Monitoring and Evaluating Restoration Vegetation Status in Mine Region Using Remote Sensing Data: Case Study in Inner Mongolia, China

Evaluating the surface water pollution risk of mineral resource exploitation via an improved approach: a case study in Liaoning Province, Northeastern China

Pollution from mineral exploitation is an important risk factor affecting surface water environment in mineral regions. It is urgent to construct a simple and accurate model to assess the surface water pollution risk from mineral exploitation in the regional scale. Thus, taking a mining province namely Liaoning in northeastern China as the study area, we proposed a framework to simulate the transport process of pollutants from mineral exploitation points to the surrounding surface water based on the "source-sink" theory. In our framework, we adopted the regional growth method (RGM) to extract the potential polluted water area as the certain "sink" considering the influence of the topography, and then applied Minimum Cumulative Resistance (MCR) model to assess the surface water pollution risk from mineral exploitation. The results revealed that: (1) 9.5% of the water areas were located at the potential impact area of MEPs. (2) The total value of resistance surface in Liaoning is relatively low, and gradually decreased from west to east. (3) MEPs in Liaoning had a high risk and seriously threatened the surface water environment, among 2125 MEPs, 733 MEPs (32.99%) were assessed as extremely high risk level, and about 35% of the MEPs were distributed within 10KM buffer zone of surface water. (4) Water pollution risk of MEPs in Dalian, Tieling, Fuxin and Dandong need to be emphasized. (5) Compared to previous studies, we considered the topographical influence before applying MCR model directly, so the results of water pollution risk were more reliable. This study provides a methodological support and scientific reference for the water environment protection and regional sustainable development.

Vegetation as an ecological indicator in assessing environmental restoration in mining areas

As global demand for natural resources escalates, the environmental impact stemming from resource extraction has risen to the forefront of contemporary discussions. This paper probed the potential of using vegetation cover as an ecological barometer to gauge the level of environmental damage and restoration in mining areas: a decline in vegetation cover may signify detrimental impacts from intense mining activities, while an increase may indicate effective local environmental stewardship. Therefore, this paper undertook an assessment and discussion of mining damage and environmental management at China's Ta'ershan Mining Area since 2007, calculating and visualizing FVC (Fractional Vegetation Cover) of the Ta'ershan Mining Area to track changes in vegetation cover between 2007 and 2021. Changes in vegetation cover in the Ta'ershan Mining Area could act as a reflection of both mining-induced damage and subsequent successful environmental management by local authorities, providing a practical way to evaluate ecological effects in resource development.

Comparison of the Vegetation Index of Reclamation Mining Areas Calculated by Multi-Source Remote Sensing Data

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.

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

Inner Mongolia in China is a typically arid and semi-arid region with vegetation prominently affected by global warming and human activities. Therefore, investigating the past and future vegetation change and its impact mechanism is important for assessing the stability of the ecosystem and the ecological policy formulation. Vegetation changes, sustainability characteristics, and the mechanism of natural and anthropogenic effects in Inner Mongolia during 2000–2019 were examined using moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data. Theil–Sen trend analysis, Mann–Kendall method, and the coefficient of variation method were used to analyze the spatiotemporal variability characteristics and sustained stability of the NDVI. Furthermore, a trend estimation method based on a Seasonal Trend Model (STM), and the Hurst index was used to analyze breakpoints and change trends, and predict the likely future direction of vegetation, respectively. Additionally, the mechanisms of the compound influence of natural and anthropogenic activities on the vegetation dynamics in Inner Mongolia were explored using a Geodetector Model. The results show that the NDVI of Inner Mongolia shows an upward trend with a rate of 0.0028/year (p < 0.05) from 2000 to 2019. Spatially, the NDVI values showed a decreasing trend from the northeast to the southwest, and the interannual variation fluctuated widely, with coefficients of variation greater than 0.15, for which the high-value areas were in the territory of the Alxa League. The areas with increased, decreased, and stable vegetation patterns were approximately equal in size, in which the improved areas were mainly distributed in the northeastern part of Inner Mongolia, the stable and unchanged areas were mostly in the desert, and the degraded areas were mainly in the central-eastern part of Inner Mongolia, it shows a trend of progressive degradation from east to west. Breakpoints in the vegetation dynamics occurred mainly in the northwestern part of Inner Mongolia and the northeastern part of Hulunbuir, most of which occurred during 2011–2014. The future NDVI trend in Inner Mongolia shows an increasing trend in most areas, with only approximately 10% of the areas showing a decreasing trend. Considering the drivers of the NDVI, we observed annual precipitation, soil type, mean annual temperature, and land use type to be the main driving factors in Inner Mongolia. Annual precipitation was the first dominant factor, and when these four dominant factors interacted to influence vegetation change, they all showed interactive enhancement relationships. The results of this study will assist in understanding the influence of natural elements and human activities on vegetation changes and their driving mechanisms, while providing a scientific basis for the rational and effective protection of the ecological environment in Inner Mongolia.

Remote Sensing Monitoring and Evaluation of Vegetation Restoration in Grassland Mining Areas—A Case Study of the Shengli Mining Area in Xilinhot City, China

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.

Framework for Spatial and Temporal Monitoring of Urban Forest and Vegetation Conditions: Case Study Zagreb, Croatia

Urban forest and vegetation conditions are an important variable in urban ecosystem management decision-making. However, it is difficult to evaluate and monitor solely on the basis of field measurements. Remote sensing technologies can greatly contribute to the faster extraction and mapping of vegetation health status indicators, on the basis of which agronomy and forestry experts can draw conclusions about the condition of urban vegetation in larger areas. A new remote sensing-based urban forest and vegetation cover monitoring framework is presented and applied to a case study of the city of Zagreb, Croatia. In this study, Sentinel-2 multi-temporal imagery was used to derive and analyze the current state of urban forest cover. Vegetation indices (NDVI, RVI, and GRVI) were calculated. K-means unsupervised classification of the vegetation indices was conducted. In this way, the dimensionality of the vegetation indices was reduced, while all the data contained in it were used to represent their graded values. Vegetation that was in a poor condition stood out better that way. Finally, PCA-based change detection was performed on the vegetation indices graded values, and a map of change was produced. These results need to be interpreted and validated by foresters and agronomists in further research.