Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318, Leipzig, Germany.
Department of Computational Hydrosystems, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318, Leipzig, Germany.
Nat Commun. 2019 Nov 8;10(1):5088. doi: 10.1038/s41467-019-13063-y.
Tropical forests play an important role in the global carbon cycle. High-resolution remote sensing techniques, e.g., spaceborne lidar, can measure complex tropical forest structures, but it remains a challenge how to interpret such information for the assessment of forest biomass and productivity. Here, we develop an approach to estimate basal area, aboveground biomass and productivity within Amazonia by matching 770,000 GLAS lidar (ICESat) profiles with forest simulations considering spatial heterogeneous environmental and ecological conditions. This allows for deriving frequency distributions of key forest attributes for the entire Amazon. This detailed interpretation of remote sensing data improves estimates of forest attributes by 20-43% as compared to (conventional) estimates using mean canopy height. The inclusion of forest modeling has a high potential to close a missing link between remote sensing measurements and the 3D structure of forests, and may thereby improve continent-wide estimates of biomass and productivity.
热带雨林在全球碳循环中发挥着重要作用。高分辨率遥感技术，如星载激光雷达，可以测量复杂的热带雨林结构，但如何解释这些信息以评估森林生物量和生产力仍然是一个挑战。在这里，我们通过将 77 万条 GLAS 激光雷达（ICESat）剖面与考虑到空间异质环境和生态条件的森林模拟进行匹配，开发了一种估算亚马逊地区底面积、地上生物量和生产力的方法。这使得我们可以为整个亚马逊地区的关键森林属性生成频率分布。与使用平均冠层高度的（常规）估计相比，这种对遥感数据的详细解释将森林属性的估计提高了 20-43%。将森林建模纳入其中具有在遥感测量和森林的 3D 结构之间建立缺失联系的巨大潜力，从而可能改进对生物量和生产力的全大陆估计。
