Vegetation Dynamics on Crete

This dataset contains long-term vegetation dynamics assessments for the Island of Crete, Greece. 10+ years ago, Landsat time series analyses were inevitably limited to few expensive images from carefully selected acquisition dates. Yet, such a static selection may have introduced uncertainties when spatial or inter-annual variability in seasonal vegetation growth were large. As seminal pre-open-data-era papers are still heavily cited, variations of their workflows are still widely used, too. Thus, we here quantitatively assessed the level of agreement between an approach using carefully selected images, and a state-of-the-art analysis that uses all available images. Further details can be found in the corresponding paper. Temporal Extent: 1984-2006. Spatial Extent: Crete, Greece Data format: The data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There are mosaics in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Sub-directories: cso: Number of Clear-Sky Observations available for this study dem: Digital Elevation Model (SRTM filled with ASTER) vd-1.0: Vegetation Dynamics 1.0 (see paper) bap: Best Available Pixel composites trend: monotonic trends fitted on BAPs vd-2.0: Vegetation Dynamics 2.0 (see paper) trend monotonic trends fitted on value of peak of season phenometrics (VPS) monotonic trends fitted on value of seasonal amplitude phenometrics (VSA) change and piecewise monotonic trends fitted on value of base level phenometrics (VBL) syndromes syndrome classification for woody vegetation syndrome classification for herbaceous vegetation net cover change for woody vegetation net cover change for herbaceous vegetation Further information: For further information, please see the publication. A web-visualization of this dataset is available here. Publication: Frantz, D., Hostert, P., Rufin, P., Ernst, S., Röder, A., van der Linden, S. (2022): Revisiting the past: Replicability of a historic long-term vegetation dynamics assessment in the era of big data analytics. Remote Sensing 14(3), 597; https://doi.org/10.3390/rs14030597 Funding: This publication was financially supported by Geo.X, the Research Network for Geosciences in Berlin and Potsdam under grant number SO_087_GeoX, and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 414984028 - SFB 1404.