全国规模的树冠覆盖模型使用google earth引擎和堆叠组合:希腊森林的案例研究
Katerina Vatitsi1, Irene Chrysafis1, Konstantinos Bellos1
1School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.
The Science of the total environment
|March 6, 2026
概括
这项研究使用机器学习和卫星数据创建了希腊国家树冠覆盖地图. 该先进的模型有助于森林监测和保护工作,支持可持续的管理实践.
科学领域:
- 林业林业 林业 林业 林业
- 遥感 遥感 遥感 遥感
- 生态生态学 生态生态学
背景情况:
- 森林生态系统提供重要服务,但由于气候变化和人类活动而面临退化,特别是在地中海地区.
- 系统的森林监测对于有效的管理和保护规划至关重要.
研究的目的:
- 开发希腊国家树冠覆盖地图,以20米分辨率.
- 评估机器学习模型对森林属性检索的性能.
- 支持国家和欧盟的自然保护和恢复政策.
主要方法:
- 使用 Sentinel-2 卫星数据,海拔,天花板高度和生态系统类型地图.
- 采用了四种机器学习算法的叠加组合:梯度提升机器,分布式随机森林,通用线性模型和深度学习.
- 为模型评估生成的每像素预测不确定性图.
主要成果:
- 国家树冠覆盖模型实现了R2的0.72和RMSE的18.846%.
- 各个森林类型的模型表现不同 (R2 0.1650.535,RMSE 19.811%25.767%).
- 展示了将机器学习与地球观测集成为标准化森林监测的潜力.
结论:
- 先进的机器学习和遥感数据可以显著提高森林绘图的准确性.
- 开发的方法为森林监测和核算提供了一种标准化的方法.
- 这些发现支持可持续的森林管理和保护政策的实施.
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