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30米空间分辨率全国森林冠层高度分布产品
The forest canopy height distribution of China at 30 m resolution
刘小强,苏艳军,胡天宇,方精云,唐志尧,唐昊,郭庆华(中国科学院植物研究所,北京大学,新加坡国立大学)
发布时间:2021/12/16 9:12:46
摘要
国家与全球尺度的高分辨率森林冠层高度产品对估算森林碳储存、理解森林生态系统过程、以及制定森林经营政策等至关重要。目前,现有的森林冠层高度产品的空间分辨率一般是500米或1000米,难以满足应用需求。新近发射的星载激光雷达传感器GEDI与ICESat-2 ATLAS可以获取全球范围内分米级空间分辨率的森林冠层高度脚点数据,为大尺度森林冠层高度的高分辨率绘制提供了可能。但是星载激光雷达获取的脚点数据是沿卫星轨道离散分布的,无法直接生成空间连续的森林冠层高度产品。以往的研究通常利用星载激光雷达脚点数据与遥感影像和环境因子建立回归模型生成空间连续的大尺度森林冠层高度产品,该方法难以充分利用GEDI与ICESat-2 ATLAS较为密集的脚点数据,并且容易受到遥感影像饱和效应的影响。本产品采用了一种深度学习指导的空间内插模型,利用深度神经网络自动学习权重的能力,解决了如何在内插模型中均衡考虑空间、环境与光谱等特征距离的权重贡献,突破了常用内插模型仅使用空间距离的局限。借助140平方公里的无人机激光雷达数据训练了该空间内插模型,内插GEDI与ICESat-2 ATLAS星载激光雷达脚点数据,成功绘制了全国30米分辨率的森林冠层高度产品。通过与三套独立的验证数据集(超过100万个的GEDI 脚点数据、33平方千米的无人机激光雷达数据和近6万条的森林清查数据)相比,这套全国森林冠层高度产品的精度较高(R2≥0.55,RMSE≤5.5 m),受益于空间内插策略,本产品在森林冠层较高的地区几乎没有出现饱和。这套高精度、高空间分辨率的全国森林冠层高度产品有助于提升大尺度森林生物量估算的准确性,为制定气候减缓政策与实现“碳中和”目标提供数据支持。
Spatially continuous estimates of forest canopy height at national to global scales are critical for quantifying forest carbon storage, understanding forest ecosystem processes, and developing forest management and restoration policies to mitigate global climate change. Spaceborne light detection and ranging (lidar) platforms, especially the Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) Advanced Topographic Laser Altimeter System (ATLAS), can measure forest canopy height in discrete footprints globally. Their coverage provides a promising data source for national to global-scale forest canopy height estimates. However, previous studies usually used a regression-based approach to develop spatially continuous forest canopy height distribution through the aid of optical images, which cannot take full advantage of the dense spaceborne lidar footprints and may still suffer from the saturation effect of optical images. We developed a novel neural network guided interpolation (NNGI) method to map China's forest canopy height by fusing GEDI, ICESat-2 ATLAS, and Sentinel-2 images. We evaluated the interpolated forest canopy height product of China by over 1,100,000 GEDI validation footprints (R2=0.55, RMSE=5.32 m), about 33 km2 drone-lidar validation data (R2=0.58, RMSE=4.93 m), and over 59,000 field plot measurements (R2=0.60, RMSE=4.88 m). Benefiting from the interpolation-based mapping strategy, the resulting product had almost no saturation effect in areas with tall forest canopies. The interpolated forest canopy height with high mapping accuracy and high spatial resolution is likely beneficial for improving the accuracy of forest biomass mapping and informing climate mitigation policy initiatives to achieve China's goal of carbon neutrality.
关键词
森林冠层高度,无人机激光雷达,星载激光雷达,遥感影像
数据引用
必引文献: Liu, X., Su, Y., Hu, T. et al. 2021. Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2021.112844.
数据引用: 数据来源于:30米空间分辨率全国森林冠层高度分布产品,刘小强,苏艳军,胡天宇,方精云,唐志尧,唐昊,郭庆华(中国科学院植物研究所,北京大学,新加坡国立大学), 2021, 中国科学院植物科学数据中心

Normative references:The forest canopy height distribution of China at 30 m resolution, 2021, Plant Data Center of Chinese Academy of Sciences
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中文致谢方式:
  感谢中国科学院植物科学数据中心(Plant Data Center of Chinese Academy of Sciences, https://www.plantplus.cn) 提供数据支撑。
To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.
Acknowledgements:
  Acknowledgement for the data support from Plant Data Center of Chinese Academy of Sciences (https://www.plantplus.cn).
数据服务
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元数据:
地理范围
东经E:72-138
北纬N:17-55
空间分辨率:100m
数据提供者
数据提供机构:中国科学院植物研究所,北京大学,新加坡国立大学
数据提供者:刘小强,苏艳军,胡天宇,方精云,唐志尧,唐昊,郭庆华
联系人:刘小强
联系电话:18518602018
电子邮箱:liuxiaoqiang@ibcas.ac.cn