This study examined the temporal variation of the Normalized Difference Vegetation Index （NDVI） and its relationship with climatic factors in the Changbai Mountain Natural Reserve （CMNR） during 2000 - 2009. The re...This study examined the temporal variation of the Normalized Difference Vegetation Index （NDVI） and its relationship with climatic factors in the Changbai Mountain Natural Reserve （CMNR） during 2000 - 2009. The results showed as follows. The average NDVI values increased at a rate of 0.0024 year-1. The increase rate differed with vegetation types, such as 0.0034 year-1 for forest and 0.0017 year-1 for tundra. Trend analyses revealed a consistent NDVI increase at the start and end of the growing season but little variation or decrease observed in July during the study period. The NDVI in CMNR showed a stronger correlation with temperature than with precipitation, especially in spring and autumn. A stronger correlation was observed between NDVI and temperature in the tundra zone （2,000-2,600m） than in the coniferous forest （1,100-1,700m） and Korean pine-broadleaved mixed forest （7oo-1,1oom） zones. The results indicate that vegetation at higher elevations is more sensitive to temperature change. NDVI variation had a strong correlation with temperature change （r=0.7311, p〈0.01） but less significant correlation with precipitation change. The result indicates that temperature can serve as a main indicator of vegetation sensitivity in the CMNR.展开更多
It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations.To better manage the natural resources,and to better understand ...It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations.To better manage the natural resources,and to better understand the closely linked Earth systems,the concept of Digital Earth has been strongly promoted since US Vice President Al Gore’s speech in 1998.One core element of Digital Earth is the use and integration of remote sensing data.Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use,but also to detect more subtle changes related for instance to climate change.To uncover global change effects on vegetation activity and phenology,it is important to establish high quality time series characterizing the past situation against which the current state can be compared.With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution.Particular emphasis was put on noise removal.Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale.Without temporal smoothing,subtle spatio-temporal patterns in vegetation composition,density and phenology would be hidden by atmospheric noise and undetected clouds.Such noise is immanent in data that have undergone solely a maximum value compositing.Within the present study,the Whittaker smoother(WS)was applied to a SPOT VGT time series.The WS balances the fidelity to the observations with the roughness of the smoothed curve.The algorithm is extremely fast,gives continuous control over smoothness with only one parameter,and interpolates automatically.The filtering efficiently removed the negatively biased noise present in the original data,while preserving the overall shape of the curves showing vegetation growth and development.Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data.Analysis of the data also revealed spatially consistent key phenological markers.Extracted seasonality parameters followed a clear meridional trend.Compared to the unfiltered data,the filtered time series increased the separability of various land cover classes.It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.展开更多
基金supported by the Science and Technology Innovation Platforms Initiative of Northeast Normal University under the project "Ecological Security and Data Assemblage of the Changbai Mountains International Georegion(Project No.106111065202)"the National Grand Fundamental Research 973 Program of China (Project No.2009CB426305)
文摘This study examined the temporal variation of the Normalized Difference Vegetation Index （NDVI） and its relationship with climatic factors in the Changbai Mountain Natural Reserve （CMNR） during 2000 - 2009. The results showed as follows. The average NDVI values increased at a rate of 0.0024 year-1. The increase rate differed with vegetation types, such as 0.0034 year-1 for forest and 0.0017 year-1 for tundra. Trend analyses revealed a consistent NDVI increase at the start and end of the growing season but little variation or decrease observed in July during the study period. The NDVI in CMNR showed a stronger correlation with temperature than with precipitation, especially in spring and autumn. A stronger correlation was observed between NDVI and temperature in the tundra zone （2,000-2,600m） than in the coniferous forest （1,100-1,700m） and Korean pine-broadleaved mixed forest （7oo-1,1oom） zones. The results indicate that vegetation at higher elevations is more sensitive to temperature change. NDVI variation had a strong correlation with temperature change （r=0.7311, p〈0.01） but less significant correlation with precipitation change. The result indicates that temperature can serve as a main indicator of vegetation sensitivity in the CMNR.
文摘It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations.To better manage the natural resources,and to better understand the closely linked Earth systems,the concept of Digital Earth has been strongly promoted since US Vice President Al Gore’s speech in 1998.One core element of Digital Earth is the use and integration of remote sensing data.Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use,but also to detect more subtle changes related for instance to climate change.To uncover global change effects on vegetation activity and phenology,it is important to establish high quality time series characterizing the past situation against which the current state can be compared.With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution.Particular emphasis was put on noise removal.Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale.Without temporal smoothing,subtle spatio-temporal patterns in vegetation composition,density and phenology would be hidden by atmospheric noise and undetected clouds.Such noise is immanent in data that have undergone solely a maximum value compositing.Within the present study,the Whittaker smoother(WS)was applied to a SPOT VGT time series.The WS balances the fidelity to the observations with the roughness of the smoothed curve.The algorithm is extremely fast,gives continuous control over smoothness with only one parameter,and interpolates automatically.The filtering efficiently removed the negatively biased noise present in the original data,while preserving the overall shape of the curves showing vegetation growth and development.Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data.Analysis of the data also revealed spatially consistent key phenological markers.Extracted seasonality parameters followed a clear meridional trend.Compared to the unfiltered data,the filtered time series increased the separability of various land cover classes.It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.
