Different government departments and researchers have paid considerable atten- tion at various levels to improving the eco-environment in ecologically fragile areas. Over the past decade, large numbers of people have ...Different government departments and researchers have paid considerable atten- tion at various levels to improving the eco-environment in ecologically fragile areas. Over the past decade, large numbers of people have emigrated from rural areas as a result of the rapid urbanization in Chinese society. The question then remains： to what extent does this migra- tion affect the regional vegetation greenness in the areas that people have moved from？ Based on normalized difference vegetation index （NDVI） data with a resolution of 1 km, as well as meteorological data and socio-economic data from 2000 to 2010 in Inner Mongolia, the spatio-temporal variation of vegetation greenness in the study area was analyzed via trend analysis and significance test methods. The contributions of human activities and natural factors to the variation of vegetation conditions during this period were also quantita- tively tested and verified, using a multi-regression analysis method. We found that： （1）the vegetation greenness of the study area increased by 10.1% during 2000-2010. More than 28% of the vegetation greenness increased significantly, and only about 2% decreased evi- dently during the study period. （2） The area with significant degradation showed a banded distribution at the northern edge of the agro-pastoral ecotone in central Inner Mongolia. This indicates that the eco-environment is still fragile in this area, which should be paid close at- tention. The area where vegetation greenness significantly improved showed a concentrated distribution in the southeast and west of Inner Mongolia. （3） The effect of agricultural labor on vegetation greenness exceeded those due to natural factors （i.e. precipitation and tempera- ture）. The emigration of agricultural labor improved the regional vegetation greenness sig- nificantly.展开更多
In Northeast Thailand, the climate change has resulted in erratic rainfall and tem- perature patterns. The region has experienced both periods of drought and seasonal floods with the increasing severity. This study in...In Northeast Thailand, the climate change has resulted in erratic rainfall and tem- perature patterns. The region has experienced both periods of drought and seasonal floods with the increasing severity. This study investigated the seasonal variation of vegetation greenness based on the Normalized Difference Vegetation Index （NDVI） in major land cover types in the region. An assessment of the relationship between climate patterns and vegeta- tion conditions observed from NDVI was made. NDVI data were collected from year 2001 to 2009 using multi-temporal Terra MODIS Vegetation Indices Product （MOD13Q1）. NDVI pro- files were developed to measure vegetation dynamics and variation according to land cover types. Meteorological information, i.e. rainfall and temperature, for a 30 year time span from 1980 to 2009 was analyzed for their patterns. Furthermore, the data taken from the period of 2001-2009, were digitally encoded into GIS database and the spatial patterns of monthly rainfall and temperature maps were generated based on kriging technique. The results showed a decreasing trend in NDVI values for both deciduous and evergreen forests. The highest productivity and biomass were observed in dry evergreen forests and the lowest in paddy fields. Temperature was found to be increasing slightly from 1980 to 2009 while no significant trends in rainfall amounts were observed. In dry evergreen forest, NDVI was not correlated with rainfall but was significant negatively correlated with temperature. These re- sults indicated that the overall productivity in dry evergreen forest was affected by increasing temperatures. A vegetation greenness model was developed from correlations between NDVI and meteorological data using linear regression. The model could be used to observe the change in vegetation greenness and dynamics affected by temperature and rainfall.展开更多
基金Projects of International Cooperation and Exchanges NSFC,No.41161140352The Major Research Plan of the National Natural Science Foundation of China,No.91325302National Natural Science Foundation of China,No.41271119
文摘Different government departments and researchers have paid considerable atten- tion at various levels to improving the eco-environment in ecologically fragile areas. Over the past decade, large numbers of people have emigrated from rural areas as a result of the rapid urbanization in Chinese society. The question then remains： to what extent does this migra- tion affect the regional vegetation greenness in the areas that people have moved from？ Based on normalized difference vegetation index （NDVI） data with a resolution of 1 km, as well as meteorological data and socio-economic data from 2000 to 2010 in Inner Mongolia, the spatio-temporal variation of vegetation greenness in the study area was analyzed via trend analysis and significance test methods. The contributions of human activities and natural factors to the variation of vegetation conditions during this period were also quantita- tively tested and verified, using a multi-regression analysis method. We found that： （1）the vegetation greenness of the study area increased by 10.1% during 2000-2010. More than 28% of the vegetation greenness increased significantly, and only about 2% decreased evi- dently during the study period. （2） The area with significant degradation showed a banded distribution at the northern edge of the agro-pastoral ecotone in central Inner Mongolia. This indicates that the eco-environment is still fragile in this area, which should be paid close at- tention. The area where vegetation greenness significantly improved showed a concentrated distribution in the southeast and west of Inner Mongolia. （3） The effect of agricultural labor on vegetation greenness exceeded those due to natural factors （i.e. precipitation and tempera- ture）. The emigration of agricultural labor improved the regional vegetation greenness sig- nificantly.
基金supported by the Faculty of Engineering and the Higher Education Research Promotion and National Research University Project of ThailandOffice of the Higher Education Commission and the Faculty of Engineering,Khon Kaen University,Thailand
文摘In Northeast Thailand, the climate change has resulted in erratic rainfall and tem- perature patterns. The region has experienced both periods of drought and seasonal floods with the increasing severity. This study investigated the seasonal variation of vegetation greenness based on the Normalized Difference Vegetation Index （NDVI） in major land cover types in the region. An assessment of the relationship between climate patterns and vegeta- tion conditions observed from NDVI was made. NDVI data were collected from year 2001 to 2009 using multi-temporal Terra MODIS Vegetation Indices Product （MOD13Q1）. NDVI pro- files were developed to measure vegetation dynamics and variation according to land cover types. Meteorological information, i.e. rainfall and temperature, for a 30 year time span from 1980 to 2009 was analyzed for their patterns. Furthermore, the data taken from the period of 2001-2009, were digitally encoded into GIS database and the spatial patterns of monthly rainfall and temperature maps were generated based on kriging technique. The results showed a decreasing trend in NDVI values for both deciduous and evergreen forests. The highest productivity and biomass were observed in dry evergreen forests and the lowest in paddy fields. Temperature was found to be increasing slightly from 1980 to 2009 while no significant trends in rainfall amounts were observed. In dry evergreen forest, NDVI was not correlated with rainfall but was significant negatively correlated with temperature. These re- sults indicated that the overall productivity in dry evergreen forest was affected by increasing temperatures. A vegetation greenness model was developed from correlations between NDVI and meteorological data using linear regression. The model could be used to observe the change in vegetation greenness and dynamics affected by temperature and rainfall.
