典型矿区植被覆盖度时空分布特征及影响因素

植被状况可以直接或间接地反映采煤对生态环境的影响。以长河井工煤矿、离柳井工煤矿、平朔露天煤矿3个典型矿区为研究区域。以Landsat数据为数据源，基于地形调节植被指数的像元二分模型提取植被覆盖度；采用趋势分析、线性回归斜率、稳定性分析方法，分析了3个典型矿区2001-2016年植被覆盖度的时空变化特征；运用"以时间换空间"的方法，采用相关分析方法对植被覆盖度变化的自然影响因素进行了分析。结果表明：（1）近16年3个典型矿区植被覆盖度呈增加趋势，长河、离柳、平朔矿区的增长速率分别为0.09%/10 a、0.10%/10 a、0.08%/10 a（P > 0.05）。（2）空间上，长河、离柳、平朔矿区植被覆盖度变化不明显比例分别占到66.63%，59.90%，62.25%，呈增加趋势的比例仅分别占28.14%、32.55%、27.81%，而呈减少趋势的比例分别占到5.23%、7.55%、9.94%。长河矿区明显改善的区域位于自然植被和耕作区的北部和东北部，离柳矿区明显改善的区域位于以低植被覆盖度为主的北部，平朔矿区明显改善的区域位于复垦的中西部。（3）不区分植被类型时，3个矿区的植被覆盖度变化与高程、高程与温度的交互作用表现出显著相关性（P < 0.01），与各自然因素的相关性总体表现为长河 > 离柳 > 平朔矿区；区分植被类型时，草地与坡度的相关性不显著（P > 0.05），与降雨量、高程存在显著正相关（P < 0.05）；灌木林与温度相关性不显著，与高程和降雨量的交互作用存在显著正相关；旱地与高程、高程与温度的交互作用存在显著相关性；疏林地与坡向、降雨量与坡向坡度的交互作用均没有表现出相关性；有林地与高程降雨量的交互作用表现出显著正相关性。探讨不同植被类型对自然因素的响应，可为矿区植被结构的选择，矿区复垦提供参考依据。

Temporal and spatial distribution characteristics and influencing factors of vegetation coverage in typical mining areas

Vegetation conditions can directly or indirectly reflect the impact of coal mining on the ecological environment. In this study, three typical mining areas of Changhe Coal Mine, Liliu Coal Mine and Pingshuo Open-pit Coal Mine are used as research areas. Based on Landsat data, the pixel coverage model based on topographically adjusted vegetation index was used to extract vegetation coverage. The trend analysis, linear regression slope and stability analysis method were used to analyze the spatial and temporal coverage of vegetation coverage in three typical mining areas from 2001 to 2016. Using the methodology of "time-for-space", the correlation analysis method was used to analyze the natural influencing factors of vegetation coverage change. The results showed that: (1) the vegetation coverage of the three typical mining areas presented an increasing trend in the past 16 years, and the growth rates of the Changhe, Liliu and Pingshuo mining areas were 0.09%/10 a, 0.10%/10 a, 0.08%/10 a, respectively (P > 0.05). (2) In terms of space, the proportions of vegetation coverage change in Changhe, Liliu, and Pingshuo mining areas were not obvious, accounting for 66.63%, 59.90%, and 62.25%, respectively. The proportions showing an increasing trend accounted for 28.14%, 32.55%, and 27.81% respectively.The proportions showing a decreasing trend accounted for 5.23%, 7.55%, and 9.94%, respectively. The obviously improved part of the Changhe mining area was located in the north and northeast of the natural vegetation and farming area. The obviously improved part of the Liliu mining area was located in the north with low vegetation coverage. The area with obvious improvement in the Pingshuo mining area was located in the central and western part of the reclamation area. (3) When the vegetation type was not distinguished, the change of vegetation coverage and the interaction of elevation, elevation and temperature in the three mining areas showed significant correlation (P < 0.01), and the correlation with various natural factors was in the order of Changhe > Liliu > Pingshuo mining area. When the vegetation type was distinguished, the correlation between grassland and slope was not significant (P > 0.05), and there was a significant positive correlation with rainfall and elevation (P < 0.05). The correlation between shrub forest and temperature was not significant. There was a significant positive correlation between the interaction of elevation and rainfall. There was a significant correlation between the interaction between dryland and elevation, elevation and temperature. The interaction between sparse forest and slope direction, rainfall and slope gradient showed no correlation. The interaction between woodland and elevation rainfall showed a significant positive correlation. Exploring the response of different vegetation types to natural factors can provide a reference for the selection of vegetation structure in the mining area and the reclamation of mining areas.