通过结合多光谱和SAR卫星数据,在国家范围内检测塑料养殖
Alessandro Fabrizi1, Peter Fiener1, Thomas Jagdhuber1,2
1Institute of Geography, University of Augsburg, Augsburg, Germany.
Scientific reports
|April 2, 2025
概括
这项研究使用卫星数据和机器学习来绘制德国农业塑料薄膜的地图,确定了超过10万公的塑料化农田. 这项技术为塑料农业的环境监测提供了一个强大的工具.
科学领域:
- 农业科学 农业科学
- 环境监测 环境监测
- 遥感 遥感 遥感 遥感
背景情况:
- 塑料薄膜在农业中越来越多地用于杂草和作物保护 (例如,温室,道).
- 人们对农业塑料薄膜,特别是皮膜对环境的影响存在担忧.
- 需要对不同塑料薄膜应用进行大规模监测.
研究的目的:
- 开发和应用一种使用卫星图像的机器学习算法,在德国范围内绘制塑料土农田 (PMF) 和植被上方的塑料覆盖 (PCV) 地图.
- 评估可免费获取的光学和雷达卫星数据的潜力,以对农业塑料进行大陆范围的监测.
主要方法:
- 利用云计算与自由可用的光学和雷达卫星图像.
- 开发并应用了用于PMF和PCV分类的机器学习算法.
- 通过地面观测验证结果,并与农业统计数据进行比较.
主要成果:
- 在2020年,在德国成功地绘制了PMF的103,000公和PCV的37,000公.
- 根据地面观测,实现了总体分类准确率为85.3%.
- 证明光学和雷达特征都很重要,PCV显示了由于底层金属框架而引起的明显雷达反射.
结论:
- 这项研究提供了不同农业塑料薄膜用途的首个国家规模地图.
- 自由获取的卫星数据与机器学习相结合,显示了有效地对农业塑料进行大陆监测的巨大潜力.
- 开发的算法准确地区分PCV和PMF,有助于环境影响评估.
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