## 4 Discussion
### A Need for Dense High-Resolution Forest Monitoring, and Future Research Trends  
Given the increasing frequency of disturbances in forests [13], the decreasing resilience of forests [229], or the rising tree mortality due to changing climate conditions and more frequent EWEs [27; 234], comprehensive, temporally, and spatially high-resolution forest monitoring is crucial. However, methodological problems or missing data could be solved in the near future. In the field of remote sensing, there are trade-offs between the spatial and temporal resolution of the sensor [235]. The future of satellite remote sensing data includes smaller and more cost-effective platforms, so-called CubeSats, which operate as a unified system or constellation. CubeSats can provide data with higher spatial resolutions well below 10 m, with a daily revisit time [235; 236]. This allows for the detection of small-scale successional events in forests and the timely detection of damage caused by EWEs. Veryhigh-resolution systems offer the possibility of object-based detection, for example, of forest damage at the individual tree level [102]. In addition to new satellite systems, forest monitoring can be improved by combining [77; 78] and making better use of existing satellite data. As the evaluation of the studies has shown, almost exclusively multispectral data have been used so far, which has the disadvantage of being weather dependent. The SAR sensor of Sentinel-1 can therefore be used as an independent data source for the detection of forest response to extreme events or as a supplement to multispectral timeseries [45; 46]. The positive development in improving forest monitoring is also due to the increasing number of publicly available data archives such as Landsat [57] or the Sentinel series [75; 76]. Regardless of the improving satellite database, the problem of missing in situ data remains [176; 205; 237]. In particular, in situ data can be used to improve the interpretation of remote sensing signals to help verify quality [237; 238] and to gain a better understanding of the forest system. In addition to data improvements and accessibility, methodological options are evolving rapidly. In most of the studies reviewed, correlations between climate extremes and remote sensing signals were measured using simple correlation indicators such as Spearman and Pearson. In contrast, [PERSON] et al. [110] used convergent cross-mapping as a novel technique to uncover non-linear causalities between timeseries data. [PERSON] et al. [239] additionally recommend the use of individual extreme values instead of the commonly used mean values, e.g., to better understand spatial patterns of tree species. The use of artificial intelligence and advanced machine learning offers great potential for climate impact research in forests with remote sensing [224; 240]. Random forest or support vector machines have already been used in many of the studies, often reaching overall accuracies above 0.85 [46; 138; 157; 179]. To reduce the problems of opacity and inexplicability, understanding the processes behind artificial intelligence must not be neglected in the future [241]. The evaluation of the different studies has shown that investigations have already been carried out at all spatial scales. However, there is a direct negative correlation between the depth of the studies and the size of the study area. For example, the effects of extreme events on specific tree species or the effects of different management practices on forest response have only been investigated in very localized study areas. Large-scale studies have been limited to changes in phenology [88; 191], biomass production [107], or forest damage [13], with no differentiation by tree species, stand location, or management type. Finally, there is a need to better understand the response of forests to variables such as soil conditions, management practices, forest structure, tree species distribution, or stand location.