GIMMS （Global Inventory Modeling and Mapping Studies） NDVI （Normalised Difference Vegetation Index） from 1982 to 2006 and MODIS （Moderate Resolution Imaging Spectroradiometer） NDVI from 2001 to 2010 were blended...GIMMS （Global Inventory Modeling and Mapping Studies） NDVI （Normalised Difference Vegetation Index） from 1982 to 2006 and MODIS （Moderate Resolution Imaging Spectroradiometer） NDVI from 2001 to 2010 were blended to extract the, grass coverage and analyze its spatial pattern. The response of grass coverage to climatic variations at annual and monthly time scales was analyzed. Grass coverage distribution had increased from northwest to southeast across China. During 1982-2010, the mean nationwide grass coverage was 34% but exhibited apparent spatial heterogeneity, being the highest （61.4%） in slope grasslands and the lowest （17.1%） in desert grasslands. There was a slight increase of the grass coverage with a rate of 0.17% per year. Increase in slope grasslands coverage was as high as 0.27% per year, while in the plain grasslands and meadows the grass coverage in- crease was the lowest （being 0.11% per year and 0.1% per year, respectively）. Across China, the grass coverage with extremely significant increase （P〈0.01） and significant increase （P〈0.05） accounted for 46.03% and 11% of the total grassland area, respectively, while those with extremely significant and significant decrease accounted for only 4.1% and 3.24%, respectively. At the annual time scale, there are no significant correlations between grass coverage and annual mean temperature and precipitation. However, the grass coverage was somewhat affected by temperature in alpine and sub-alpine grassland, alpine and sub-alpine meadow, slope grassland and meadow, while grass coverage in desert grassland and plain grassland was more affected by precipitation. At the monthly time-scale, there are significant correlations between grass coverage with both temperature and precipitation, indicating that the grass coverage is more affected by seasonal fluctuations of hydrothermal conditions. Additionally, there is one-month time lag-effect between grass coverage and climate factors for each grassland types.展开更多
基金The National Natural Science Foundation of China, No.41271361 National Basic Research Program of China, No.2010CB950702+2 种基金 The APN Projects, No.ARCP2013-16NMY-Li The Public Sector Linkages Program supported by AusAID, No.64828 China＇s High-tech Special Projects, No.2007AA 10Z231
文摘GIMMS （Global Inventory Modeling and Mapping Studies） NDVI （Normalised Difference Vegetation Index） from 1982 to 2006 and MODIS （Moderate Resolution Imaging Spectroradiometer） NDVI from 2001 to 2010 were blended to extract the, grass coverage and analyze its spatial pattern. The response of grass coverage to climatic variations at annual and monthly time scales was analyzed. Grass coverage distribution had increased from northwest to southeast across China. During 1982-2010, the mean nationwide grass coverage was 34% but exhibited apparent spatial heterogeneity, being the highest （61.4%） in slope grasslands and the lowest （17.1%） in desert grasslands. There was a slight increase of the grass coverage with a rate of 0.17% per year. Increase in slope grasslands coverage was as high as 0.27% per year, while in the plain grasslands and meadows the grass coverage in- crease was the lowest （being 0.11% per year and 0.1% per year, respectively）. Across China, the grass coverage with extremely significant increase （P〈0.01） and significant increase （P〈0.05） accounted for 46.03% and 11% of the total grassland area, respectively, while those with extremely significant and significant decrease accounted for only 4.1% and 3.24%, respectively. At the annual time scale, there are no significant correlations between grass coverage and annual mean temperature and precipitation. However, the grass coverage was somewhat affected by temperature in alpine and sub-alpine grassland, alpine and sub-alpine meadow, slope grassland and meadow, while grass coverage in desert grassland and plain grassland was more affected by precipitation. At the monthly time-scale, there are significant correlations between grass coverage with both temperature and precipitation, indicating that the grass coverage is more affected by seasonal fluctuations of hydrothermal conditions. Additionally, there is one-month time lag-effect between grass coverage and climate factors for each grassland types.
