Identifying the vegetation destruction and restoration in surface coal mines across China over the past three decades by EAuto-VDR.

IF 8 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Journal of Environmental Management Pub Date : 2025-08-01 Epub Date: 2025-06-14 DOI:10.1016/j.jenvman.2025.126195
Chengye Zhang, Li Guo, Jun Li, Quansheng Li, Hui Kang, Yaling Xu, Simit Raval
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引用次数: 0

Abstract

It is crucial for balancing energy demands and ecological protection to understand the patterns of vegetation disturbance in surface coal mines at a national or global scale. This study developed a new method called EAuto-VDR (Enhanced Auto-VDR, Automatically identifying the vegetation destruction and restoration) to automatically identify the vegetation disturbance and revealed the disturbance patterns for more than 300 surface coal mines across various climatic contexts in China. The EAuto-VDR consists of five steps: construction of sample dataset, identification of disturbance types, automatic determination of vegetated/bare ground threshold, extraction of disturbance time, magnitude, and duration, and intelligent optimization of the results. The results show that: (1) The accuracy of EAuto-VDR reached 0.96, 0.92, and 0.90 for identifying disturbance types, destruction time, and restoration time, respectively. A dataset documenting histories of vegetation destruction and restoration has been produced and made publicly available in this paper. (2) General spatio-temporal patterns of vegetation disturbance across 329 surface coal mines of China have been revealed. Over the past three decades, surface coal mining activities in China have resulted in vegetation destruction area of 1271.34 km2 totally, and 457.23 km2 has been restored, and the average restoration rate is 0.36. Large-scale vegetation destruction due to mining activities began around 2003, with significant restoration activities beginning from 2010, and the "Area destroyed per ton of coal mined" (ADt, m2/t) has been decreasing until to the latest. The "S-shaped" relationship between the cumulative vegetation destruction area and the disturbance duration, the "progressive mining" mode, and the "restoring while mining" phenomenon, were discovered. This study solved the problem of how to automatically identify the vegetation disturbance for surface coal mines in various climatic contexts, which provides an effective tool for investigating vegetation dynamics for surface coal mines at the national and even the global scale in future.

利用EAuto-VDR识别近30年中国露天矿植被破坏与恢复
了解全国或全球露天煤矿植被扰动规律,对平衡能源需求与生态保护具有重要意义。本文提出了一种新的植被破坏与恢复自动识别方法EAuto-VDR (Enhanced Auto-VDR, automatic identification vegetation destruction and restoration),用于自动识别植被扰动,并揭示了中国300多个露天煤矿在不同气候背景下的扰动模式。EAuto-VDR包括五个步骤:构建样本数据集,识别干扰类型,自动确定植被/裸地阈值,提取干扰时间、强度和持续时间,以及智能优化结果。结果表明:(1)EAuto-VDR识别干扰类型、破坏时间和恢复时间的准确率分别达到0.96、0.92和0.90;本文制作了一个记录植被破坏和恢复历史的数据集,并向公众开放。(2)揭示了中国329个露天煤矿植被扰动的一般时空格局。近30年来,中国露天采煤活动共造成1271.34 km2植被破坏,恢复457.23 km2,平均恢复率为0.36。采矿活动对植被的大规模破坏始于2003年左右,从2010年开始出现了明显的恢复活动,“每吨采煤破坏面积”(ADt, m2/t)一直到最近都在减小。发现累积植被破坏面积与扰动持续时间、“渐进开采”模式、“边开采边恢复”现象呈“s”型关系。该研究解决了不同气候背景下露天矿植被扰动的自动识别问题,为今后在全国乃至全球范围内研究露天矿植被动态提供了有效的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Environmental Management
Journal of Environmental Management 环境科学-环境科学
CiteScore
13.70
自引率
5.70%
发文量
2477
审稿时长
84 days
期刊介绍: The Journal of Environmental Management is a journal for the publication of peer reviewed, original research for all aspects of management and the managed use of the environment, both natural and man-made.Critical review articles are also welcome; submission of these is strongly encouraged.
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