Understanding the response of vegetation variation to climate change and human activities is critical for addressing future conflicts between humans and the environment,and maintaining ecosystem stability.Here,we aime...Understanding the response of vegetation variation to climate change and human activities is critical for addressing future conflicts between humans and the environment,and maintaining ecosystem stability.Here,we aimed to identify the determining factors of vegetation variation and explore the sensitivity of vegetation to temperature(SVT)and the sensitivity of vegetation to precipitation(SVP)in the Shiyang River Basin(SYRB)of China during 2001-2022.The climate data from climatic research unit(CRU),vegetation index data from Moderate Resolution Imaging Spectroradiometer(MODIS),and land use data from Landsat images were used to analyze the spatial-temporal changes in vegetation indices,climate,and land use in the SYRB and its sub-basins(i.e.,upstream,midstream,and downstream basins)during 2001-2022.Linear regression analysis and correlation analysis were used to explore the SVT and SVP,revealing the driving factors of vegetation variation.Significant increasing trends(P<0.05)were detected for the enhanced vegetation index(EVI)and normalized difference vegetation index(NDVI)in the SYRB during 2001-2022,with most regions(84%)experiencing significant variation in vegetation,and land use change was determined as the dominant factor of vegetation variation.Non-significant decreasing trends were detected in the SVT and SVP of the SYRB during 2001-2022.There were spatial differences in vegetation variation,SVT,and SVP.Although NDVI and EVI exhibited increasing trends in the upstream,midstream,and downstream basins,the change slope in the downstream basin was lower than those in the upstream and midstream basins,the SVT in the upstream basin was higher than those in the midstream and downstream basins,and the SVP in the downstream basin was lower than those in the upstream and midstream basins.Temperature and precipitation changes controlled vegetation variation in the upstream and midstream basins while human activities(land use change)dominated vegetation variation in the downstream basin.We concluded that there is a spatial heterogeneity in the response of vegetation variation to climate change and human activities across different sub-basins of the SYRB.These findings can enhance our understanding of the relationship among vegetation variation,climate change,and human activities,and provide a reference for addressing future conflicts between humans and the environment in the arid inland river basins.展开更多
Land cover change is a major challenge for many developing countries. Spatiotemporal information on this change is essential for monitoring global terrestrial ecosystem carbon, climate and biosphere exchange, and land...Land cover change is a major challenge for many developing countries. Spatiotemporal information on this change is essential for monitoring global terrestrial ecosystem carbon, climate and biosphere exchange, and land use management. A combination of LST and the EVI indices in the global disturbance index (DI) has been proven to be useful for detecting and monitoring of changes in land covers at continental scales. However, this model has not been adequately applied or assessed in tropical regions. We aimed to demonstrate and evaluate the DI algorithm used to detect spatial change in land covers in Lao tropical forests. We used the land surface temperature and enhanced vegetation index of the Moderate Resolution Imaging Spectroradiometer time-series products from 2006-2012. We used two dates Google EarthTM images in 2006 and 2012 as ground truth data for accuracy assessment of the model. This research demonstrated that the DI was capable of detecting vegetation changes during seven-year periods with high overall accuracy;however, it showed low accuracy in detecting vegetation decrease.展开更多
According to the time series data of Enhanced Vegetation Index (EVI) in Four-Lake Area of Jianghan Plain during the period 2001-2007, we use Harmonic Analysis of Time Series (HANTS) to conduct cloud removing processin...According to the time series data of Enhanced Vegetation Index (EVI) in Four-Lake Area of Jianghan Plain during the period 2001-2007, we use Harmonic Analysis of Time Series (HANTS) to conduct cloud removing processing, and calculate the sum of square N of time series value of each pixel. The pixels with N>0.25 are classified as vegetation coverage area; the pixels with N<0.25 are classified as non-vegetation coverage area. As to vegetation coverage area, we use the second-order difference method to judge the frequency of peak value of EVI time series data. Within one year, the vegetation coverage area with peak value happening 1 time is woodland and grassland; the vegetation coverage area with peak value happening 2 times is arable land; the vegetation coverage area with peak value happening 3 times or more is vegetable land. Supervised classification method is used to identify cities, towns, water area in non-vegetation coverage area and woodland, grassland in vegetation coverage area. We draw the land cover classification diagram of Four-Lake Area in the period 2001-2007. In comparison with the land cover classification based on multitemporal ETM data in 2001, the difference of area of arable land is within 10%. Using MODIS-EVI data, we can rapidly and efficiently conduct land cover classification with low cost. The dynamic analysis results indicate that the area of arable land is in the process of declining, while the area of other cover types shows an increasing trend.展开更多
为了了解西北地区MODIS-NDVI和MODIS-EVI两种植被指数的特点，本文利用美国NASA LP DAAC（Land Process Distributed Active Archive Center）2004年1～12月的250m分辨率16天植被指数合成的MOD13 Q1数据集，对西北地区不同类型植被NDVI...为了了解西北地区MODIS-NDVI和MODIS-EVI两种植被指数的特点，本文利用美国NASA LP DAAC（Land Process Distributed Active Archive Center）2004年1～12月的250m分辨率16天植被指数合成的MOD13 Q1数据集，对西北地区不同类型植被NDVI和EVI的特征进行分析，并对西北地区MODIS-NDVI饱和问题进行了初步研究。结果表明：NDVI和EVI对干旱-半干旱气候区植被覆盖度不高的植被类型描述能力相似，月际变化趋势一致。西北地区各种植被类型NDVI比EVI高，NDVI与EVI的差异总体上呈现从半荒漠、草原、农区到林区，随NDVI值的增加而增大的规律。对植被度覆盖度高的阔叶林和针叶林，在植被生长旺盛期，NDVI总在0．8附近波动，NDVI随植被的生长增加的很小，一直维持在一个高且平的范同内，不再能看出植被生长变化的现象，即饱和现象严重；而EVI表现良好，随着植被的生长而增加，能明显地反映出植被生长的季节变化。西北高寒草甸和陕西关中农业区NDVI也出现有不同程度的饱和，饱和时间因植被的不同从1～2月不等。0．8可作为NDVI饱和的阈值。NDVI饱和问题对卫星监测植被的研究和应用会产生误差，EVI能较好地解决NDVI的饱和问题。展开更多
基金National Natural Science Foundation of China(42230720).
文摘Understanding the response of vegetation variation to climate change and human activities is critical for addressing future conflicts between humans and the environment,and maintaining ecosystem stability.Here,we aimed to identify the determining factors of vegetation variation and explore the sensitivity of vegetation to temperature(SVT)and the sensitivity of vegetation to precipitation(SVP)in the Shiyang River Basin(SYRB)of China during 2001-2022.The climate data from climatic research unit(CRU),vegetation index data from Moderate Resolution Imaging Spectroradiometer(MODIS),and land use data from Landsat images were used to analyze the spatial-temporal changes in vegetation indices,climate,and land use in the SYRB and its sub-basins(i.e.,upstream,midstream,and downstream basins)during 2001-2022.Linear regression analysis and correlation analysis were used to explore the SVT and SVP,revealing the driving factors of vegetation variation.Significant increasing trends(P<0.05)were detected for the enhanced vegetation index(EVI)and normalized difference vegetation index(NDVI)in the SYRB during 2001-2022,with most regions(84%)experiencing significant variation in vegetation,and land use change was determined as the dominant factor of vegetation variation.Non-significant decreasing trends were detected in the SVT and SVP of the SYRB during 2001-2022.There were spatial differences in vegetation variation,SVT,and SVP.Although NDVI and EVI exhibited increasing trends in the upstream,midstream,and downstream basins,the change slope in the downstream basin was lower than those in the upstream and midstream basins,the SVT in the upstream basin was higher than those in the midstream and downstream basins,and the SVP in the downstream basin was lower than those in the upstream and midstream basins.Temperature and precipitation changes controlled vegetation variation in the upstream and midstream basins while human activities(land use change)dominated vegetation variation in the downstream basin.We concluded that there is a spatial heterogeneity in the response of vegetation variation to climate change and human activities across different sub-basins of the SYRB.These findings can enhance our understanding of the relationship among vegetation variation,climate change,and human activities,and provide a reference for addressing future conflicts between humans and the environment in the arid inland river basins.
文摘Land cover change is a major challenge for many developing countries. Spatiotemporal information on this change is essential for monitoring global terrestrial ecosystem carbon, climate and biosphere exchange, and land use management. A combination of LST and the EVI indices in the global disturbance index (DI) has been proven to be useful for detecting and monitoring of changes in land covers at continental scales. However, this model has not been adequately applied or assessed in tropical regions. We aimed to demonstrate and evaluate the DI algorithm used to detect spatial change in land covers in Lao tropical forests. We used the land surface temperature and enhanced vegetation index of the Moderate Resolution Imaging Spectroradiometer time-series products from 2006-2012. We used two dates Google EarthTM images in 2006 and 2012 as ground truth data for accuracy assessment of the model. This research demonstrated that the DI was capable of detecting vegetation changes during seven-year periods with high overall accuracy;however, it showed low accuracy in detecting vegetation decrease.
基金Supported by National Natural Science Foundation of China(40971113)Innovative Group Project of Natural Science Foundation of Hubei Province (2006ABC013)
文摘According to the time series data of Enhanced Vegetation Index (EVI) in Four-Lake Area of Jianghan Plain during the period 2001-2007, we use Harmonic Analysis of Time Series (HANTS) to conduct cloud removing processing, and calculate the sum of square N of time series value of each pixel. The pixels with N>0.25 are classified as vegetation coverage area; the pixels with N<0.25 are classified as non-vegetation coverage area. As to vegetation coverage area, we use the second-order difference method to judge the frequency of peak value of EVI time series data. Within one year, the vegetation coverage area with peak value happening 1 time is woodland and grassland; the vegetation coverage area with peak value happening 2 times is arable land; the vegetation coverage area with peak value happening 3 times or more is vegetable land. Supervised classification method is used to identify cities, towns, water area in non-vegetation coverage area and woodland, grassland in vegetation coverage area. We draw the land cover classification diagram of Four-Lake Area in the period 2001-2007. In comparison with the land cover classification based on multitemporal ETM data in 2001, the difference of area of arable land is within 10%. Using MODIS-EVI data, we can rapidly and efficiently conduct land cover classification with low cost. The dynamic analysis results indicate that the area of arable land is in the process of declining, while the area of other cover types shows an increasing trend.
文摘为了了解西北地区MODIS-NDVI和MODIS-EVI两种植被指数的特点，本文利用美国NASA LP DAAC（Land Process Distributed Active Archive Center）2004年1～12月的250m分辨率16天植被指数合成的MOD13 Q1数据集，对西北地区不同类型植被NDVI和EVI的特征进行分析，并对西北地区MODIS-NDVI饱和问题进行了初步研究。结果表明：NDVI和EVI对干旱-半干旱气候区植被覆盖度不高的植被类型描述能力相似，月际变化趋势一致。西北地区各种植被类型NDVI比EVI高，NDVI与EVI的差异总体上呈现从半荒漠、草原、农区到林区，随NDVI值的增加而增大的规律。对植被度覆盖度高的阔叶林和针叶林，在植被生长旺盛期，NDVI总在0．8附近波动，NDVI随植被的生长增加的很小，一直维持在一个高且平的范同内，不再能看出植被生长变化的现象，即饱和现象严重；而EVI表现良好，随着植被的生长而增加，能明显地反映出植被生长的季节变化。西北高寒草甸和陕西关中农业区NDVI也出现有不同程度的饱和，饱和时间因植被的不同从1～2月不等。0．8可作为NDVI饱和的阈值。NDVI饱和问题对卫星监测植被的研究和应用会产生误差，EVI能较好地解决NDVI的饱和问题。
