在采矿景观中检测干扰和恢复:基于改进的土地趋势和机器学习的新型时间序列框架
1School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha 410083, China; Hunan Key Laboratory of Nonferrous Resources and Geological Disaster Exploration, Changsha 410083, China.
Environmental research
|February 2, 2026
概括
采矿区的植被监测显示了普遍的绿化趋势,超过60%的受破坏区域得到恢复. 这项研究提供了一种实用方法,用于追踪矿山景观中的植被动态.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 生态生态学 生态生态学
背景情况:
- 采矿对生态系统产生重大影响,因此植被监测对于环境保护和区域发展至关重要.
- 植被是资源开采区生态系统健康的关键指标.
- 有效监测植被动态对于采矿地区的可持续土地管理至关重要.
研究的目的:
- 用谷歌地球引擎量化评估石庄矿区的时空植被变化 (变绿,变棕,干扰,恢复) 的情况.
- 评估改进的LandTrendr算法的有效性,用于监测矿山景观中的植被.
- 为了分类植被变化模式,并确定变化的驱动因素.
主要方法:
- 在谷歌地球引擎 (GEE) 1997-2020年的数据上使用趋势分析和改进的LandTrendr算法.
- 利用机器学习算法来分类植被变化模式.
- 专注于植被绿化,棕色化,干扰和恢复的时空分析.
主要成果:
- 观察到一种普遍的绿化趋势,局部色与人类活动有关.
- 干扰集中在特定的年份 (1999,2003,2012年),随后出现明显的恢复模式.
- 矿区内超过60%的被破坏的植被被恢复,城市中心附近的破坏堆显示>64%的高质量恢复.
结论:
- 改进的LandTrendr算法有效监控开采景观中的植被恢复,克服了以前的检测问题.
- 人工修复努力体现在植被的成功恢复中,特别是在城市中心附近.
- 这项研究为矿业影响地区的生态监测和环境管理策略提供了有价值的框架.
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