The ecological environment is suffering from great human disturbance. Scientific assessment of landscape ecological risks can provide scientific guidance for land use management. This study focused on Chaoyang County in China, used ecological risk assessment methods to characterize the impact of land use/land cover (LUCC) change, and revealed the risk aggregation pattern with the help of spatial autocorrelation analysis. The results showed that ecological risk was increased from 2000 to 2010 but decreased from 2010 to 2018. The ecological risk of the Daling River and Xiaoling River basin was at a relatively high level, and low in the northwest and southeast of the study which covered by forest land. Occupying cultivated land for built-up and large-scale deforestation were two of the main factors to contribute to the increase of ecological risk. The distribution of High-High (HH) and Low-Low (LL) risk agglomeration areas was basically the same as risk levels, but the scope is smaller and more precise. Thus, HH and LH risk agglomeration area should be paid more attention to prevent the adverse impact of adjacent areas. Our study gave a novel perspective to investigate the pattern of ecological risk in order for government managers to identify key risk areas.

Coal production will cause serious damage to regional vegetation, especially in ecologically fragile grasslands. It is the consensus of all major countries to conduct vegetation restoration and management monitoring in areas damaged by coal production. This paper compares the adaptability of different data sources and different vegetation indices to grassland mining areas and proposes a normalized environmental vegetation index (NEVI) suitable for vegetation monitoring in grassland mining areas. Based on the Landsat and Sentinel data from 2005 to 2019, this paper uses NEVI to monitor the vegetation destruction and restoration of the Shengli mining area. The main result is that the vegetation restoration work in the Shengli mining area started in 2007 and was gradually carried out in subsequent years. The restoration effect of vegetation is significantly better in the east than in the west. The NEVI of the vegetation in the east can reach, or exceed, the level of natural vegetation in the same period. The restoration of vegetation degradation in some areas requires strengthening of management and maintenance measures.

This research is based on Landsat5 TM, Landsat8 OLI/TIRS remote sensing data using RSEI model to analyze and monitor the ecological environment and its temporal and spatial changes in the forest-grass transition zone in Northeast China from 2004 to 2019. The change characteristics of the ecological environment of different types of land cover types are monitored by RSEI method, and the response of different land cover types to natural factors such as precipitation and temperature is analyzed at the same time. The distribution of RSEI in the study area presents the characteristics of high in the east and low in the west. The eastern mountainous area is densely covered with woodland, which is the area with the best ecological environment quality in the study area. The grassland in the western plain and the saline-alkali land around the river are the areas with poor ecological environment in the study area. Climate, precipitation, topography and other natural elements work together to form the quality of the ecological environment in the study area roughly bounded by 120˚E. In years with poor natural conditions, this dividing line will have a clear eastward shifting trend, especially in the northern part of the study area. The spatial distribution of RSEI in the study area has a high degree of spatial autocorrelation, and Global Moran's I has been above 0.8 over the years. In terms of temporal changes in ecological conditions, the ecological environment in the study area was basically stable from 2004 to 2008, with a slight deterioration; it improved significantly from 2008 to 2011; however, it deteriorated significantly from 2011 to 2019. According to the results of partial correlation analysis, the ecological environment of the former is highly correlated with natural elements such as climate and precipitation, while the latter is mainly affected by human factors.

As a unique ecosystem with multiple ecological functions but high fragility, grassland in arid areas is very vulnerable to changes in the natural environment or human activities, resulting in various ecological and environmental problems. In order to study the degree and spatial extent of the influence of climatic conditions and human activities, especially mining activities, on grasslands in arid regions, we used remote sensing data to monitor the vegetation of the Xilin Gol grassland over a long period. The significant greening and browning areas of Xilin Gol grassland vegetation from 2000 to 2020 were extracted by a time series analysis. At the same time, the correlation analysis method was used to obtain the response of the Xilin Gol grassland vegetation to climatic factors and social and economic factors. In addition, we propose a new method based on buffer analysis and correlation analysis to calculate the influence range of vegetation degradation due to mining. We used this method to determine the influence range of vegetation degradation in the main mining area of the Xilin Gol grassland. The results showed that the vegetation condition of the Xilin Gol grassland were slightly improved from 2000 to 2020. Its vegetation was significantly affected by precipitation, and more than 50% of the area’s vegetation changes were highly correlated with precipitation changes. However, the area with the most serious vegetation degradation was mainly affected by human factors, and this part accounted for about 0.13% of the total area. In the form of direct damage and indirect effects (pulling population and economic growth to expand built-up areas), coal mining has become the main driving factor in the most significant areas of vegetation damage in the study area. Vegetation coverage in areas with significant greening and significant browning was highly correlated with economic factors, indicating that the vegetation changes were significantly affected by economic development. This study can reflect the vegetation changes and main driving factors in the overall and key areas of the Xilin Gol League and is a meaningful reference for the local balance of economic development and environmental protection.

Following vegetation reclamation in mining areas, secondary damage may occur at any time, especially in locations that have been mined for decades or even hundreds of years. Effective monitoring strategies are required to accurately assess plant growth and to detect the ecological effects of reclamation. Single satellite monitoring is often difficult to ensure vegetation monitoring needs, therefore multi-source remote sensing is preferred. Different sensor parameters and variation in spectral bands can lead to differences in the type of data obtained, and subsequently, methods for evaluating these differences are required for simultaneous sensor/band use. In this study, NDVI was selected to characterize the vegetation growth of the Antaibao Open-pit Coal Mine Dump by analyzing the correlation between different types of sensors (Landsat 8, HJ, Sentinel-2) and vegetation greenness in order to facilitate satellites’ replacement and supplement. Results show that: (1) Landsat 8 and Sentinel-2 satellite have a high relevance for monitoring the vegetation, but the correlation between these two sensors and HJ is relatively low, (2) the correlation between NDVI values varied by vegetation type, tree (R = 0.8698) > combined grass, shrub and tree (R = 0.7788) > grass (R = 0.7619) > shrub (R = 0.7282), and (3) the phenomenon of “Low value is high, high value is low” in the NDVI value with HJ satellite monitoring may have been caused by a weak signal strength and low sensitivity of the HJ sensor. Comparing the correlation of multi-source sensors to monitor the vegetation in the mining areas can be helpful to determine the alternative supplement of sensors through conversion formulas, which are helpful in realizing the long-term monitoring of dumps and detecting reclamation response in mining areas.