## 3 Results
### Temporal and Spatial Resolution  
The section below examines the spatial resolution and temporal periods of remote sensing data in the reviewed articles. Figure 6 illustrates the time period covered by the remote sensing data in comparison to the publication dates of the reviewed articles. Each study was categorized, based on the best spatial resolution of the sensors. The categories are as follows: very high spatial resolution (below 10 m), high resolution (10 to 30 m), medium resolution (above 30 to 1000 m), and coarse resolution (more than 1000 m). The remote sensing data time periods are grouped into four categories: mono-temporal (with only one time-step), multi-temporal (with multiple observations in one year), multi-temporal (multiple years with single observations), and timeseries (with at least ten timesteps and multiple recordings over several years). Figure 6 shows that the majority of the reviewed articles used timeseries to detect extreme weather impacts or long-term trends on midlatitude forests. Forest impacts can be measured with their changes. Therefore, the collection of repetitive data is a great advantage. Satellite-based timeseries analysis profits from this feature, and is therefore the most commonly used temporal resolution group. These systems typically have a medium-to-coarse resolution. The MODIS and AVHRR missions have been ongoing for a long time, resulting in a wealth of data. These two sensors are clearly identified in the Figure. The light-blue lines starting in 1982 are associated with studies using AVHRR, and the dark-blue lines starting around 2000 are associated with studies using MODIS. The light-orange lines with long-time periods represent Landsat, which has a long data archive. For example, [PERSON] et al. [101] used the Landsat database to create a complete 42-year timeseries to study the effects of climate change on high mountain ecosystems in Bulgaria.  
Figure 6: Overview of investigated timeframes in reviewed publications. The temporal resolution is depicted using different point and line styles, while colors indicate the spatial resolution (**a**). Summary of the spatial resolution (**b**) and temporal resolution (**c**).  
Overall, coarse (20.6%) and medium resolution (51.6%) studies predominate. This is especially the case in the first years of the publication year timeframe. Almost one fifth (18.3%) of the publications used high resolution data, which increased in 2017 and can be traced back to the start of the Copernicus program. With around one tenth (9.5%) of all publications, the use of very high-resolution data is low, especially in older study periods. Overall, only two of the papers reviewed used high-resolution spaceborne data to assess the impacts of climate change and EWEs in temperate forests. [PERSON] et al. (2012) used an already available high resolution sensor (Formosat-2) to study storm damage in forests after a January 2009 storm event in southwestern France, and [PERSON] et al. (2013) used multi-seasonal RapidEye data to update the forest cover database of the Bavarian Forest National Park.  
Only 6.3% of all studies use mono-temporal data, and 10.3% use multi-temporal data, with observations in one year. Compared to satellite systems with regular repeat cycles, high-resolution sensors mostly use UAVs or aircraft as carrier systems, and therefore have mostly single-date data acquisitions. Consequently, only satellite systems were used to study the impacts of climate or EWEs change on a timeseries basis (78.6% of all) or multi-temporal over several years (4.8% of all).