Affected by climate change and policy factors, Kazakhstan is the country with the most severe ecological degradation and grassland conflicts in Central Asia. Therefore, studying the state of grassland carrying resources in Kazakhstan is particularly important for understanding the responses of grassland ecosystems to climate change and human activities. Based on Kazakhstan's remote sensing data and animal husbandry statistics, this study analyzes the patterns of changes in grassland ecosystems in Kazakhstan based on the supply and consumption of these ecosystems. The results show that: 1) From 2003 to 2017, the number of livestock raised in Kazakhstan showed a trend of sustained and steady growth. Due to freezing damage, the scale of livestock farming decreased in 2011, but a spatial difference in the livestock farming structure was not obvious. 2) The fluctuation of grassland supply in Kazakhstan has increased, while the consumption due to animal husbandry has also continued to increase, resulting in an increasing pressure on the grassland carrying capacity. 3) Between 2003 and 2017, the overall grassland carrying status of Kazakhstan have been abundant, but the grassland carrying pressure index has shown a steadily increasing trend, the grassland carrying pressure is growing, and it is mainly determined by grassland productivity. The greater pressure in lower Kyzylorda state, the southern Kazakhstan state of the cultivated land and the northern Kazakhstan state has gradually expanded to include the agro-pastoral zone and the semi-desert zone. 相似文献
We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.