Phenological Changes Of Vegetation In The Mu Us Sandy Land And Its Response To Climate Change

Vegetation phenology is a sensitive index for terrestrial ecosystem of respond to climate change.Therefore,accurate monitoring of vegetation phenology on global and regional scales is of far-reaching significance for understanding the interaction between climate and biosphere.However,various NDVI data sets used to monitor vegetation phenology have shortcomings in time and space resolution.It will be very beneficial to study vegetation phenology to fuse different sensor data and combine the advantages of each data set.Therefore,this study takes Maowusu sandy land as the research area,uses GIMMS NDVI3g and MOD 13Q1 data,compares STNLFFM,ESTARFM and FSDAF three time-space data fusion algorithms,and selects STNLFFM fusion algorithm to fuse two different sensor data to form NDVI time series data with high resolution for a long time.Further comparing the three fitting and reconstruction methods of S-G,D-L and AG,finally choosing AG fitting method to denoise NDVI data,extracting three phenological parameters:Start of Growing Season(SOS),End of Growing Season(EOS)and Length of Growing season(LOS),and analyzing the temporal and spatial variation characteristics of vegetation phenology.The research conclusions are as follows:(1)The results of three fusion methods show that the fusion accuracy of ESTARFM and FSDAF is better than the STNLFFM in the heterogeneity region.The fusion accuracy of STNLFFM is relatively higher than the other two methods in the homogeneity region.In addition,STNLFFM’s spatio-temporal fusion results in different seasons and years have little difference and are less affected by spatio-temporal differences.Generally speaking,the fusion accuracy based on non-local filtering fusion algorithm(STNLFFM)is the highest,followed by ESTARFM and FSDAF.Therefore,STNLFFM method is selected to fuse GIMMS NDVI and MODIS NDVI data.(2)Compared with the initial NDVI time series curve,the quality of NDVI time series curve fitted by three phenological fitting reconstruction methods,namely S-G filtering method,AG fitting method and D-L fitting method,is improved.All the noise has been removed in different degrees,among which AG fitting method and D-L fitting method are better for smoothing the time series curve,and S-G filtering method is closer to the original curve,but the noise removal effect is not significant.AG fitting method has the best correlation with the original curve in the whole year and growing season,and is more suitable for fitting and reconstructing the vegetation NDVI time series curve in Mu Us sandy land.(3)SOS of vegetation in Mu Us Sandy Land is mainly concentrated in the 130th-15th day of a year,EOS is mainly concentrated in the 300th-30th day and GSL is mainly concentrated in the 150-16th day.There are obvious altitude differences in vegetation phenology space.The vegetation in low altitude areas of Mu Us Sandy Land enters the growing season first,while the vegetation in high altitude areas enters the growing season later.EOS has obvious difference between north and south in space.The growing season of vegetation in the northern part of the study area ended earlier,generally from late September to early October,and the growing season of vegetation in the southern part of the study area ended from late October to early November.GSL is influenced by vegetation SOS and vegetation EOS,with the longest growing season in Shenmu and Yulin at low altitude in Maowusu Sandy Land,and the shortest growing season in Ejinhoro Banner,Otog Banner,Lingwu,Dingbian and Jingbian at high altitude.The reason may be that there are many kinds of vegetation on the edge of sandy land or in low-lying areas.The hillside vegetation at high altitude has single species and weak growth ability,and its growth and development are sensitive to hydrothermal conditions and climate change.The phenological change trend of vegetation in Mu Us Sandy Land also has obvious segmentation characteristics.From 1982 to 1999,vegetation SOS showed a delayed trend,from 1999 to 2008,it showed an advanced trend,and from 2008 to 2019,the advanced trend slowed down.EOS showed an advance trend in 1982-1995,accelerated in 1995-2004 and delayed in 2004-2019.The change trend of vegetation phenology in Mu Us Sandy Land is obviously dependent on altitude.With the increase of altitude,the beginning of the growing season of vegetation is delayed and the length of growing season is shortened.In low altitude areas,the SOS trend of vegetation is the most significant,the GSL trend of vegetation is the most significant,and the EOS change of vegetation is not significant.(4)The response analysis of vegetation phenology to climate change in Mu Us Sandy Land shows that there is a significant correlation between vegetation phenology and temperature and precipitation.Different monthly precipitation has different effects on SOS,and SOS is significantly correlated with spring pre-season temperature and spring pre-season precipitation.EOS is significantly correlated with autumn pre-season temperature,pre-season precipitation and September precipitation.EOS has the strongest correlation with precipitation in September,and has a certain lag.The lower altitude area is sensitive to the temperature,and the rising of the temperature is beneficial for plants to avoid frost.Vegetation phenology in higher altitude areas responded significantly to changes in temperature and precipitation.When the precipitation is insufficient,the unilateral increase of temperature intensifies evaporation,resulting in water loss,which leads to the delay of SOS and the advance of EOS.When the precipitation increases significantly,the combined conditions of water and heat in these areas are improved,which leads to the delay of SOS and EOS.