在垃圾填埋场使用高分辨率土地表面温度数据检测热点:活跃和关闭地点的案例研究
Sedat Yalcinkaya1, Fatih Dogan2
1Department of Environmental Engineering, Faculty of Engineering, Marmara University, Istanbul, Türkiye.
概括
使用机器学习对垃圾填埋场进行高分辨率的热监测, 即使是封闭的垃圾填埋场也显示表面温度升高,这表明需要持续观察的长期热活动.
科学领域:
- 环境科学
- 遥感技术
- 地理信息学
背景情况:
- 垃圾填埋场由于废物分解而产生显著的热活动.
- 关闭垃圾填埋场后的监测至关重要,但缺乏高分辨率的热数据.
- 了解垃圾填埋场的热行为对于有效的废物管理至关重要.
研究的目的:
- 使用机器学习研究活跃和封闭垃圾填埋场的热行为.
- 将来自卫星的陆地表面温度 (LST) 数据缩小到10米分辨率.
- 评估热活动并确定垃圾填埋场中的持续热点.
主要方法:
- 使用Landsat 8和Landsat 9 LST数据缩小使用Sentinel-2光谱指数.
- 使用自动机器学习框架来评估各种回归算法 (例如神经网络,高斯过程).
- 对2023-2024年进行月度观测,并对缩小规模的LST数据进行热点分析.
主要成果:
- 神经网络 (Net) 对活跃的Kömürcüoda垃圾填埋场是最佳的;高斯过程 (Gp) 对关闭的Odayeri垃圾填埋场是最佳的.
- 与原始数据集相比,缩小后的LST数据显示出高准确度 (RMSE 0.74°C2.38°C).
- 在现有垃圾填埋场和封闭垃圾填埋场均发现持续高温区域.
结论:
- 基于机器学习的缩小有效地提高了LST监测的分辨率.
- 即使在关闭后,奥达耶里垃圾填埋场仍表现出显著的热活动.
- 垃圾填埋场表面的温度可能会长时间保持高,因此需要持续监测.
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