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.

Simulation of land use trends and assessment of scale effects on ecosystem service values in the Huaihe River basin, China.

Land use demand change in the Huaihe River basin (HRB) and ecosystem service values (ESVs) in watersheds are important for the sustainable development and use of land resources. This paper takes the HRB as the research object, and using remote sensing images of land use as the data source adopts the comprehensive evaluation analysis method of ESVs based on equivalent factors and sensitivity analysis of the performance characteristics of ESV changes of different land use types. The PLUS model is used to predict spatiotemporal land use change characteristics to 2030 combining inertial development, ecological development, and cultivated land development. The spatial distribution and aggregation of ESVs at each scale were also explored by analyzing ESVs at municipal, county, and grid scales. Considering also hotspots, the contribution of land use conversion to ESVs was quantified. The results showed that (1) from 2000 to 2020, cultivated land decreased sharply to 28,344.6875 km2, while construction land increased sharply to 26,914.563 km2, and the change of other land types was small. (2) The ESVs in the HRB were 222,019 × 1012 CNY in 2000, 235,015 × 1012 CNY in 2005, 234,419 × 1012 CNY in 2010, 229,885 × 1012 CNY in 2015, and 224,759 × 1012 CNY in 2020, with an overall fluctuation, first increasing and then decreasing. (3) The ESVs were 219,977 × 1012 CNY, 218,098 × 1012 CNY, 219,757 × 1012 CNY, and 213,985 × 1012 CNY under the four simulation scenarios of inertial development, ecological development, cultivated land development, and urban development, respectively. At different scales, the high-value areas decreased, and the low-value areas increased. (4) The hot and cold spots of ESV values were relatively clustered, with the former mainly clustered in the southeast region and the latter mainly clustered in the northwest region. The sensitivity of ecological value was lower than 1, while the ESV was inelastic to the ecological coefficient, and the results were plausible. The mutual conversion of cultivated land to water contributed the most to ESVs. Based on the multi-scenario simulation of land use in the HRB by the PLUS model, we identified the spatial distribution characteristics of ESVs at different scales, which can provide a scientific basis and multiple perspectives for the optimization of land use structure and socio-economic development decisions.

Analysis of spatial and temporal changes of vegetation cover and its driving forces in the Huainan mining area.

The Huainan mining area is rich in coal resources and has sparse vegetation and many collapsed waterways. Large-scale and long-term underground coal mining has led to a fragile ecological environment in the mining area, and it is urgent to solve the contradiction between coal development and ecological environmental protection. The Huainan mining area was selected as the research object, and the vegetation cover was extracted using 10-phase Landsat multispectral remote sensing images from 1989 to 2021 to analyze its spatial and temporal changes and driving forces to provide a scientific basis for the guided restoration of the ecological environment in the region. Combined with the image dichotomous model, regression slope, correlation coefficient, and standard deviation of vegetation cover grid points in different time series, standard deviation ellipse, and center of gravity migration, we analyzed the spatial and temporal variation pattern of vegetation cover for 33 years and revealed the responses of temperature, precipitation, population density, GDP, and afforestation area to vegetation cover. Results show the following: (1) from 1989 to 2021, the overall vegetation cover in the study area tended to decrease with 36.48% of the areas increasing and 63.52% of the areas decreasing, primarily in the very low and medium range; (2) the center of gravity of different types of vegetation cover generally shifted from north to south during 33 years; (3) climate and social activities had a substantial effect on the spatial heterogeneity of the vegetation cover in the study area. There is significant spatial heterogeneity in the effects of climate and social activities on the vegetation in the study area with human activities negatively correlating with vegetation cover. Mining activities are the primary driver of the evolution of regional vegetation cover, with climate change serving as a secondary driver.