Analysis Of Vegetation Seasonal Variation In Yanhe River Basin Based On Spatial And Temporal Resolution Fusion Technology

Spatial and temporal resolution fusion technology can merge different temporal resolution and spatial resolution of multi-source remote sensing satellite images to get high spatial and high temporal resolution remote sensing image,and then satisfy high space-time resolution of changes in vegetation monitoring research.The spatial resolution fusion model is mainly used for the fusion of reflectivity data,and the vegetation index obtained through multi-spectral band operation is closely related to the growth state of vegetation.In this paper,based on the spatial and temporal resolution fusion technology,synthesizing vegetation index time series data can be long reflect the status of vegetation growth,providing the optimal data source for the dynamic monitoring of vegetation change.This paper selects the Yanhe river basin which is typical hilly-gully region as study area,and selects Landsat OLI and MODIS09A1 reflectivity synthetic data as data sources,using BI and IB fusion algorithm to generate the fusion image.Firstly to evaluate the precision of the fusion results,and to build six vegetation index of time series data,on the basis of the optimal vegetation index NDVI,using the HANTS smoothing NDVI time-series data,and analyzing the seasonal variation characteristics of different vegetation types,finally analyzing the monthly average temperature and accumulative total precipitation and the relationship between different vegetation,to discusses the influence of meteorological factors with vegetation.The basic conclusions are as follows:(1)The IB fusion results are more accurate.Comparing the results of BI and IB fusion,the IB images can better reflect the features of true ground and texture,which are closer to the real image.The mean difference and standard deviation between the real image and IB image are both smaller,the correlation coefficient with the real image is the highest.In space,the vegetation index of IB fusion is almost all positive correlation distribution,and the significance t-value test is adopted.The low correlation coefficients of different indexes are mainly distributed in cloud cover area and the gully region affected by the BRDF.Among them,the TSAVI index has the highest spatial correlation,with a positive correlation ratio of 85.1% and a highly positive correlation of 49.1%.By comparing the simple correlation coefficient,the spatial correlation coefficient reveals the change of the fusion VI image and the real VI image in space.(2)The changes of NDVI time series data obtained by ESTARFM are consistent with the growth characteristics of corresponding vegetation types,the result can reflect the seasonal variation characteristics of different vegetation types.Through selecting NDVI as optimal index to reflect the vegetation growth state,based on the HANTS smoothing method to reconstruct the NDVI time-series data of the Yanhe river basin in 2015,the type conclude cultivated land,forest land,shrub land,high coverage grassland,low coverage grassland.NDVI time series data is consistent with the growth characteristics of corresponding vegetation types over time and can reflect the seasonal variation characteristics of different vegetation types.Among them,the NDVI growth rate of forest in the spring is the largest,and the NDVI value is the highest in summer.The NDVI of high vegetation coverage grassland is located under woodland.In autumn,NDVI of cultivated land declines the fastest.The NDVI curve of shrub land is similar to that of forest land.Due to the difference of coverage,The time sequence curve of low and medium coverage grassland is relatively gentle.Among all vegetation types,its NDVI is the lowest.(3)Meteorological factors have a positive correlation with the NDVI of vegetation,while the vegetation has a hysteresis effect.Correlation between different vegetation types of NDVI and meteorological factors(temperature and precipitation factor)respectively were analyzed,and the results show that the NDVI value was positively correlation with average temperature and the accumulative rainfall during the month,and last month,the average temperature and precipitation has certain positive correlation.