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Consequences of fine-scale heterogeneity on predictions of the carbon cycle using lidar data and a height-structured ecosystem model.

机译：利用激光雷达数据和高度结构化的生态系统模型预测碳循环的精细尺度异质性的后果。

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To more accurately predict carbon stocks and fluxes in forests, it is important to measure fine-scale heterogeneity in ecosystem structure across the landscape, and incorporate the underlying mechanisms responsible for the observed heterogeneity in ecosystem models. This study used large-footprint lidar and a height-structured ecosystem model to estimate carbon stocks and fluxes at Hubbard Brook Experimental Forest (HBEF). At HBEF elevation gradients yield a decline in aboveground carbon stock, due to changes in net growth rates and disturbance at higher elevations. Lidar and a height structured ecosystem model can accurately quantified aboveground carbon stocks. Estimates of aboveground carbon fluxes depended on the availability of lidar data, the representation of fine-scale heterogeneity in climate and soil inputs, and the simulation of spatial variation in disturbance. Predictions of forest structure depended strongly on simulating the mechanisms that drive heterogeneity in forest structure across the landscape.
机译：为了更准确地预测森林中的碳储量和通量，重要的是测量整个景观中生态系统结构的精细异质性，并纳入造成生态系统模型异质性的潜在机制。这项研究使用大型足迹激光雷达和高度结构化的生态系统模型来估算哈伯德布鲁克实验林（HBEF）的碳储量和通量。在HBEF海拔高度，由于净增长率的变化和较高海拔的干扰，导致地面碳储量下降。激光雷达和高度结构化的生态系统模型可以准确定量地上碳储量。地上碳通量的估计取决于激光雷达数据的可用性，气候和土壤输入中细尺度异质性的表示以及扰动空间变化的模拟。森林结构的预测在很大程度上取决于模拟驱动景观中森林结构异质性的机制。

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