森林干扰监测使用基于云的Sentinel-2卫星图像和机器学习
1Forest Research Institute, Department of Forest Ecology and Silviculture, University of Sopron, Bajcsy-Zsilinszky u 4, 9400 Sopron, Hungary.
Journal of imaging
|January 22, 2024
概括
这项研究使用了Sentinel-2卫星数据和谷歌地球引擎来绘制匈牙利森林破坏的地图. 该方法准确地识别干旱和伤害,显示了国家森林监测的潜力.
科学领域:
- 林业林业 林业 林业 林业
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
背景情况:
- 由于匈牙利森林破坏的频率越来越高,因此需要有效的监测工具.
- 遥感为检测森林干扰提供了一种快速,经济高效的解决方案.
- 将卫星数据与云计算和现场数据集成,提高了监控能力.
研究的目的:
- 开发和验证一种使用卫星图像和谷歌地球引擎监测森林破坏的综合方法.
- 在匈牙利的一个研究地点检测和绘制森林干扰,包括干旱和伤害的地图.
- 评估该方法在区分树种和它们对损害的反应方面的能力.
主要方法:
- 使用高分辨率的ESA Sentinel-2卫星图像和谷歌地球引擎云平台.
- 用于检测森林干扰 (2017-2020年) 的植被指数 (NDVI和Z·NDVI) 的导出.
- 应用随机森林机器学习分类器用于树种分布映射和使用混矩阵的准确性评估.
主要成果:
- 通过使用NDVI地图,成功检测了Nagyerdő森林的干旱和伤.
- 在索引地图上,树种对损害的反应有明显的差异.
- 实现了高准确率:99.1%的生产者,71%的用户和71%的森林损害;81.9%的树种.
结论:
- 综合遥感和云计算方法有效监测森林损害和物种分布.
- 该方法表现出高精度和弹性,适合扩展到国家级森林监测.
- 未来的工作包括整合系统收集的现场数据,最近的卫星图像和人工智能,以加强国家森林监测.
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