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 (R² = 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.

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.

The phenological parameters estimated from different data may vary, especially in response to climatic factors. Therefore, we estimated the start of the growing season (SOS) and the end of the growing season (EOS) based on sunlight-induced chlorophyll fluorescence (SIF), the normalized difference vegetation index (NDVI) and the near-infrared reflectance of vegetation (NIRv). The SIF, NDVI and NIRv breakpoints were detected, and the trends and change-points of phenological parameters based on these data were analyzed. The correlations between the phenological parameters and snow-related factors, precipitation, temperature, soil moisture and population density were also analyzed. The results showed that SIF and NIRv could identify breakpoints early. SIF could estimate the latest SOS and the earliest EOS. NDVI could estimate the earliest SOS and the latest EOS. The change-points of SOS_SIF were mostly concentrated from 2001 to 2003, and those of SOS_NDVI and SOS_NIRv occurred later. The change-points of EOS_SIF and EOS_NIRv were mostly concentrated from 2001 to 2007, and those of EOS_SIF occurred later. Differently from the weak correlation with SOS_SIF, SOS_NDVI and SOS_NIRv were significantly correlated with snow-related factors. The correlation between the meteorological factors in the summer and autumn and EOS_SIF was the most significant. The population density showed the highest degree of interpretation for SOS_NIRv and EOS_NDVI. The results reveal the differences and potentials of different remote-sensing parameters in estimating phenological indicators, which is helpful for better understanding the dynamic changes in phenology and the response to changes in various influencing factors.

Continuous climate warming in the last few decades has led to global climate anomalies, resulting in frequent drought events in arid/semiarid regions with fragile and sensitive ecological environment. The Mongolian Plateau (MP) is located at the mid-latitude arid/semiarid climate region, which is deemed as the most sensitive region in response to global climate change. In order to understand the spatiotemporal characteristics of droughts in Mongolian Plateau under changing climate, we divided the study area into three climatic regions via Köppen climate classification. Then, the seasonal and annual drought trends were analyzed by standardized precipitation evaporation index (SPEI), which is a function of monthly mean temperatures, highest temperatures, lowest temperatures and precipitations, collected from the 184 meteorological stations from 1980 to 2015. Mann–Kendall (MK) test was employed to detect if there is an abrupt change of annual drought, while the empirical orthogonal function method (EOF) was adopted to investigate the spatiotemporal characteristics of droughts across the Mongolian Plateau. Results from MK test illustrated that the SPEI-12 exhibited statistically significant downward trends (a < 0.05) for all three climatic regions of the Mongolian Plateau. EOF spatial analysis indicated that Region III experienced the most severe drought from 1980 to 2015. During the 35 years period, an abrupt change of drought was detected in 1999. Before year 1999, the climate was relatively humid. However, the entire region became more arid after year 1999, reflected by remarkably increased frequency and intensity of drought. SPEI-3 revealed the trend of drought at seasonal scale. We found that drought became more severe in spring, summer, and fall seasons for the entire MP. However, winter became more humid. Different climate regions exhibited quite different drought seasonality: Region I experienced a severe arid trend in summer and fall. For Region II and III, summer became more arid. All three regions became more humid in winter season, especially for Region I, with the Sen’s slope of 0.0241/a.

Landslide identification is an increasingly important research topic in remote sensing and the study of natural hazards. It is essential for hazard prevention, mitigation, and vulnerability assessments. Despite great efforts over the past few years, its accuracy and efficiency can be further improved. Thus, this study combines the two most popular approaches: susceptibility analysis and change detection thresholding, to derive a landslide identification method employing novel identification criteria. Through a quantitative evaluation of the proposed method and masked change detection thresholding method, the proposed method exhibits improved accuracy to some extent. Our susceptibility-based change detection thresholding method has the following benefits: (1) it is a semi-automatic landslide identification method that effectively integrates a pixel-based approach with an object-oriented image analysis approach to achieve more precise landslide identification; (2) integration of the change detection result with the susceptibility analysis result represents a novel approach in the landslide identification research field.