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
The fraction of absorbed photosynthetically active radiation (fAPAR) is an essential diagnostic variable to investigate the temporal and spatial dynamics of the terrestrial biosphere. The present study provides a new method to assess global vegetation greening phase dynamics, derived from fAPAR time series from four different remote sensing products. A robust algorithm is developed to detect intra-annual greening phase patterns and derive seasonality patterns of vegetation dynamics at the global scale. The comparison of four independent remote sensing datasets shows significantly consistent global spatiotemporal patterns at the 95% confidence level. Regions where the remote sensing datasets show consistent results, as well as regions where at least one of the used remote sensing datasets deviates, can be identified. The derived global greening phase dataset and analysis method provides a solid framework for the evaluation of global vegetation models.
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