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.

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.

Understanding of the influencing factors of industrial sector carbon dioxide emissions is essential to reduce natural and anthropogenic greenhouse gas emissions. In this paper, we applied the Logarithmic Mean Divisia Index (LMDI) decomposition method based on the extended Kaya identity to analyze the changes in industrial carbon dioxide emissions resulting from 39 industrial sectors in Inner Mongolia northeast of China over the period 2003–2012. The factors were divided into five types of effects i.e., industrial growth effect, industrial structure effect, energy effect, energy intensity effect, population effect and comparative analysis of differential influences of various factors on industrial sector. Our results clearly show that (1) Industrial sector carbon dioxide emissions have increased from 134.00 million ton in 2003 to 513.46 million ton in 2012, with an annual average growth rate of 16.097%. The industrial carbon dioxide emissions intensity has decreased from 0.99 million ton/billion yuan to 0.28 million ton/billion yuan. Also, the energy structure has been dominated by coal; (2) Production and supply of electric power, steam and hot water, coal mining and dressing, smelting and pressing of ferrous metals, petroleum processing, coking and nuclear fuel processing, and raw chemical materials and chemical products account for 89.74% of total increased industrial carbon dioxide emissions; (3) The industrial growth effect and population effect are found to be a critical driving force for increasing industrial sector carbon dioxide emissions over the research period. The energy intensity effect is the crucial drivers of the decrease of carbon dioxide emissions. However, the energy structure effect and industrial structure effect have considerably varied over the study years without displaying any clear trend.

Based on the 1965–2017 climate data of 18 meteorological stations in the Songliao Plain maize belt, the Coupled Model Intercomparision Project (CMIP5) data, and the 1998–2017 maize yield data, the drought change characteristics in the study area were analyzed by using the standardized precipitation evapotranspiration index (SPEI) and the Mann–Kendall mutation test; furthermore, the relationship between meteorological factors, drought index, and maize climate yield was determined. Finally, the maize climate yields under 1.5 °C and 2.0 °C global warming scenarios were predicted. The results revealed that: (1) from 1965 to 2017, the study area experienced increasing temperature, decreasing precipitation, and intensifying drought trends; (2) the yield of the study area showed a downward trend from 1998 to 2017. Furthermore, the climate yield was negatively correlated with temperature, positively correlated with precipitation, and positively correlated with SPEI-1 and SPEI-3; and (3) under the 1.5 °C and the 2.0 °C global warming scenarios, the temperature and the precipitation increased in the maize growing season. Furthermore, under the studied global warming scenarios, the yield changes predicted by multiple regression were −7.7% and −15.9%, respectively, and the yield changes predicted by one-variable regression were −12.2% and −21.8%, respectively.

As the main defoliators of coniferous forests in Shandong Province, China, pine caterpillars (including Dendrolimus suffuscus suffuscus Lajonquiere, D. spectabilis Butler, and D. tabulaeformis Tsai et Liu) have caused substantial forest damage, adverse economic impacts, and losses of ecosystem resources. Therefore, elucidating the effects of drought on the outbreak of these pests is important for promoting forestry production and ecological reconstruction. Accordingly, the aim of the present study was to analyse the spatiotemporal variation of drought in Shandong Province, using the Standard Precipitation Index, and to investigate the impact of drought on the outbreak of pine caterpillar infestations. Future trends in drought and pine caterpillar populations were then estimated using the Hurst exponent. The results showed that: (1) Drought decreased gradually and showed a wetting trend from 1981 to 2012, with frequency decreasing on a decadal scale as follows: 1980s > 1990s > 2000s > 2010s; (2) The total area of pine caterpillar occurrence decreased strongly from 1992 to 2012; (3) Long-term or prolonged drought had a greater positive impact on pine caterpillar outbreak than short-term drought; (4) In the future, a greater portion of the province’s area will experience increased wetting conditions (57%) than increased drought (43%), and the area of pine caterpillar outbreak is estimated to decrease overall. These findings help elucidate the relationship between drought and pine caterpillar outbreak in Shandong Province and, hence, provide a basis for developing preventive measures and plans.

The frequency of extreme climate events has increased resulting in major changes to vegetation in arid and semi-arid areas. We selected 12 extreme climate indices and used trend analysis and multiple linear regression models to analyze extreme climate trends in steppe areas of Inner Mongolia and their impact on the normalized difference vegetation index (NDVI). From 1998 to 2017, the NDVI of the Inner Mongolia steppe increased overall; however, there was a small area of decrease. Extreme climate indices related to warming exhibited increasing trends, particularly in the desert steppe. Although the extreme precipitation index did not change significantly overall, it increased in the northeastern and southwestern regions of the study area and decreased in the central region. The established model showed that the extreme climate explained the highest NDVI variation in desert steppe (R² = 0.413), followed by typical steppe (R² = 0.229), and meadow steppe (R² = 0.109). In desert steppe, TX90P (warm days index) had the greatest impact; in typical steppe, R10 (number of heavy precipitation days index) had the greatest impact; in meadow steppe, R95P (very wet days index) had the greatest impact. This study offered new insights into dynamic vegetation changes in steppe areas of Inner Mongolia and provided a scientific basis for implementing environmental protection strategies.