在伊比利亚半岛的森林中,树冠高度和生物质分布
Yang Su1,2,3, Martin Schwartz4, Ibrahim Fayad4
1Département d'Informatique, École Normale Supérieure - PSL, 45 Rue d'Ulm, 75005, Paris, France. yang.su@ens.fr.
Scientific data
|April 22, 2025
概括
用深度学习和遥感数据为伊比利亚半岛创建了森林树冠高度和生物质的高分辨率地图. 这些地图为森林监测,气候变化减缓和可持续林业实践提供了关键的见解.
科学领域:
- 林业科学 林业科学
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
背景情况:
- 准确的植被树冠高度和生物质测绘对于森林监测,气候变化研究和可持续林业至关重要.
- 现有的方法往往缺乏有效森林管理所需的解决方案或全面覆盖.
研究的目的:
- 为伊比利亚半岛 (2017-2021) 开发高分辨率 (10米为树冠高度,50米为AGB) 森林树冠高度和地面生物质 (AGB) 地图.
- 将多源遥感数据 (Sentinel-1,Sentinel-2,LiDAR) 集成到深度学习框架中,以提高绘图准确度.
主要方法:
- 利用深度学习框架,特别是两个UNET模型,训练在空中激光扫描 (ALS) 和全球生态系统动态调查 (GEDI) 数据上,以估计顶高度.
- 采用随机森林模型,通过将UNET衍生的高度预测与西班牙国家森林库存 (NFI) 数据相关联,来得出地表生物质 (AGB) 估计.
- 与6308个西班牙NFI地块相比,验证了树冠高度预测,在覆盖着树木的地区实现了2-3米的平均绝对误差 (MAE).
主要成果:
- 创建了2017年至2021年伊比利亚森林的高分辨率树冠高度地图 (10米) 和AGB地图 (50米).
- 实现了高精度的树冠高度映射,MAE为1.22m (ALS训练的UNET) 和3.24m (GEDI训练的UNET),在2-3m的外部验证.
- 估计AGB的MAE大约为29mg/ha,提供可靠的生物质分布数据.
结论:
- 开发的高分辨率地图为伊比利亚半岛的森林监测和管理提供了重大进展.
- 这些详细的洞察力支持保护战略,碳捕获评估和可持续森林管理实践的实施.
- 多样化的遥感数据和深度学习的整合为大规模的生态绘图提供了强大的方法.
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