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This study analyzed the spatial and temporal variations in the Normalized Difference Vegetation Index (NDVI) on the Mongolian Plateau from 1982-2013 using Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data and explored the effects of climate factors and human activities on vegetation. The results indicate that NDVI has slight upward trend in the Mongolian Plateau over the last 32 years. The area in which NDVI increased was much larger than that in which it decreased. Increased NDVI was primarily distributed in the southern part of the plateau, especially in the agro-pastoral ecotone of Inner Mongolia. Improvement in the vegetative cover is predicted for a larger area compared to that in which degradation is predicted based on Hurst exponent analysis. The NDVI-indicated vegetation growth in the Mongolian Plateau is a combined result of climate variations and human activities. Specifically, the precipitation has been the dominant factor and the recent human effort in protecting the ecological environments has left readily detectable imprints in the NDVI data series.

Long-term remote sensing normalized difference vegetation index (NDVI) datasets have been widely used in monitoring vegetation changes. In this study, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g dataset was used as the data source, and the dimidiate pixel model, intensity analysis, and residual analysis were used to analyze the changes of vegetation coverage in Inner Mongolia-from 1982 to 2010-and their relationships with climate and human activities. This study also explored vegetation changes in Inner Mongolia with respect to natural factors and human activities. The results showed that the estimated vegetation coverage exhibited a high correlation (0.836) with the actual measured values. The increased vegetation coverage area (49.2% of the total area) was larger than the decreased area (43.3%) from the 1980s to the 1990s, whereas the decreased area (57.1%) was larger than the increased area (35.6%) from the 1990s to the early 21st century. This finding indicates that vegetation growth in the 1990s was better than that in the other two decades. Intensity analysis revealed that changes in the average annual rate from the 1990s to the early 21st century were relatively faster than those in the 1980s-1990s. During the 1980s-1990s, the gain of high vegetation coverage areas was active, and the loss was dormant; in contrast, the gain and loss of low vegetation coverage areas were both dormant. In the 1990s to the early 21st century, the gains of high and low vegetation coverage areas were both dormant, whereas the losses were active. During the study period, areas of low vegetation coverage were converted into ones with higher coverage, and areas of high vegetation coverage were converted into ones with lower coverage. The vegetation coverage exhibited a good correlation (R 2 = 0.60) with precipitation, and the positively correlated area was larger than the negatively correlated area. Human activities not only promote the vegetation coverage, but also have a destructive effect on vegetation, and the promotion effect during 1982 to 2000 was larger than from 2001 to 2010, while, the destructive effect was larger from 2000 to 2010.

In this study, we used bands 7, 4, and 3 of the Advance Himawari Imager (AHI) data, combined with a Threshold Algorithm and a visual interpretation method to monitor the entire process of grassland fires that occurred on the China-Mongolia border regions, between 05:40 (UTC) on April 19th to 13:50 (UTC) on April 21st 2016. The results of the AHI data monitoring are evaluated by the fire point product data, the wind field data, and the environmental information data of the area in which the fire took place. The monitoring result shows that, the grassland fire burned for two days and eight hours with a total burned area of about 2708.29 km2. It mainly spread from the northwest to the southeast, with a maximum burning speed of 20.9 m/s, a minimum speed of 2.52 m/s, and an average speed of about 12.07 m/s. Thus, using AHI data can not only quickly and accurately track the dynamic development of a grassland fire, but also estimate the spread speed and direction. The evaluation of fire monitoring results reveals that AHI data with high precision and timeliness can be highly consistent with the actual situation.

Variation in vegetation cover in Inner Mongolia has been previously studied by the remote sensing data spanning only one decade. However, spatial and temporal variations in vegetation cover based on the newly released GIMMS NDVI3g data spanning nearly thirty years have yet to be analyzed. In this study, we applied the methods of the maximum value composite (MVC) and Pearson's correlation coefficient to analyze the variations of vegetation cover in Inner Mongolia based on GIMMS NDVI3g data spanning from 1982 to 2013. Our results indicate that the normalized difference vegetation index (NDVI) increased at a rate of 0.0003/a during the growing seasons despite of the drier and hotter climate in Inner Mongolia during the past three decades. We also found that vegetation cover in the southern agro-pastoral zone significantly increased, while it significantly decreased in the central Alxa. The variations in vegetation cover were not significant in the eastern and central regions. NDVI is positively correlated with precipitation (r=0.617, P=0.000) and also with air temperature (r=0.425, P=0.015), but the precipitation had a greater effect than the air temperature on the vegetation variations in Inner Mongolia.

As the global climate has changed, studies on the relationship between vegetation and climate have become crucial. We analyzed the long-term vegetation dynamics and diverse responses to extreme climate changes in Inner Mongolia, based on long-term Global Inventory Monitoring and Modelling Studies (GIMMS) NDVI3g datasets, as well as the eight extreme precipitation indices and six extreme temperature indices that are highly correlated with the occurrence of droughts or floods, heat or cold temperature disasters, and vegetation growth in Inner Mongolia. These datasets were analyzed using linear regression analysis, the Hurst exponent index, residual analysis, and the Pearson correlation analysis. The results showed the following: (1) The vegetation dynamical changes exhibited trends of improvement during 1982 to 2015, and 68% of the vegetation growth changes in Inner Mongolia can be explained by climate changes. (2) The extreme precipitation indices exhibited a slight change, except for the annual total wet–day precipitation (PRCPTOT). The occurrence of extreme cold temperatures showed a significant decline, while the occurrence of extreme warm temperatures showed an upward trend in Inner Mongolia. (3) The typical steppe, desert steppe, and forest steppe regions are more sensitive to extreme large precipitation, and the forest regions are more sensitive to extreme warm temperatures. (4) Extreme precipitation exhibits a one-month lagged effect on vegetation that is larger than the same-month effects on the grassland system. Extreme temperature exhibits same-month effects on vegetation, which are larger than the one-month lagged effects on the forest system. Therefore, studies of the relationship between extreme climate indices and vegetation are important for performing risk assessments of droughts, floods, and other related natural disasters.

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