Department of Geographical Sciences, University of Maryland at College Park, College Park, Maryland, USA.
Department of Geography, National University of Singapore, Singapore.
Glob Chang Biol. 2023 Jun;29(12):3378-3394. doi: 10.1111/gcb.16682. Epub 2023 Apr 4.
Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.
森林碳是全球碳循环中一个庞大而不确定的组成部分。造成这种复杂性的一个重要原因是植被垂直结构和范围的空间异质性，这种异质性是由气候、土壤和干扰的变化造成的，它同时影响着当代的碳储量和通量。遥感和生态系统建模的最新进展有可能极大地改善植被结构的特征及其对碳的影响。在这里，我们使用了两项美国宇航局（NASA）星载激光雷达任务——全球生态系统动力学调查（GEDI）和冰、云和陆地高程卫星 2（ICESat-2）——收集的树冠高度的新型遥感观测数据，以及一个新开发的全球生态系统动态模型（v3.0），来描述全球森林结构的空间异质性，并量化其对森林碳储量和通量的相应影响。多尺度评估结果表明，与其他估计值（包括实地清查、基于遥感的产品和国家统计数据）相比，结果较为有利。然而，这种方法在植被结构上使用了数量级多得多的数据（37.7 亿个激光雷达样本），而以前使用的数据则少得多，这使得模型估计的空间分辨率有了定性的提高（从 0.25°到 0.01°）。在这个分辨率下，基于过程的模型现在能够捕捉到以前无法获得的森林结构的详细空间模式，包括自然和人为干扰以及恢复的模式。通过新的遥感数据和生态系统建模的新颖集成，本研究弥合了现有的基于经验的遥感方法和基于过程的建模方法之间的差距。这项研究更广泛地证明了星载激光雷达观测在全球范围内推进碳建模的有前途的价值。
