Leveraging machine learning and Landsat time series for high-resolution mapping of mining-induced vegetation changes in Ouagadougou, Burkina Faso

Q2 Environmental Science Environmental Challenges Pub Date : 2024-10-02 DOI:10.1016/j.envc.2024.101026
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Abstract

Quarries are open pit mines, where construction materials such as granite and clay are extracted from the earth's surface, leading to a major disturbance of vegetation and subsequent degradation of ecosystem services. Assessing mining-associated degradation over time remains a challenge. This paper uses a temporal series of Landsat satellite image extracted from Google Earth Engine (GEE) for monitoring mining effects on land cover around the West African city of Ouagadougou, Burkina Faso. We conducted a binary classification of quarry and non-quarry using Landsat 9 images. To account for varying quarry patterns, we applied the LandTrendr algorithm to analyze images from 1990 to 2022, mapping annual vegetation disturbance and recovery. This generated high-resolution land cover maps with over 90 % accuracy across 257 quarries (38 km²). LandTrendr identified disturbance years with 87 % accuracy and assessed recovery in abandoned quarries. Remote sensing accuracy was validated through fieldwork and analysis of historical aerial photos. Our findings revealed that major disturbances coincided with new quarrying activities, particularly during urban expansion from 2005 to 2019, with NDVI decreasing by 0.3 in active mining areas. Vegetation recovery was observed in 23 % of the abandoned quarries, with NDVI increases by 0.15 within five years post abandonment, particularly in clay quarries. The strongest recovery occurred in given up abandoned from 2015 to 2019, aligned with increased conservation efforts. Our approach offers valuable insights into mining impacts and vegetation resilience in semi-arid environments, informing sustainable urban planning and environmental management in rapidly transforming regions of West Africa.
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利用机器学习和大地遥感卫星时间序列对布基纳法索瓦加杜古采矿引起的植被变化进行高分辨率测绘
采石场属于露天矿,从地表开采花岗岩和粘土等建筑材料,会对植被造成严重干扰,进而导致生态系统服务退化。评估与采矿相关的长期退化仍是一项挑战。本文利用从谷歌地球引擎(GEE)提取的陆地卫星图像时间序列,监测采矿对西非城市布基纳法索瓦加杜古周边土地植被的影响。我们使用 Landsat 9 图像对采石场和非采石场进行了二元分类。为了考虑不同的采石场模式,我们应用 LandTrendr 算法分析了 1990 年至 2022 年的图像,绘制了年度植被干扰和恢复图。这样绘制出的高分辨率土地覆被图准确率超过 90%,覆盖 257 个采石场(38 平方公里)。LandTrendr 确定干扰年份的准确率为 87%,并对废弃采石场的恢复情况进行了评估。通过实地考察和对历史航空照片的分析,对遥感的准确性进行了验证。我们的研究结果表明,重大干扰与新的采石活动相吻合,尤其是在 2005 年至 2019 年的城市扩张期间,活跃采矿区的 NDVI 下降了 0.3。在 23% 的废弃采石场观察到了植被恢复,在废弃后的五年内,NDVI 上升了 0.15,尤其是在粘土矿场。在 2015 年至 2019 年期间放弃的采石场中,植被恢复最为显著,这与保护工作的加强是一致的。我们的方法为半干旱环境中的采矿影响和植被恢复能力提供了宝贵的见解,为西非快速转型地区的可持续城市规划和环境管理提供了信息。
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来源期刊
Environmental Challenges
Environmental Challenges Environmental Science-Environmental Engineering
CiteScore
8.00
自引率
0.00%
发文量
249
审稿时长
8 weeks
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