通过使用机器学习和卫星图像等级方法推进森林碳库存的绘制
Svetlana Illarionova1, Polina Tregubova2, Islomjon Shukhratov2
1Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, 121205, Moscow, Russia. s.illarionova@skoltech.ru.
Scientific reports
|September 9, 2024
概括
这项研究引入了一个使用卫星数据和机器学习来估计森林碳库存的自动化管道. 该方法准确预测森林结构和碳含量,有助于全球气候变化缓解努力.
科学领域:
- 生态生态学 生态生态学
- 林业林业 林业 林业 林业
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 森林监测对生物多样性,资源管理和气候变化缓解至关重要.
- 准确的森林碳库存估计需要详细的物种,年龄,高度和基底面积数据.
- 遥感和机器学习为分析大森林面积提供了可扩展的解决方案.
研究的目的:
- 为森林碳库存估计开发自动机器学习管道.
- 利用免费可用的Sentinel-2卫星观测数据来预测森林结构和碳库存.
- 为大规模森林碳评估提供端到端的解决方案.
主要方法:
- 使用多光谱的Sentinel-2遥感数据.
- 使用极端梯度提升 (XGBoost) 算法进行分类和回归.
- 综合管理层级库存数据作为参考测量.
- 探索了碳库存估计的直接和层次方法.
主要成果:
- 在物种预测方面获得了0.75的F1得分.
- 获得的R平方值为立场年龄为0.75,高度为0.58,基底面积为0.56.
- 通过使用遥感数据和转换因子,成功估计了干碳库存.
结论:
- 开发的管道为大规模森林碳库存估计提供了强大而准确的方法.
- 该方法有效地整合了卫星图像,机器学习和库存数据.
- 这种方法使得全球评估更可靠,即使使用有限的注释数据集.
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