The Responses Of Forest Phenology On Climate Change In Changbai Mountains

Recently, more and more evidences have shown that global climate is becoming warmer and warmer, and vegetation phenology is an integraph of global changes and a comprehensive indicator of landscape and environment changes. The responses of vegetation phenology to global environment changes have become a focus field of global changes. Forest is an important part in global ecological system, and the forest phenology is the comprehensive biological index which reflect the influences of the short- and long-term climate changes on the forest growth stage. Remote sensing technology offer the technical means for monitoring the vegetation phenology on landscape scale, and realize the spatial transition of phenological data from points to coverage.Combing with ten-day SPOT/NDVI data, meteorological data and varities of statistic data using GIS and remote sensing technology, the paper constructs a double logistic model, and calculate forest phenology metrics based on it, then analysize the spatio-temporal patterns and change trends of the start of growing season (SOS), the end of growing season (EOS), the length of growing season (LOS), annual DN (digital number) max, annual DN amplitude and DN integral of growing period in Changbai Mountains during 1999~2008. Subsequently, the paper evaluates the characteristics of climate change in Changbai Mountains, and analysize the relationship between growing season variation and climate factors, then discuss the response and feedback mechanism of vegetation phenology metrics to regional air temperature and precipitation in different vegetation types. From these researches, some basic conclusions are drawn as follows:1 Double logistic model constructionBased on remote sensing and GIS, the paper develop a double logistic model that combines the merits of logistic model and global function. The model can not only extract vegetation phenophases for several years simultaneously, but also avoid the boundary effects of time-series curve. With this feature, the data obtained are more flexible and complicated for fitting curves. It is more proper to fit the both sides of curve using dynamic ratios for the SOS and EOS being different biophysical processes. Moreover, the model treats each pixel individually without setting absolute thresholds or empirical constants, in which way, the model can characterize the physical meaning of different pixels more accurately. Validation through comparing with the field data and previous research results demonstrate that double logistic model used in this paper is appropriate for the study of vegetation phenology in Changbai Mountains.2 Temporal-spatial patterns of forest phenology metrics in Changbai Mountains (1) During the year 1999~2008, the SOS of forest in Changbai Mountains commences on the 100th to 140th day of year, which is consistent with the period of leaf unfolding for forest in late April and early May. Among of the period the day 100~110 plus the day 110~120 take a greater proportion. The advance areas of SOS are small from the whole aspects, only accounting for 32.46% over the study area, and the average advance ratio is about 0.71 d/yr; The delay areas of SOS take up to 67.54%, and the average delay ratio is about 0.43 d/yr.(2) The EOS concentrate on the day 270~290, of which the day 280~290 takes a greater proportion, it is corresponding to the period of forest defoliation in the fall. The delay areas of EOS take a greater proportion, about 65.3%, which chiefly centralize at the middle-southern area, and the average delay ratio is about 0.53 d/yr. Meanwhile, the advance areas of EOS distribute in the southern extrem and northern part, which occupy 34.7% of whole study area, and the average advance ratio is about 0.57 d/yr.(3) The LOS of forest mainly range from 140 to 180 days, of which the period 160~180 takes a greater proportion. The LOS prolong in middle-eastern areas and shorten in northwestern areas, and the spatial pattern present as Southeast-Northwest discrepancy. The prolonged areas and shortened areas occupy 59.69% and 40.31%, and the prolonged ratio and the shortened ratio are 0.67 d/yr and 1 d/yr, respectively.(4) DN integral of growing period, annual DN max and annual DN amplitude all express increasing trend, and their spatial patterns are similar, because the three metrics are correlated. If LOS keeps invariant, the increase of annual DN max or annual amplitude will result in the increase of DN integral of growing period. If both LOS and base DN keep invariant, annual DN max and annual DN amplitude will have the same meaning.3 Characteristic of climate change in Changbai MountainsConsidering the annual mean temperature and annual precipitation variation, the decadal climate variation have gone through one process:“cold and wet– cold and dry– warm and wet– warm and dry”, and the last period will persist in the future. From the perspective of spatial pattern, the distribution and change ampltude of annual mean temperature and mean temperature in spring are similar, but obvious differences exist between other seasons. The precipitation distribution in spring and in autumn is similar, nothing but the rainfall increasing areas in autumn are little larger than that in sping. The spatial pattern of annual precipitation is consistent with the overlay of precipitation distribution in spring and in autumn.4 Relationship beween climate and the variations of forest growing seasonThe correlation coefficient and partial correlation coefficient are high between decad NDVI and decad precipitation/temperature in most regions of Changbai Mountains, and the influnence of temperature on forest growing process are more significant than that of precipitation. There are a significant negative correlation between the SOS and the average monthly temperature before SOS in majority of various vegetation types, and the EOS have a significant positive correlation with the average monthly temperature before EOS and after SOS. The relationship display relatively complex, the SOS of coniferous forest have a positive correlation with average monthly precipitation before SOS, and it is opposite to broadleave forest. The EOS of each forest types express negative correlation with annual precipitation before EOS and after SOS, except Quercus mongolica.