Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems
Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems
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DOI：
10.3390/rs9121227
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发表时间：
2017-12-01
期刊：
影响因子：
5
通讯作者：
Zona, Donatella
中科院分区：
文献类型：
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作者：
Davidson, Scott J.;Santos, Maria J.;Zona, Donatella
北极冻土带生态系统是甲烷（CH4）的主要来源，其变异受当地环境和气候因素的影响，如地下水位、微地形和植被群落的空间异质性。甲烷通量的测量尺度之间存在脱节，可以用一米分辨率的室和100-1000米的涡动相关塔进行测量，而模式估计通常是在类似于100公里的尺度上进行的。因此，将场地水平测量提升到更大的尺度进行模型比较是至关重要的。由于植被在解释冻土带景观中CH4通量的变异性方面起着关键作用，我们测试了遥感植被图是否可以用于将通量提升到更大的尺度。本研究的目的是比较四种不同的制图方法和两种将地块水平CH4排放升级到EC塔测量值的方法。我们发现线性判别分析（LDA）提供了EC塔足迹内苔原植被的最准确表示（分类精度在65%到88%之间）。无论采用何种制图方法，利用植被群落面积分数的升级后的CH4排放量与EC塔测量值呈正相关（在0.57 ~ 0.81之间）。在使用LDA分类器生成的植被图时，面积加权足迹模型的相关性达到0.88，优于简单的面积加权方法。这些结果表明，冻土带植被的高度空间异质性对通量有很强的影响，其变化表明了环境或气候参数对通量的潜在影响。尽管如此，在足迹模型中同化遥感苔原植被图成功地提高了跨尺度的通量。
Arctic tundra ecosystems are a major source of methane (CH4), the variability of which is affected by local environmental and climatic factors, such as water table depth, microtopography, and the spatial heterogeneity of the vegetation communities present. There is a disconnect between the measurement scales for CH4 fluxes, which can be measured with chambers at one-meter resolution and eddy covariance towers at 100-1000 m, whereas model estimates are typically made at the similar to 100 km scale. Therefore, it is critical to upscale site level measurements to the larger scale for model comparison. As vegetation has a critical role in explaining the variability of CH4 fluxes across the tundra landscape, we tested whether remotely-sensed maps of vegetation could be used to upscale fluxes to larger scales. The objectives of this study are to compare four different methods for mapping and two methods for upscaling plot-level CH4 emissions to the measurements from EC towers. We show that linear discriminant analysis (LDA) provides the most accurate representation of the tundra vegetation within the EC tower footprints (classification accuracies of between 65% and 88%). The upscaled CH4 emissions using the areal fraction of the vegetation communities showed a positive correlation (between 0.57 and 0.81) with EC tower measurements, irrespective of the mapping method. The area-weighted footprint model outperformed the simple area-weighted method, achieving a correlation of 0.88 when using the vegetation map produced with the LDA classifier. These results suggest that the high spatial heterogeneity of the tundra vegetation has a strong impact on the flux, and variation indicates the potential impact of environmental or climatic parameters on the fluxes. Nonetheless, assimilating remotely-sensed vegetation maps of tundra in a footprint model was successful in upscaling fluxes across scales.