## 4 Results and Discussion
### Information-Enriched Long-Term Vegetation Change  
The LSP-based decomposition of total photosynthetically active vegetation cover into herbaceous and woody components is visualized in Figure 8a for the year 1985. Compared to the total vegetation cover (the sum of blue and green), this split offers another layer of interpretation that readily depicts areas with different vegetation compositions. It is important to note that this split does not produce meaningful results when the underlying assumptions are violated (cf. Section 3.2.2, e.g., for a deciduous forest with a substantial seasonal component, as well as lush grasslands in more protected pockets that do not entirely dry out during summer. These areas are very rare in Crete though, thus we believe that the decomposition provides reliable results at large. The decomposition is highly informative when paired with a trend-and-change analysis, followed by syndrome-based labeling of vegetation dynamics classes (Figure 8b,c; Table 4). Half of Crete's semi-natural areas (i.e., cells of 30 \(\times\) 30 km\({}^{2}\)) do not show substantial changes in woody cover (52%), whereas 8.5% and 19% are subject to gradual decreases and increases, respectively. The remaining 20% were disturbed, and almost half of these were on a recovery trajectory thereafter. The ongoing decline after a disturbance is fairly rare. More than half of all areas do not show strong changes in the herbaceous cover (56.5%), while 15.5% and 28% of the pixels show signs of decreasing and increasing cover, respectively. The accuracy assessment of the syndromes reveals that severe disturbances in the woody vegetation, as well as the absence of a disturbance were detected with high quality. Mild disturbances, however, were detected less reliably such that our expert only confirmed 62% of these algorithmic decisions. In addition, 3.68% of all samples were not labeled as the expert could not make a reliable decision. The trend of the woody vegetation was only assessed for samples where the change detection was positively evaluated, thus reducing the sample size by 99; another eight samples were not labeled as the expert could not make a reliable decision. The trend component, both for the total length of the time series (in case no change was detected), and for the time after a disturbance were confirmed to be of high quality. The same is true for the trend analysis of the herbaceous vegetation. In both cases, the algorithmic decision was rejected in less than 1% of samples. It is acknowledged that our validation is liberal in nature, as the interpreter had access to the algorithm's decision and was tasked to reject decisions that did not match with human perception. Furthermore, this validation should only be interpreted in a qualitative sense, although our feature space (subpixel fractions of vegetation, annual LSP metrics), and observed change phenomena (long-term trends) are quantitative. Thus, it is to be expected that a more conservative validation with an independent and quantitative reference dataset would result in somewhat lower accuracies. However, such data on vegetation components and their fractional cover, as well as long-term trends thereof, are unfortunately not available for the study area, especially when considering the historic investigation period. This is similar to many other regions, where fractional cover reference data do not exist and cannot be created retrospectively either due to the non-existence of high resolution images at required temporal granularity, with Australia being an exception in terms of field data generation (but still for rather contemporary periods) [90]. Nevertheless, our quality assessment still underlines the robustness of this approach, and it is rooted in ecological principles of the seasonal pattern of photosynthetically active vegetation cover of woody and herbaceous vegetation components in semi-arid areas, see e.g., [91].