# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 1 Introduction
### Windstorm Damage Studies with SAR
The results show that changes in backscatter relate to the damages caused by the wind and insect outbreaks. In this case, an overall accuracy of 69-84% was achieved for the delineation of areas affected by the wind damage. The study showed that L-band space-borne SAR data can be employed over larger areas and ecosystem types in the temperate and boreal regions to delineate and detect damaged areas. [PERSON] et al. [8] developed a straightforward approach for a rapid windthrow detection in mixed temperate forests using Sentinel-1 C-band VV and VH polarization data. Following radiometric correction of Sentinel-1 scenes acquired approximately 10 days before and 30 days after the storm event, a SAR composite images of before and after the storm were generated. The differences in backscatter before the storm and after the storm in windthrown and in intact forest were studied. A change detection method was developed. Locations of windthrown areas of a minimum extent of 0.5 ha was suggested. The detection was based on user-defined parameters. While the results from the independent study area in Germany indicate that the method is very promising for detecting areal windthrow with a producer's accuracy of 0.88, its performance was less satisfactory at detecting scattered windthrown trees. Moreover, the rate of false positives was low, with a user's accuracy of 0.85 for (combined) areal and scattered windthrown areas. These results underscore that C-band backscatter data have a great potential to rapidly detect the locations of windthrow in mixed temperate forests within approximately two weeks after a storm event. Other methods potentially suitable for mapping windthrown forests with SAR data include approaches demonstrated in other studies of natural and/or anthropogenic forest disturbance. These include mapping snow-damaged forest areas [23], monitoring selective logging and thinning operations in boreal and tropical forest biomes [11; 24; 25; 26; 27; 28], forest clear cutting and other forest changes [29; 30; 31; 32; 33; 34]. A common observation is that, while at L-band direct pixel-wise (or area-based) change detection using averaged-backscatter can be attempted, due to a better sensitivity to forest structure and volume, this does not really work at shorter wavelength such as C-band. Especially in the absence of fully polarimetric SAR capability. At C-band, texture analysis/extraction and subsequent image segmentation should be attempted after speckle is reduced (e.g., using image aggregation of scenes acquired before and after the forest disturbance event). At C-band, falling of trees does not change strongly the total backscatter, since the needles and smaller branches still create \"bright enough\" random-volume layer. Thus, texture from shadows in standing and fallen trees is the key feature to rely upon in the analysis. At even shorter wavelength and even higher resolution, such as X-band, texture analysis becomes the central way to proceed with the change detection, in addition to single pass interferometry with X-band data. Interestingly, most of the studies cited above rely on some kind of bitemporal change detection (even if \"before\" and \"after\" scenes are aggregated in two composite images). This does not really allow analyzing the added value of incorporating additional scenes of temporal dimension into the analysis. However, the idea of using textural features, even at stand level, and follow-up image segmentation appears most fruitful and is adopted and elaborated in our further analysis and methodology development.