the seasonally integrated far red normalised difference vegetation index  using the nomenclature of hope et al.
<cit> , the timing of the maximum ndvi-fr  and the level of maximum ndvi-fr .
we hypothesize that the main explanatory variables controlling the seasonal growth dynamics of the six main vegetation types are timing of the snow melt, temperature during the growing season, light in the form of photosynthetic active radiation, rain during the growing season, the general state of the vegetation as expressed by the sindvi in the previous growing season and the temperature during the previous year growing season.
we expect that the different vegetation types will show different response from the explanatory variables.
RESULTS
ndvi-fr seasonal modelling
the difference in seasonal greenness and onset of growing season between vegetation types is clear in figure  <dig>  it shows a summary of all the used ndvi-fr data for the six vegetation types during  <dig> to  <dig>  in the figure the quadratic model has been fitted to the average ndvi-fr of each vegetation type for each week.
for the  <dig> separate dataset  the quadratic models fitted with a mean r <dig> value of  <dig> .
the dry and sparsely vegetated fell field melts free from snow earlier than the rest of the vegetation types but are more sparsely vegetated and therefore less green.
from the relative dry dwarf shrub heaths  over the moist salix heath to the more wet grassland and fens later snow melt and higher maximum ndvi-fr values are clear.
looking across years,  <dig> was a record year with deep snow cover and low spring temperatures causing the growing season to start very late.
contrary,  <dig> had low snow cover and high temperatures both through winter  and during spring causing an early start of the growing season.
interannual and seasonal characteristics of the vegetation types
summary of the major growth dynamics variables  and exploratory variables  for each vegetation type and each year is shown in figure  <dig>  only the full season data are shown.
<dig> is only used as the previous year data in  <dig> and therefore not shown directly.
the data show that the  <dig> vegetation types have different growth dynamics and grow under different abiotic conditions.
especially the dry types  show different characteristics than the other types.
they experience an earlier snowmelt and therefore earlier start on the growing season.
this results in a higher input of photosynthetical radiation partly since the radiation is higher early than later during the summer and partly due to the longer growing season.
also the summed temperatures are slightly higher for the dry types than for the other types.
fell field and dryas heath have an earlier maximum of the growth season but a lower maximum ndvi-fr and lower seasonal integrated ndvi-fr than the rest.
salix heath is the vegetation type that melts out latest in the season and peaks later than the other types.
contrary to the dry types it has a shorter growing season and hence experience lower summed temperatures and par .
salix heath has a medium maxndvi that does not change much between years.
the highest ndvi-fr values occur in the more wet vegetation types, grassland and fen.
fen has the highest maximum values and highest sindvi but experiences abiotic conditions close to average for the area.
most of the vegetation types show a significant trend towards earlier snow melt and lower maxndvi.
changes between years in maxndvi are small.
before running the models we tested for correlation between each set of explanatory variable.
we did not find any correlations with r <dig> higher than  <dig>  among the used variables and therefore included all in the modelling.
high correlation is on the other hand found between two of the main variables, maxndvi and sindvi with distinct grouping of the vegetation types along the possible regression line – dry types at the bottom and more moist and vigorous types at the top rigth.
the same pattern was reported by hope et al.
<cit> .
gamm modelling
the modelling of sindvi and doymax was completed with success for each of the vegetation types and for the entire datasets.
however, the modelling of maxndvi was not successful.
the models did not converge with  <dig> degrees of smoothing and when we tried to raise the numbers of smoothing we found that it resulted in unreasonable responses that had no biological meaning.
table  <dig> shows a summary of the results of the modelling of sindvi and doymax.
for each response variable is shown the included explanatory variables of the seven models and an indication for the direction of the correlation of each variable.
the total variance that was explained is shown for each model as the adjusted r <dig>
variables included in the final models and their relation to the modeled response variables: seasonal integrated ndvi-fr  and the timing of maximum ndvi-fr .
'+' indicates that the variable has a linear positive correlation with the response variable while '-' indicates a negative linear relation.
brackets indicate a non-linear correlation but with an overall positive or negative trend.
'∩' indicate a non-linear response with a maximum, and 'u' indicate a non-linear response with a minimum.
blank indicates that the variable didn't improve the model and was therefore not included * green-up is defined from the start of the growing season to the day of maximum.
sindvi gamm models: the sindvi model for the overall vegetation cover  show that all the variables hypothesized to control the sindvi was significant and therefore included .
78% of the variance in sindvi is explained by this model.
sindvi decreases non-linearly with late snow melt so that early snow melt before doy  <dig>  does not seem to change sindvi much but after this time sindvi decreases sharply with later snow melt.
the opposite is true for the amount of light  and temperatures in the previous year where changes in the lower range has higher relative impact than with much light and high temperatures in the previous year.
the relationship between rain and sindvi is almost linearly in the observed range so that more rain during the summer will decrease the sindvi.
however, the relationship is not as pronounced as with the other variables.
sindvi increases linearly with the integrated vegetation greenness in the previous year  as would be expected while it seems that the current year's air temperature is beneficial to sindvi only until a curtain limit where after it influences negatively.
this behaviour of sindvi to the explanatory variables is to some degree different when looking at the individual vegetation types.
only the negative effect of air temperature in the previous year on sindvi is the same for all vegetation types.
the fell field model did not include the time of snow melt in the model and had as opposed to the overall model a positive, linear relation between temperature, light, rain and previous year's sindvi.
with 84% of the variance explained fell field is the vegetation type with the most variance in sindvi explained by the model.
cassiope heath show the same results as for the combined vegetation types but the dryas heath model did not include light and rain in the model and for snow melt the response in sindvi is positive with later melt until a maximum in the beginning of june  where sindvi thereafter respond negatively to later snow melt.
only 21% of the variance in sindvi is explained by the model.
the pattern changes more going towards the more wet vegetation types until fen where sindvi  respond positive to later snow melt, increasing light and added rain.
doymax gamm models: the timing of the maximum ndvi-fr is explained a lot better than sindvi.
the lowest adjusted r <dig> is  <dig>  and the highest  <dig>  indicating that the used explanatory variables are controlling most of the variance of doymax.
the response curves for the combined vegetation types are shown in figure  <dig>  changes towards later snow melt clearly indicates a later occurring maximum as does increasing air temperatures during the green-up period although the positive response disappear with very high green-up temperatures.
both rain in the green-up period and temperatures in the previous year show an optimum range for later timing although none of the responses are as pronounced as for esm or airup.
the model explains 89% of the variance in the timing of the combined vegetation types.
a late snow melt and hence later onset of the growing season is significantly postponing the time of maximum in all vegetation types.
the shape of the response for grassland indicates a slight negative response with very early snow melts but confidence intervals are so large here that this might be due to the relative small sample size.
higher temperatures and amount of light and rain during the green-up period respond in a later maximum for both the cassiope heath and the salix snow bed.
the integrated vegetation greenness in the previous year  is only included in a few of the models and in general do not seem to have a great impact on the timing of the greening in a following year.
discussion
ndvi-fr measured in six vegetation types
as illustrated in figure  <dig> and  <dig> we here document for the first time to our knowledge how ndvi-fr varies substantially between six vegetation types in the high arctic.
other studies have reported on one or two vegetation types  or from the lower arctic regions .
not surprisingly, ndvi-fr shows lower max values in dry and sparsely vegetated areas than in moist and wet areas.
for example, in  <dig>  ndvi-fr peaks around  <dig>  in the fens whereas peak values for dryas heath are around  <dig> .
looking at plant species types, we found increasing values of maxndvi and sindvi from moss and lichens at fell fields to evergreen and wintergreen species in dryas heath and cassiope heath and further to deciduous species in salix heath, grassland and fen.
evergreen and wintergreen species invests energy in green leaves that are robust and can sustain the harsh winter conditions.
hence they develop small thick waxy leaves which are able to start photosynthesis right after snowmelt, or even before snowmelt  <cit> .
these leaves require high amounts of energy to develop and sustain  <cit>  and therefore less green tissue is exposed during the growing season than is the case for the deciduous plant types.
this is mirrored in the ndvi-fr values .
on the other hand, salix arctica, fen species and grassland species have a high acute need of nutrients and water for development of green tissue during the growing season and hence deciduous plant species are found at moist and wet locations where water and nutrients are more abundant than in the drier ecotones.
looking at doymax  three different groups are evident.
fell field and dryas heath peaks first followed by cassiope heath, grassland and fen, whereas salix heath peaks latest in the growing season.
fell field and dryas heath are both situated at exposed plateaus where snow might blow off during winter months and which get snow free early in spring.
plant species here have an early snow melt and hence start of growing season  and can take advantage of the high par levels around midsummer.
therefore they peak earlier than species growing where snow stays later.
in the other end of the scale, salix heath is often situated around late melting snow fans in the zackenberg area  <cit> , and hence, they experience the latest doymax.
from figure 3d it is evident that the growing season starts at approximately the same time at cassiope heath, grassland and fen.
these community types experience doymax at the same time  and we conclude that this also is caused by the time of snowmelt.
timing
during the years of monitoring, we found a trend of earlier end of snow melt in all years after  <dig> , and hence, an earlier timing of maxndvi  albeit this trend was only clear after year  <dig>  therefore, we would expect increasing values of sindvi.
however, contrary to regional studies of greening in the arctic  we found a decreasing trend of the ndvi-fr and sindvi in some of the vegetation types at zackenberg .
however, only a few of the trends are very significant  and with the short monitoring period and known variability in the climate in ne-greenland one year with opposite trend might reduce or remove the significance.
there also is a tendency to trambling effects in the drier areas but given that the significance of decrease is highest in the wet areas we assume that trambling is currently not an issue.
however, it seems that trambling is a factor that needs to be taken into account in the long time series analysis in the coming years.
the decrease in ndvi-fr and sindvi for some of the vegetation types combined with the simultaneous earlier end of snow melt  this indicates that the availability of water from melting snow is important for the area.
therefore, although greening might occur wide spread in the arctic there are variations on the local scale that will influence the regional trends in the longer term.
the models
monitoring growth dynamics of the vegetation types in the high arctic with remote sensing is influenced by a range of variables .
the need to be able to model the growth dynamics from the reliable measurements is therefore clear.
many different models describing the evolution of ndvi-fr through the growing season has been published.
fisher introduced a double-logistic function for modelling of growing season by ndvi in farm land  <cit>  while lüdeke et al.
used a three-spline function in the global frankfurt biosphere model  <cit> .
an intersecting straight lines model was used by shibayama et al.
for detecting phenophases in the subarctic  <cit> .
the maximum composite technique of filtering avhrr-based ndvi values and then use of the data directly with linear interpolation have been applied world-wide including in the subarctic alaska  <cit> .
however, the latter method need data regularly from the growing season which is not possible in the high arctic ne-greenland where field researchers depending on the year arrives after the start of the growing season.
several of the mentioned methods have been accessed for this dataset but fits were not as good as those obtained with the 2nd degree model.
we therefore conclude that for the relative sparse high arctic vegetation a quadratic model is the best for simple and quick estimation of growth dynamics.
marchand et al also used this for high arctic vegetation  <cit> .
the model can be easily applied and works for separate vegetation types in the high arctic as for the vegetation as a whole.
the response variables  describing the integrated vegetation greenness and timing of maximum was modelled successfully.
unfortunately, maxndvi was not possible to model.
this is probably based on two reasons: 1) very little variance in maxndvi from year to year and 2) uncertainties in measurements of +/-  <dig>  due to surface moisture, reflection or shadowing of the surroundings  and the view angle of the instrument at the time of measurement.
for example, jacobsen and hansen found a clear relation between soil moisture and maxndvi in zackenberg  <cit> .
however, changes in maxndvi between vegetation types are still clear  and can be used for future monitoring of changes in vegetation composition.
the mixed models used in this study are complex models that are not easily interpreted – especially if many degrees of smoothing have been used.
we have limited the amount to three degrees of smoothing resulting in biological meaningful explanation of the controlling environmental factors.
fewer degrees would restrict to almost linear relations in most cases and more degrees will cause the model to follow the variance and not the overall pattern of the relationships.
unfortunately, it is not possible to obtain either the variance explained by each variable with the given method nor is it possible to interpret variables importance between models.
though it has not been possible to estimate the variance explained by each variable we believe sindvi in the previous year to be the most important.
yet, the remaining variables in the models are also significant and therefore explain some of the variance although the level explained is uncertain.
other simpler models have been presented  but these have not produced satisfactory explanation.
explanatory variables
sindvi: in general, the r <dig> values for the models of sindvi are satisfactory except for the model of dryas heath where r <dig> only reaches  <dig> , and other factors must explain sindvi.
looking at all models, two variables  show the same directional output for both the model of sindvi for all vegetation types together and the individual models for each vegetation type .
temperature in the previous year shows a negative relationship with sindvi , and therefore, higher temperatures during the growing season causes lower sindvi the following year.
the reason for this could be that high temperatures have been shown to increase the number of flowers in the following year  <cit> .
this would result in lower ndvi-fr simply because of the brighter colours of the flowers and lower absorption by chlorophyll but also in less greenness since the photosynthates are used for setting flowers and not for biomass growth.
another reason could be the effect of increased temperatures where snow patches disappear early in the season and the soil dries out causing low soil moisture levels.
this would limit growth and build-up of resources for the following year  <cit> .
contrary to this, sindvi the previous year shows a positive correlation with sindvi the current year except in grassland where no relationship exists .
almost all high arctic plant species are perennials and store resources in storage organs  from year to year  <cit> .
assuming that high values of sindvi indicates good growth conditions the previous year, the plants probably have build up resources in storage organs for growth the current year, and this is what is mirrored in high sindvi values.
a third variable, temperature during the growing season  seems interesting.
for the overall model and for the cassiope heath, salix heath and grassland models a non-linear response with a maximum for sindvi is shown .
that is, sindvi increases with increasing temperatures to a certain point where after sindvi decreases as temperatures get higher.
the explanation to this could be that the dominant species of these vegetation types all have a northern distribution pattern in greenland and are not common at lower latitudes  <cit> .
hence, they grow close to a regional southern distribution limit, and only a temperature increase to a certain degree increases their photosynthesis.
larger increases in temperature limit the photosynthesis, ceasing growth and causing sindvi to decrease  <cit>  probably due to enzymatic inhibition of the photosynthetic apparatus.
since temperatures peaks in late july and early august we do not expect the negative response from higher temperatures to be an effect of day length.
doymax: in general a higher variance can be explained by the models of doymax than sindvi for all vegetation types , but patterns in the individual explanatory variables for the six vegetation types are difficult to locate except for time of snowmelt.
all vegetation types except grassland shows a positive correlation between this variable and doymax meaning that the later the snow melts, and hence the start of the growing season, the later the doymax is reached.
this seems logic since all plant species needs a certain length of time after snowmelt to assimilate carbon and reach maximum biomass.
this is also obvious from figure 3b and figure 3d where doymax appears in three different groups depending on time of snow melt.
other factors than time may however, influence this period, for example air temperature.
our model shows a positive correlation between temperature and doymax in all vegetation types except grassland and fen meaning that high temperatures in the green-up period delays doymax.
our only explanation to this phenomenon is as described above that too high temperatures may inhibit photosynthesis in the species growing close to their regional southern limit or increase flowering that will lower the build-up of green plant material.
in conclusion we believe that the high r <dig> values for all vegetation types for the modelled doymax is due to a strong correlation with time of snow melt.
climatic change in the zackenberg area
climatic changes in the zackenberg area is currently believed to show mainly as increased temperatures and winter precipitation  <cit> .
increased precipitation will result in more snow hence prolonging the snow cover.
this would lead towards decreasing greenness and later timing of the maximum.
however, hinkler investigated the snow distribution during future climate scenarios and concluded that the projected increased precipitation and higher temperatures will leave summer-snow cover depletion close to status quo  <cit> .
recent years  have shown examples of this where a deep snow cover quickly can melt away if the temperatures in the spring are higher than normal.
in the case of year 2002; it had the next highest recorded snow depth  at the end of winter but the snow melted off only one day later than the average melt off  <cit> .
further, the unclear response from the high arctic locations of walker et al.
<cit>  combined with the decrease in sindvi from  <dig> to  <dig> in our study enhances the need for further studies in these areas.
also, the clear difference in the controlling variables and their induced response in the growth dynamics between vegetation types highlight the importance of high resolution studies.
in regional climate change models the growth dynamics are often supplied by avhrr and satellites with similar resolution.
a regional study based on recent higher resolution satellite data  and based on the methods presented here would supply valuable new knowledge in this area – an area that clearly needs attention for future regional or global climate modelling.
CONCLUSIONS
although greening is reported to occur widespread in the arctic, we have shown that variations exist that might influence regional trends in the longer term.
we investigated the growth dynamics for six dry, mesic and wet vegetation types using ndvi-fr and generalized additive mixed models .
we found a quadratic model to be the best for simple and quick estimation of seasonal integrated ndvi-fr and used this in the analysis.
snow melt and temperature were of major importance for the timing of the maximum growth as well as for the seasonal growth.
more than 85% of the variance in timing of the maximum growth is explained by the models and similar for the seasonal growth of mesic and wet vegetation types.
we found several non-linear growth responses to the environmental variables and conclude that the uses of gamms are valuable for investigating growth dynamics in the arctic.

 land surface phenology is a key variables for modelling of the terrestrial ecosystems in a global change perspective and as such as input into circulation models. recent models agree that changes in vegetation and soil processes will have net positive feedback on future global warming. further, the way terrestrial ecology is implemented in gcm's will have a strong impact on the ability to predict future climatic changes and knowledge of changes in the vegetation cover and the reasons for these are therefore of major importance. this has led to a number of publications and assessments on the greening and impact of global climate change in northern high latitudes based on monitoring studies and experimental studies. several of these studies have investigated the effect of temperature, light and fertilization on photosynthesis and found that fertilization and increased temperatures significantly increases the photosynthesis while shading decreases the photosynthesis. thawing degree-days and time of snow cover melt was found as the dominating controls on the phenology in the subarctic. recently, walker et al. included warming experiments from locations across the tundra biome in a meta-analysis of the response of tundra vegetation to predicted warming. they found a clear response where warming increased height and cover of dwarf shrubs while moss, lichen cover and species diversity decreased. however, the high arctic sites showed a lack of response believed to be based on the limited ability for the high arctic plants to respond to changes in the growing season temperature and length. very few studies of the controls on arctic vegetation phenology have been performed in the high arctic. the most extensive study from high arctic greenland dates back to the 1930'ies where sørensen found that temperature and timing of snow melt were the most important factors for phenological responses. more recently, marchand et al. used experimental warming on high arctic grasslands, finding that warming enhanced the green cover. a clear control of temperature and photosynthetically active radiation on the timing of flowering was found by høye et al. who investigated the phenology and flowering of dryas spp. hybrids along a snowmelt gradient in northeast greenland. normalised difference vegetation index is a spectral measure that has been used widely in some of these studies as proxy for biophysical variables like biomass, leaf area index, co flux etc. and that can be acquired non-destructively. vegetation reflects light differently in the red and infrared wavelength resulting in a measure that is highly correlated with the biophysical variables. this enables long-term monitoring of land surface phenology using the same plots instead of traditional destructive methods where plant material is harvested for weighing, leaf area measurements etc. several authors have tested different phenological models for use with ndvi and land surface phenology including double-logistic functions, sigmoid functions, quadratic functions and others. however, hope et al. used the low-resolution avhrr satellites to investigate trends and possible controls of ndvi on the north slope of alaska. they focussed on the seasonal integrated ndvi over three different vegetation types and found no relation between meteorological observations and sindvi using standard correlations. they speculated that the complex interactions between climate and vegetation might require more complex modelling to reveal the relationships between these factors. karlsen et al. used a similar integrated measure for the study of inter-annual changes in land surface phenology. in this paper we present a seven year field study of land surface phenology in the high arctic and use mixed models to investigate the possible controls of the growth dynamics. the ndvi-fr measurements are obtained from individual plots within the zackenberg ecological research operations area using a hand held skye instruments sensor at six vegetation types that range over dry, mesic and wet types. this sensor uses the red and far red bands instead of the traditional red and near-infrared bands. we investigate the seasonally integrated far red normalised difference vegetation index using the nomenclature of hope et al., the timing of the maximum ndvi-fr and the level of maximum ndvi-fr. we hypothesize that the main explanatory variables controlling the seasonal growth dynamics of the six main vegetation types are timing of the snow melt, temperature during the growing season, light in the form of photosynthetic active radiation, rain during the growing season, the general state of the vegetation as expressed by the sindvi in the previous growing season and the temperature during the previous year growing season. we expect that the different vegetation types will show different response from the explanatory variables.  ndvi-fr seasonal modelling the difference in seasonal greenness and onset of growing season between vegetation types is clear in figure it shows a summary of all the used ndvi-fr data for the six vegetation types during to in the figure the quadratic model has been fitted to the average ndvi-fr of each vegetation type for each week. for the separate dataset the quadratic models fitted with a mean r value of. the dry and sparsely vegetated fell field melts free from snow earlier than the rest of the vegetation types but are more sparsely vegetated and therefore less green. from the relative dry dwarf shrub heaths over the moist salix heath to the more wet grassland and fens later snow melt and higher maximum ndvi-fr values are clear. looking across years, was a record year with deep snow cover and low spring temperatures causing the growing season to start very late. contrary, had low snow cover and high temperatures both through winter and during spring causing an early start of the growing season. interannual and seasonal characteristics of the vegetation types summary of the major growth dynamics variables and exploratory variables for each vegetation type and each year is shown in figure only the full season data are shown. is only used as the previous year data in and therefore not shown directly. the data show that the vegetation types have different growth dynamics and grow under different abiotic conditions. especially the dry types show different characteristics than the other types. they experience an earlier snowmelt and therefore earlier start on the growing season. this results in a higher input of photosynthetical radiation partly since the radiation is higher early than later during the summer and partly due to the longer growing season. also the summed temperatures are slightly higher for the dry types than for the other types. fell field and dryas heath have an earlier maximum of the growth season but a lower maximum ndvi-fr and lower seasonal integrated ndvi-fr than the rest. salix heath is the vegetation type that melts out latest in the season and peaks later than the other types. contrary to the dry types it has a shorter growing season and hence experience lower summed temperatures and par. salix heath has a medium maxndvi that does not change much between years. the highest ndvi-fr values occur in the more wet vegetation types, grassland and fen. fen has the highest maximum values and highest sindvi but experiences abiotic conditions close to average for the area. most of the vegetation types show a significant trend towards earlier snow melt and lower maxndvi. changes between years in maxndvi are small. before running the models we tested for correlation between each set of explanatory variable. we did not find any correlations with r higher than among the used variables and therefore included all in the modelling. high correlation is on the other hand found between two of the main variables, maxndvi and sindvi with distinct grouping of the vegetation types along the possible regression line – dry types at the bottom and more moist and vigorous types at the top rigth. the same pattern was reported by hope et al.. gamm modelling the modelling of sindvi and doymax was completed with success for each of the vegetation types and for the entire datasets. however, the modelling of maxndvi was not successful. the models did not converge with degrees of smoothing and when we tried to raise the numbers of smoothing we found that it resulted in unreasonable responses that had no biological meaning. table shows a summary of the results of the modelling of sindvi and doymax. for each response variable is shown the included explanatory variables of the seven models and an indication for the direction of the correlation of each variable. the total variance that was explained is shown for each model as the adjusted r variables included in the final models and their relation to the modeled response variables: seasonal integrated ndvi-fr and the timing of maximum ndvi-fr.'+' indicates that the variable has a linear positive correlation with the response variable while'-' indicates a negative linear relation. brackets indicate a non-linear correlation but with an overall positive or negative trend.'∩' indicate a non-linear response with a maximum, and'u' indicate a non-linear response with a minimum. blank indicates that the variable didn't improve the model and was therefore not included * green-up is defined from the start of the growing season to the day of maximum. sindvi gamm models: the sindvi model for the overall vegetation cover show that all the variables hypothesized to control the sindvi was significant and therefore included. 78% of the variance in sindvi is explained by this model. sindvi decreases non-linearly with late snow melt so that early snow melt before doy does not seem to change sindvi much but after this time sindvi decreases sharply with later snow melt. the opposite is true for the amount of light and temperatures in the previous year where changes in the lower range has higher relative impact than with much light and high temperatures in the previous year. the relationship between rain and sindvi is almost linearly in the observed range so that more rain during the summer will decrease the sindvi. however, the relationship is not as pronounced as with the other variables. sindvi increases linearly with the integrated vegetation greenness in the previous year as would be expected while it seems that the current year's air temperature is beneficial to sindvi only until a curtain limit where after it influences negatively. this behaviour of sindvi to the explanatory variables is to some degree different when looking at the individual vegetation types. only the negative effect of air temperature in the previous year on sindvi is the same for all vegetation types. the fell field model did not include the time of snow melt in the model and had as opposed to the overall model a positive, linear relation between temperature, light, rain and previous year's sindvi. with 84% of the variance explained fell field is the vegetation type with the most variance in sindvi explained by the model. cassiope heath show the same results as for the combined vegetation types but the dryas heath model did not include light and rain in the model and for snow melt the response in sindvi is positive with later melt until a maximum in the beginning of june where sindvi thereafter respond negatively to later snow melt. only 21% of the variance in sindvi is explained by the model. the pattern changes more going towards the more wet vegetation types until fen where sindvi respond positive to later snow melt, increasing light and added rain. doymax gamm models: the timing of the maximum ndvi-fr is explained a lot better than sindvi. the lowest adjusted r is and the highest indicating that the used explanatory variables are controlling most of the variance of doymax. the response curves for the combined vegetation types are shown in figure changes towards later snow melt clearly indicates a later occurring maximum as does increasing air temperatures during the green-up period although the positive response disappear with very high green-up temperatures. both rain in the green-up period and temperatures in the previous year show an optimum range for later timing although none of the responses are as pronounced as for esm or airup. the model explains 89% of the variance in the timing of the combined vegetation types. a late snow melt and hence later onset of the growing season is significantly postponing the time of maximum in all vegetation types. the shape of the response for grassland indicates a slight negative response with very early snow melts but confidence intervals are so large here that this might be due to the relative small sample size. higher temperatures and amount of light and rain during the green-up period respond in a later maximum for both the cassiope heath and the salix snow bed. the integrated vegetation greenness in the previous year is only included in a few of the models and in general do not seem to have a great impact on the timing of the greening in a following year. discussion ndvi-fr measured in six vegetation types as illustrated in figure and we here document for the first time to our knowledge how ndvi-fr varies substantially between six vegetation types in the high arctic. other studies have reported on one or two vegetation types or from the lower arctic regions. not surprisingly, ndvi-fr shows lower max values in dry and sparsely vegetated areas than in moist and wet areas. for example, in ndvi-fr peaks around in the fens whereas peak values for dryas heath are around. looking at plant species types, we found increasing values of maxndvi and sindvi from moss and lichens at fell fields to evergreen and wintergreen species in dryas heath and cassiope heath and further to deciduous species in salix heath, grassland and fen. evergreen and wintergreen species invests energy in green leaves that are robust and can sustain the harsh winter conditions. hence they develop small thick waxy leaves which are able to start photosynthesis right after snowmelt, or even before snowmelt. these leaves require high amounts of energy to develop and sustain and therefore less green tissue is exposed during the growing season than is the case for the deciduous plant types. this is mirrored in the ndvi-fr values. on the other hand, salix arctica, fen species and grassland species have a high acute need of nutrients and water for development of green tissue during the growing season and hence deciduous plant species are found at moist and wet locations where water and nutrients are more abundant than in the drier ecotones. looking at doymax three different groups are evident. fell field and dryas heath peaks first followed by cassiope heath, grassland and fen, whereas salix heath peaks latest in the growing season. fell field and dryas heath are both situated at exposed plateaus where snow might blow off during winter months and which get snow free early in spring. plant species here have an early snow melt and hence start of growing season and can take advantage of the high par levels around midsummer. therefore they peak earlier than species growing where snow stays later. in the other end of the scale, salix heath is often situated around late melting snow fans in the zackenberg area, and hence, they experience the latest doymax. from figure 3d it is evident that the growing season starts at approximately the same time at cassiope heath, grassland and fen. these community types experience doymax at the same time and we conclude that this also is caused by the time of snowmelt. timing during the years of monitoring, we found a trend of earlier end of snow melt in all years after, and hence, an earlier timing of maxndvi albeit this trend was only clear after year therefore, we would expect increasing values of sindvi. however, contrary to regional studies of greening in the arctic we found a decreasing trend of the ndvi-fr and sindvi in some of the vegetation types at zackenberg. however, only a few of the trends are very significant and with the short monitoring period and known variability in the climate in ne-greenland one year with opposite trend might reduce or remove the significance. there also is a tendency to trambling effects in the drier areas but given that the significance of decrease is highest in the wet areas we assume that trambling is currently not an issue. however, it seems that trambling is a factor that needs to be taken into account in the long time series analysis in the coming years. the decrease in ndvi-fr and sindvi for some of the vegetation types combined with the simultaneous earlier end of snow melt this indicates that the availability of water from melting snow is important for the area. therefore, although greening might occur wide spread in the arctic there are variations on the local scale that will influence the regional trends in the longer term. the models monitoring growth dynamics of the vegetation types in the high arctic with remote sensing is influenced by a range of variables. the need to be able to model the growth dynamics from the reliable measurements is therefore clear. many different models describing the evolution of ndvi-fr through the growing season has been published. fisher introduced a double-logistic function for modelling of growing season by ndvi in farm land while lüdeke et al. used a three-spline function in the global frankfurt biosphere model. an intersecting straight lines model was used by shibayama et al. for detecting phenophases in the subarctic. the maximum composite technique of filtering avhrr-based ndvi values and then use of the data directly with linear interpolation have been applied world-wide including in the subarctic alaska. however, the latter method need data regularly from the growing season which is not possible in the high arctic ne-greenland where field researchers depending on the year arrives after the start of the growing season. several of the mentioned methods have been accessed for this dataset but fits were not as good as those obtained with the 2nd degree model. we therefore conclude that for the relative sparse high arctic vegetation a quadratic model is the best for simple and quick estimation of growth dynamics. marchand et al also used this for high arctic vegetation. the model can be easily applied and works for separate vegetation types in the high arctic as for the vegetation as a whole. the response variables describing the integrated vegetation greenness and timing of maximum was modelled successfully. unfortunately, maxndvi was not possible to model. this is probably based on two reasons: 1) very little variance in maxndvi from year to year and 2) uncertainties in measurements of +/- due to surface moisture, reflection or shadowing of the surroundings and the view angle of the instrument at the time of measurement. for example, jacobsen and hansen found a clear relation between soil moisture and maxndvi in zackenberg. however, changes in maxndvi between vegetation types are still clear and can be used for future monitoring of changes in vegetation composition. the mixed models used in this study are complex models that are not easily interpreted – especially if many degrees of smoothing have been used. we have limited the amount to three degrees of smoothing resulting in biological meaningful explanation of the controlling environmental factors. fewer degrees would restrict to almost linear relations in most cases and more degrees will cause the model to follow the variance and not the overall pattern of the relationships. unfortunately, it is not possible to obtain either the variance explained by each variable with the given method nor is it possible to interpret variables importance between models. though it has not been possible to estimate the variance explained by each variable we believe sindvi in the previous year to be the most important. yet, the remaining variables in the models are also significant and therefore explain some of the variance although the level explained is uncertain. other simpler models have been presented but these have not produced satisfactory explanation. explanatory variables sindvi: in general, the r values for the models of sindvi are satisfactory except for the model of dryas heath where r only reaches, and other factors must explain sindvi. looking at all models, two variables show the same directional output for both the model of sindvi for all vegetation types together and the individual models for each vegetation type. temperature in the previous year shows a negative relationship with sindvi, and therefore, higher temperatures during the growing season causes lower sindvi the following year. the reason for this could be that high temperatures have been shown to increase the number of flowers in the following year. this would result in lower ndvi-fr simply because of the brighter colours of the flowers and lower absorption by chlorophyll but also in less greenness since the photosynthates are used for setting flowers and not for biomass growth. another reason could be the effect of increased temperatures where snow patches disappear early in the season and the soil dries out causing low soil moisture levels. this would limit growth and build-up of resources for the following year. contrary to this, sindvi the previous year shows a positive correlation with sindvi the current year except in grassland where no relationship exists. almost all high arctic plant species are perennials and store resources in storage organs from year to year. assuming that high values of sindvi indicates good growth conditions the previous year, the plants probably have build up resources in storage organs for growth the current year, and this is what is mirrored in high sindvi values. a third variable, temperature during the growing season seems interesting. for the overall model and for the cassiope heath, salix heath and grassland models a non-linear response with a maximum for sindvi is shown. that is, sindvi increases with increasing temperatures to a certain point where after sindvi decreases as temperatures get higher. the explanation to this could be that the dominant species of these vegetation types all have a northern distribution pattern in greenland and are not common at lower latitudes. hence, they grow close to a regional southern distribution limit, and only a temperature increase to a certain degree increases their photosynthesis. larger increases in temperature limit the photosynthesis, ceasing growth and causing sindvi to decrease probably due to enzymatic inhibition of the photosynthetic apparatus. since temperatures peaks in late july and early august we do not expect the negative response from higher temperatures to be an effect of day length. doymax: in general a higher variance can be explained by the models of doymax than sindvi for all vegetation types, but patterns in the individual explanatory variables for the six vegetation types are difficult to locate except for time of snowmelt. all vegetation types except grassland shows a positive correlation between this variable and doymax meaning that the later the snow melts, and hence the start of the growing season, the later the doymax is reached. this seems logic since all plant species needs a certain length of time after snowmelt to assimilate carbon and reach maximum biomass. this is also obvious from figure 3b and figure 3d where doymax appears in three different groups depending on time of snow melt. other factors than time may however, influence this period, for example air temperature. our model shows a positive correlation between temperature and doymax in all vegetation types except grassland and fen meaning that high temperatures in the green-up period delays doymax. our only explanation to this phenomenon is as described above that too high temperatures may inhibit photosynthesis in the species growing close to their regional southern limit or increase flowering that will lower the build-up of green plant material. in conclusion we believe that the high r values for all vegetation types for the modelled doymax is due to a strong correlation with time of snow melt. climatic change in the zackenberg area climatic changes in the zackenberg area is currently believed to show mainly as increased temperatures and winter precipitation. increased precipitation will result in more snow hence prolonging the snow cover. this would lead towards decreasing greenness and later timing of the maximum. however, hinkler investigated the snow distribution during future climate scenarios and concluded that the projected increased precipitation and higher temperatures will leave summer-snow cover depletion close to status quo. recent years have shown examples of this where a deep snow cover quickly can melt away if the temperatures in the spring are higher than normal. in the case of year 2002; it had the next highest recorded snow depth at the end of winter but the snow melted off only one day later than the average melt off. further, the unclear response from the high arctic locations of walker et al. combined with the decrease in sindvi from to in our study enhances the need for further studies in these areas. also, the clear difference in the controlling variables and their induced response in the growth dynamics between vegetation types highlight the importance of high resolution studies. in regional climate change models the growth dynamics are often supplied by avhrr and satellites with similar resolution. a regional study based on recent higher resolution satellite data and based on the methods presented here would supply valuable new knowledge in this area – an area that clearly needs attention for future regional or global climate modelling.  although greening is reported to occur widespread in the arctic, we have shown that variations exist that might influence regional trends in the longer term. we investigated the growth dynamics for six dry, mesic and wet vegetation types using ndvi-fr and generalized additive mixed models. we found a quadratic model to be the best for simple and quick estimation of seasonal integrated ndvi-fr and used this in the analysis. snow melt and temperature were of major importance for the timing of the maximum growth as well as for the seasonal growth. more than 85% of the variance in timing of the maximum growth is explained by the models and similar for the seasonal growth of mesic and wet vegetation types. we found several non-linear growth responses to the environmental variables and conclude that the uses of gamms are valuable for investigating growth dynamics in the arctic. 
we find several non-linear growth responses to the environmental variables. snow melt and temperature are of major importance for the timing of the maximum growth as well as for the seasonal growth. more than 85% of the variance in timing of the maximum growth is explained by the models and similar for the seasonal growth of mesic and wet vegetation types. this paper presents an extensive study from zackenberg ecological research operations where snow melt, climate and growing season characteristics of six major high arctic vegetation types has been monitored during to we investigate the growth dynamics for dry, mesic and wet types using hand held measurements of far red normalised difference vegetation index and generalized additive mixed models. changes in land surface phenology are of major importance to the understanding of the impact of recent and future climate changes in the arctic. this indicates that although greening might occur wide-spread in the arctic there are variations on the local scale that might influence the regional trends on the longer term. we conclude that the uses of gamms are valuable for investigating growth dynamics in the arctic. contrary to several other studies in the arctic we found a significant decreasing trend of the seasonally integrated ndvi-fr in some vegetation types.
 changes in land surface phenology are of major importance to the understanding of the impact of recent and future climate changes in the arctic. this paper presents an extensive study from zackenberg ecological research operations where snow melt, climate and growing season characteristics of six major high arctic vegetation types has been monitored during to we investigate the growth dynamics for dry, mesic and wet types using hand held measurements of far red normalised difference vegetation index and generalized additive mixed models.  snow melt and temperature are of major importance for the timing of the maximum growth as well as for the seasonal growth. more than 85% of the variance in timing of the maximum growth is explained by the models and similar for the seasonal growth of mesic and wet vegetation types. we find several non-linear growth responses to the environmental variables.  we conclude that the uses of gamms are valuable for investigating growth dynamics in the arctic. contrary to several other studies in the arctic we found a significant decreasing trend of the seasonally integrated ndvi-fr in some vegetation types. this indicates that although greening might occur wide-spread in the arctic there are variations on the local scale that might influence the regional trends on the longer term. 