显示露天煤矿碳储存时空动态的景观指数

Ming Chang, Shuying Meng, Xinran He, Long Chen, Lei Zhao, Haitao Yang, Ruiguo Wang, Xianghao Wang, Yuxia Zhao, Peng Zhao
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引用次数: 0

摘要

煤炭是中国的主要资源,露天开采占全球产量的很大一部分。然而,包括采矿和生态恢复在内的这一活动会对生态系统碳储存及其分布产生一定影响,其相关因素也不明确。本文基于土地利用/土地覆被特征(LULC),对中国典型的大型煤矿--海尔乌苏煤矿的碳储量变化进行了量化,并结合 InVEST 模型、景观生态功能贡献率和多元回归模型,分析了 2007 至 2022 年碳密度的影响因素。结果如下(1)从 2007 年到 2017 年,碳储量从 159.95 × 104 下降到 147.51 × 104,然后上升到 2022 年的 151.91 × 104。(2)2007-2022 年碳储量森林与草地面积景观格局耦合协调度在 0.887~0.867 之间,2012 年最低点为 0.720。(3)碳储量与植被指数、温度和海拔显著相关,这些因素可解释碳储量空间变异的 37.5%;逐步回归分析表明,整合香农多样性指数(SHEI)和聚集指数(AI)等景观格局可提高解释率 1.4%。(4)根据景观生态功能贡献率分析,可将碳储存敏感区分为三个等级:距矿区 0~4 km 的极敏感区、4~8 km 的敏感区和 8 km 以上的不敏感区。本研究提出了煤矿碳储量变化的分析策略,突出了景观格局对碳储量的重要影响作用,为煤矿生态管理提供了可靠的参考支撑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A landscape index for indicating the spatio-temporal dynamics of carbon storage in an opencast coal mine
Coal is China’s main resource, with open-pit mining accounting for a significant portion of global production. However, this activity, including mining and ecological restoration, can have a definite impact on ecosystem carbon storage and its distribution; its associated factors are also unclear. In this paper, we quantify the carbon storage changes in Haerwusu coal mine, a typical large-scale coal mine in China, based on land use/land cover (LULC) characteristics, and analyze the impact factors of carbon density from 2007 to 2022 by integrating the InVEST model with the landscape ecological function contribution ratio and multiple regression model. The results are as follows. (1) Carbon storage decreased from 159.95 × 104 to 147.51 × 104 from 2007 to 2017 and then increased to 151.91 × 104 to 2022. (2) The degree of coordination between carbon storage forest and grassland area landscape pattern coupling ranged from 0.887 to 0.867 from 2007 to 2022, with the lowest point at 0.720 in 2012. (3) Carbon storage was significantly related to vegetation indices, temperature, and elevation, and these factors can explain 37.5% of the carbon storage spatial variability; stepwise regression analysis showed that the integration of landscape patterns, such as Shannon’s diversity index (SHEI) and the aggregation index (AI), could improve the explanation by 1.4%. (4) Based on the analysis of the landscape ecological function contribution ratio, the carbon storage-sensitive areas can be classified into three levels: extremely sensitive areas ranging 0 to 4 km from the mine, sensitive areas ranging 4 to 8 km, and insensitive areas ranging beyond 8 km. This study proposes a strategy for analyzing changes of carbon storage in coal mines, highlights the important role of landscape patterns in influencing carbon storage, and provides a reliable reference support for the ecological management of coal mines.
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