基于ICESat-2数据和机器学习的全球30米年均植被高度地图 (2000-2022年)
Maria O Hunter1, Leandro Parente2, Yu-Feng Ho2
1Remote Sensing and GIS Laboratory (LAPIG/UFG), Goiânia, Brazil. maria.hunter@ufg.br.
Scientific data
|August 23, 2025
概括
科学家使用卫星数据为非森林生态系统创建了第一个全球植被中位数高度的地图. 这种新工具为了解全球生态系统结构和生物多样性提供了关键数据.
科学领域:
- 生态与遥感
- 地理空间分析
- 环境监测
背景情况:
- 精确的植被高度测量对于生态系统评估,碳储存和生物多样性研究至关重要.
- 现有的全球高度模型主要关注森林,忽视了草生和灌木生态系统的关键数据.
- 对于非森林环境的全球植被结构数据存在差距.
研究的目的:
- 开发所有生态系统,包括矮植被的植被中位数高度的第一个全球估计.
- 从2000年到2022年提供每年30米分辨率的植被高度数据.
- 在全球模型中增强植被高度数据的详细性和异质性.
主要方法:
- 使用ICESat-2卫星的Lidar和Landsat无云复合材料,以及其他地球观测光谱数据.
- 开发了一组渐变增强树 (GBT) 模型,使用了10个独立的ICESat-2段.
- 估计90%的预测间隔来量化模型的不确定性.
主要成果:
- 在测试组件中获得2.35米的平均平方误差 (RMSE),0.515的R2和0.62的D2回归得分.
- 在2000年至2022年期间以30米分辨率生成全球年均植被高度地图.
- 与现有产品相比,在短植被生态系统中表现出更多的细节和异质性.
结论:
- 开发的模型成功地提供了植被中位数高度的全面全球估计,填补了关键数据缺口.
- 这种方法增强了非森林生态系统中的植被结构的特征.
- 输出地图,参考样本和训练模型在CC-BY许可证下公开提供,以支持进一步的研究.
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