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A method to derive vegetation distribution maps for pollen dispersion models using birch as an example

机译:以桦树为例推导花粉扩散模型植被分布图的方法

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Detailed knowledge of the spatial distribution of sources is a crucial prerequisite for the application of pollen dispersion models such as, for example, COSMO-ART (COnsortium for Small-scale MOdeling - Aerosols and Reactive Trace gases). However, this input is not available for the allergy-relevant species such as hazel, alder, birch, grass or ragweed. Hence, plant distribution datasets need to be derived from suitable sources. We present an approach to produce such a dataset from existing sources using birch as an example. The basic idea is to construct a birch dataset using a region with good data coverage for calibration and then to extrapolate this relationship to a larger area by using land use classes. We use the Swiss forest inventory (1 km resolution) in combination with a 74-category land use dataset that covers the non-forested areas of Switzerland as well (resolution 100 m). Then we assign birch density categories of 0%, 0.1%, 0.5% and 2.5% to each of the 74 land use categories. The combination of this derived dataset with the birch distribution from the forest inventory yields a fairly accurate birch distribution encompassing entire Switzerland. The land use categories of the Global Land Cover 2000 (GLC2000; Global Land Cover 2000 database, 2003, European Commission, Joint Research Centre; resolution 1 km) are then calibrated with the Swiss dataset in order to derive a Europe-wide birch distribution dataset and aggregated onto the 7 km COSMO-ART grid. This procedure thus assumes that a certain GLC2000 land use category has the same birch density wherever it may occur in Europe. In order to reduce the strict application of this crucial assumption, the birch density distribution as obtained from the previous steps is weighted using the mean Seasonal Pollen Index (SPI; yearly sums of daily pollen concentrations). For future improvement, region-specific birch densities for the GLC2000 categories could be integrated into the mapping procedure.
机译:对源的空间分布的详细了解是应用花粉弥散模型(例如COSMO-ART(小规模模型研究联合会-气溶胶和反应性痕量气体)的关键先决条件。但是,此输入不适用于与过敏相关的物种,例如榛树,al木,桦树,草或豚草。因此,植物分布数据集需要从合适的来源中获得。我们以桦木为例,提出了一种从现有资源生成此类数据集的方法。基本思想是使用具有良好数据覆盖率的区域构造桦树数据集,然后通过使用土地利用类别将这种关系外推到更大的区域。我们将瑞士森林资源清单(分辨率为1 km)与74类土地使用数据集结合使用,该数据集也涵盖了瑞士的非森林地区(分辨率为100 m)。然后,我们将74%的土地利用类别中的桦树密度类别分别指定为0%,0.1%,0.5%和2.5%。将此导出的数据集与森林资源清单中的桦木分布相结合,可以得出涵盖整个瑞士的相当准确的桦木分布。然后,使用瑞士数据集对2000年全球土地覆盖物(GLC2000;全球土地覆盖物2000数据库,2003年,欧洲委员会,联合研究中心;分辨率1 km)的土地使用类别进行校准,以得出欧洲范围内的桦木分布数据集并汇总到7公里的COSMO-ART网格上。因此,此程序假定某个GLC2000土地使用类别在欧洲可能出现的任何地方都具有相同的桦树密度。为了减少对该关键假设的严格应用,使用平均季节性花粉指数(SPI;每日花粉浓度的年度总和)对从上述步骤获得的桦树密度分布进行加权。为了将来的改进,可以将GLC2000类别的特定于地区的桦木密度集成到映射过程中。

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