## 3 Results
### Review of Thematic Foci on Extreme Weather Events and Climate Change
#### 3.6.2 Storm  
A total of 11 studies dealing with the effects of storms in temperate forests were found in all the articles reviewed. Common to all of them is that the authors tried to detect damaged or completely destroyed forest patches and are therefore categorized within \"dieback/mortality\". Seven of these studies focused on windthrow. The scope of the studies was limited to smaller areas (<8000 km\({}^{2}\)) compared to drought or trend studies, which cover areas up to the entire northern hemisphere. Also noteworthy was the exclusive use of high-resolution sensors to measure windthrow. The most frequently used sensor was Sentinel-2. Multispectral sensors allow for the calculation of vegetation indices. Since the values of the VIs change [44; 71] or the variance of the VIs increases after a storm event [45], this type of sensor is particularly suitable for detecting possible damage. In the study of [PERSON] et al. [159], the potential of Sentinel-2 to predict storm areas and intensities at a small scale in Germany was investigated. Using logistic regression and random forest (RF) machine learning models, texture measures such as roughness were shown to be the best predictors. Also, [PERSON] et al. [45] used RF with Sentinel-2 scenes to detect wind damage after the Vaia EWE in northern Italy. The results showed a strong negative correlation between the decrease in NDMI (Normalized Difference Moisture Index) or NDVI and the severity of the damage. The authors suggest the use of multiple VIs together to improve RF accuracy.  
The Vaia storm event was the focus of two additional studies. For [PERSON] et al. [44], the NDWI8A (Normalized Difference Wetness Index 8A) and NDWI (Normalized Difference Wetness Index) indices derived from the Sentinel-2 tiles proved to be the most suitable for monitoring windthrow after the Vaia event. The third study on forest damage causedby the Vaia storm event was carried out by [PERSON] et al. [46]. The main focus was the comparison between Sentinel-2 and Sentinel-1. A higher accuracy was achieved with Sentinel-2, although Sentinel-1 offers many advantages for various applications due to its weather independence. These investigations provide examples of how severe storm events can have environmental and economic consequences for temperate forests.  
Sentinel-1 SAR data were used for storm damage detection in another study. [PERSON] et al. [86] used a simple and direct method of change detection, \"image differencing\", by calculating the difference between pre-storm and post-storm Sentinel-1 composites. The validation of their method at an independent test site showed that the method worked better for areal windthrow than for scattered windthrow.  
Sub 10 m high resolution sensors have been used by [PERSON] et al. [103] and [PERSON] et al. [102]. The high resolution of the Formosat-2 satellite enabled the object-based detection of changes in storm-damaged forests in the publication of [PERSON] et al. [102], although the small-scale study was limited to homogeneous stands only. The authors achieved higher overall accuracies (87.8%) for the object-based approach compared to the pixel-based approach, and highlighted the positive correlation between tree age and wind sensitivity. [PERSON] et al. [103] showed that high-resolution satellite data (RapidEye) can be used to determine storm damage very accurately in comparison to methods based on aerial imagery shortly after an event.  
In summary, both optical and SAR data are highly suitable for the detection of windthrow areas. Windthrow is detected with greater accuracy over larger areas, and the age of the trees affects their sensitivity to wind. Furthermore, it was established that storm events are exclusively associated with dieback, with a regional focus on Europe. Four other studies [71, 114, 160, 161] did not deal exclusively with storms, and are described in more detail elsewhere.