Mapping the local variability of Natura 2000 habitats with remote sensing
Mapping the local variability of Natura 2000 habitats with remote sensing
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DOI：
10.1111/avsc.12115
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发表时间：
2014-10-01
影响因子：
2.8
通讯作者：
Stenzel, Stefanie
中科院分区：
文献类型：
--
作者：
Feilhauer, Hannes;Dahlke, Carola;Stenzel, Stefanie
我们可以使用多光谱遥感数据绘制离散的Natura 2000栖息地类型及其植物区系变异性吗？与全范围成像光谱数据相比，这些数据表现如何？遥感数据的哪些光谱和空间特征对生境及其变异性的准确制图很重要？LocationA沼泽复杂的巴伐利亚州，南部德国。MethodsTo比较成像光谱和多光谱遥感数据的性能，机载光谱数据（AISA双）进行光谱和空间重采样的两个国家的最先进的多光谱传感器（RapidEye和哨兵-2）的特性，从而在三个数据集具有不同的光谱和空间分辨率。基于这三个数据集，我们使用了实地调查，排序技术（非度量多维尺度），以及回归和分类技术（随机森林）的组合，以获得自然2000栖息地类型的分布图及其组成的变化。随后，我们分析了空间和光谱图像分辨率和光谱覆盖率的映射performance.ResultsMire栖息地类型和它们的植物区系组成的影响，可以准确地映射与多光谱遥感数据。在强调栖息地之间的植物区系差异的情况下，三个传感器的模型的拟合仅略有不同。这些影响和空间分辨率的重要性进行了讨论。ConclusionsThe结果是令人鼓舞的，并证实，多光谱数据可以允许离散的栖息地和他们的本地变异的组合映射。尽管如此，问题的可移植性的方法，以生境类型与不太明显的光谱差异，并在缩小差距之间的差距，精细尺度植被记录和粗分辨率图像仍然开放。
QuestionsCan we map both discrete Natura 2000 habitat types and their floristic variability using multispectral remote sensing data? How do these data perform compared to full range imaging spectroscopy data? Which spectral and spatial characteristics of remote sensing data are important for accurate mapping of habitats and their variability?LocationA mire complex in Bavaria, southern Germany.MethodsTo compare the performance of imaging spectroscopy and multispectral remote sensing data, airborne spectroscopy data (AISA Dual) were spectrally and spatially resampled to the characteristics of two state-of-the-art multispectral sensors (RapidEye and Sentinel-2), resulting in three data sets with different spectral and spatial resolution. Based on the three data sets, we used a combination of field surveys, ordination techniques (non-metric multidimensional scaling), as well as regression and classification techniques (Random Forests) to derive maps of the distribution of Natura 2000 habitat types and their compositional variability. Subsequently, we analysed effects of the spatial and spectral image resolution and spectral coverage on the mapping performance.ResultsMire habitat types and their floristic composition could be accurately mapped with multispectral remote sensing data. In the case of accentuated floristic differences between habitats, the fits of the models for the three sensors differed only marginally. These effects and the importance of the spatial resolution are discussed.ConclusionsThe results are encouraging and confirm that multispectral data may allow the combined mapping of discrete habitats and their local variability. Still, questions with respect to the transferability of the approach to habitat types with less pronounced spectral differences, and with regard to bridging the gap between fine-scale vegetation records and coarse resolution imagery remain open.