Spatial Heterogeneity of Vegetation Response to Mining Activities in Resource Regions of Northwestern China

Effects of coal mining and climate-environment factors on the evolution of a typical Eurasian grassland

Coal mining can significantly impact vegetation evolution, yet the limited information on its patterns and driving factors hampers efforts to mitigate these effects and reclaim abandoned mines. This study aimed to 1) examine vegetation evolution in a semiarid steppe watershed in northeast China; and 2) characterize the driving factors behind this evolution. We analyzed the impact of twelve selected driving factors on fractional vegetation coverage (FVC) from 2000 to 2021 using a dimidiate pixel model, Sen's slope analysis, Mann-Kendall trend test, coefficient of variation analysis, and Geodetector model. At a significance level of α = 0.05, our findings revealed a south-to-north decline pattern in FVC, a significant decrease trend in proximity to coal mines, and a notable increase trend adjacent to river channels. Approximately 37% of the watershed exhibited low FVC, while the overall temporal trend across the watershed was deemed insignificant. Areas surrounding the mines experienced a substantial reduction in FVC due to coal mining activities, while FVC variations across the watershed were linked to precipitation, temperature, and soil type. FVC predictions improved notably when interactions between multiple two-way factors were considered. Each driving factors displayed an optimal range (e.g., precipitation = 63-71 mm) for maximizing FVC. Given the study watershed's status as a national energy base, understanding vegetation responses to coal mining and climate-environment changes is crucial for sustaining fragile terrestrial ecosystems and socioeconomic development. Achieving a long-time balance between coal extraction and ecological protection is essential. The study outcomes hold significant promise for advancing ecological conservation, vegetation restoration, and mitigation of environmental degradation in semiarid regions affected by extensive coal mining and climate fluctuations. These findings contribute to the strategic management of such areas, promoting sustainable practices amidst evolving environmental challenges.

Prediction of the spatiotemporal evolution of vegetation cover in the Huainan mining area and quantitative analysis of driving factors

The prediction of the spatiotemporal dynamic evolution of vegetation cover in the Huainan mining area and the quantitative evaluation of its driving factors are of great significance for protecting and restoring the environment in this area. This study uses the Landsat 5 TM and Landsat 8 OLI time-series data to estimate the vegetation cover and uses the transition matrix to analyze the spatiotemporal transfer of vegetation cover from 1989 to 2004, 2004 to 2021, and 2021 to 2030. In addition, a structural equation model (SEM) was established in this study to assess the driving factors of vegetation cover. The quantitative analysis and the cellular automata (CA)-Markov model were performed to predict the future vegetation cover in the Huainan mining area. The results are as follows: (1) In different periods, the vegetation cover types were mainly high cover types transferred to other vegetation cover types; (2) human activities are the key factors affecting the vegetation growth, while topographical factor is the most influential factor promoting the vegetation growth; (3) highly consistent CA-Markov and multi-criteria evaluation (MCE) predicted results of vegetation cover in 2030 compared to that in 2021. The proportion of bare soil and low cover types had increased significantly, mainly concentrated in the internal area of the mines. The prediction of the spatiotemporal evolution of vegetation cover in the Huainan mining area and the quantitative change in driving factors are of significant importance for the restoration of the environment in mining areas.

Greening or browning? The macro variation and drivers of different vegetation types on the Qinghai-Tibetan Plateau from 2000 to 2021

Vegetation greenness is one of the main indicators to characterize changes in terrestrial ecosystems. China has implemented a few large-scale ecological restoration programs on the Qinghai-Tibetan Plateau (QTP) to reverse the trend of ecosystem degradation. Although the effectiveness of these programs is beginning to show, the mechanisms of vegetation degradation under climate change and human activities are still controversial. Existing studies have mostly focused on changes in overall vegetation change, with less attention on the drivers of change in different vegetation types. In this study, earth satellite observation records were used to robustly map changes in vegetation greenness on the QTP from 2000 to 2021. The random forest (RF) algorithm was further used to detect the drivers of greenness browning on the QTP as a whole and in seven different vegetation types. The results show that an overall trend of greening in all seven vegetation types on the QTP over a 21-year period. The area of greening was 46.54×10⁴ km², and browning was 5.32×10⁴ km², representing a quarter and 2.86% of the natural vegetation area, respectively. The results of the browning driver analysis show that areas with high altitude, reduced annual precipitation, high intensity of human activity, average annual maximum and average annual minimum precipitation of approximately 500 mm are most susceptible to browning on the QTP. For the seven different vegetation types, their top 6 most important browning drivers and the ranking of drivers differed. DEM and precipitation changes are important drivers of browning for seven vegetation types. These results reflect the latest spatial and temporal dynamics of vegetation on the QTP and highlight the common and characteristic browning drivers of vegetation ecosystems. They provide support for understanding the response of different vegetation to natural and human impacts and for further implementation of site-specific restoration measures.

Comprehensive Evaluation of the Eco-Geological Environment in the Concentrated Mining Area of Mineral Resources

The urbanization rate in China has been increasing in recent years, and along with the increasing intensity of human engineering activities, ecological and geological (eco-geological) degradation have become key factors impeding sustainable urban development. Taking the concentrated mineral exploitation area of Tonghua City as an example, the distribution of mines in the area is concentrated and the spatial heterogeneity is significant. This paper includes 14 evaluation indicators in three aspects: eco-geological environment background, anthropogenic and mining engineering activities, and environmental pollution. Then, based on game theory combined with ANP-CV (Analytic Network Process and Coefficient of Variation), two empowerment methods, GIS spatial calculation is used to evaluate the eco-geological environment quality (EEQ). The results showed that the EEQ was divided into grades I–V from high to low, with areas of 21.13%, 30.35%, 27.00%, 14.30%, and 7.22%, respectively; the EEQ of the Hun River basin has a high spatial autocorrelation and low EEQ, and the EEQ grade of mines was divided on this basis; the hot spot analysis is useful for determining the EEQ, as well as for allocating mine restoration resources in a sensible manner. Finally, we propose countermeasures to improve EEQ, and this study can provide a scientific basis for ecological construction and geological environmental protection in Tonghua City.

Land Degradation Assessment with Earth Observation

For decades now, land degradation has been identified as one of the most pressing problems facing the planet [...]

Multiple Effects of Topographic Factors on Spatio-Temporal Variations of Vegetation Patterns in the Three Parallel Rivers Region, Southeast Qinghai-Tibet Plateau

Topographic factors are critical for influencing vegetation distribution patterns, and studying the interactions between them can enhance our understanding of future vegetation dynamics. We used the Moderate-resolution Imaging Spectroradiometer Normalized Differential Vegetation Index (MODIS NDVI) image dataset (2000–2019), combined with the Digital Elevation Model (DEM), and vegetation type data for trend analysis, and explored NDVI variation and its relationship with topographic factors through an integrated geographically-weighted model in the Three Parallel Rivers Region (TPRR) of southeastern Qinghai-Tibet Plateau (QTP) in the past 20 years. Our results indicated that there was no significant increase of NDVI in the entire basin between 2000–2019, except for the Lancang River basin. In the year 2004, abrupt changes in NDVI were observed across the entire basin and each sub-basin. During 2000–2019, the mean NDVI value of the whole basin increased initially and then decreased with the increasing elevation. However, it changed marginally with variations in slope and aspect. We observed a distinct spatial heterogeneity in vegetation patterns with elevation, with higher NDVI in the southern regions NDVI than those in the north as a whole. Most of the vegetation cover was concentrated in the slope range of 8~35°, with no significant difference in distribution except flat land. Furthermore, from 2000 to 2019, the vegetation cover in the TPRR showed an improving trend with the changes of various topographic factors, with the largest improvement area (36.10%) in the slightly improved category. The improved region was mainly distributed in the source area of the Jinsha River basin and the southern part of the whole basin. Geographically weighted regression (GWR) analysis showed that elevation was negatively correlated with NDVI trends in most areas, especially in the middle reaches of Nujiang River basin and Jinsha River basin, where the influence of slope and aspect on NDVI change was considerably much smaller than elevation. Our results confirmed the importance of topographic factors on vegetation growth processes and have implications for understanding the sustainable development of mountain ecosystems.