# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 5 Discussions and Future Work  
The windstorm analyzed in this study took place during the Finnish summer, on 12 August 2017, wherefore the most important Sentinel-1 scenes were also from the summer. Earlier and other ongoing studies have revealed that late autumn or early winter is the best season for forest parameter estimation from SAR images in the boreal region (e.g., [59; 60]). The results of this study show that the summer SAR images could also be applicable in forest change detection caused by a windstorm damage. One challenge when developing an operational damage monitoring method is that the windstorms occur all around the year, including during winter in Scandinavia and other boreal region. The weather conditions and the temperature can also rapidly change from above to below 0 \({}^{\circ}\)C. Under frozen conditions, a normalized radar cross-section decreases (e.g., [61]). Generally, freeze-thaw environmental transitions affect the classification methods and accuracies if they happen just after or during the damage and if a rapid assessment is needed. This study provides some alternative methods to be developed further to be part of an operational windstorm damage monitoring system. Three different classification algorithm were tested to classify the forest observations on the three categories, non-damaged, severely damaged and slightly damaged forest patches. In total, 27 Sentinel-1 image covers were acquired and originally used, 16 before the damages and 11 after damages, in addition to the field observations and other geo-referenced data. The explanatory variables were derived from the intensities of the two polarizations VV and VH of the Sentinel-1 images and from the quantities of the other geo-referenced data. Two alternative analysis units were tested: (1) the forest stands or forest areas to be cut due to the windstorm damage; and (2) the forest patches constructed using a segmentation algorithm. The main analyses were carried out with the Alternative 1. The data were split to training data and validation for assessing the uncertainties of the results of the different methods. A statistical method was developed to construct the confidence intervals of the probabilities of the estimated damage categories. One goal was to find the minimum number of the images after the damages for a rapid operational monitoring method. Using calculation units that are derived with a segmentation algorithm, that is, the units that are homogeneous with respect to backscattering coefficient, slightly increased the overall accuracies (OAs), and in some cases also the user's accuracies (UAs). In some cases, the UAs and producer's accuracies (PAs) were smaller than with the given boundaries. This may imply that the given boundaries followed the damaged areas or the effect of variation in the image conditions on the results is significant or the segmentation-based approach needs further development. Although some windstorm damage studies have been published so far with space-borne SAR data, quantitative uncertainty assessments are generally lacking. Many comparisons with accuracy figures are thus not possible. Our results are competitive with, e.g., those of [PERSON] et al. [21]. Our study showed that the damages could be identified even a few days after the damage, which is quite unique. On the other hand, we should keep in mind that windstorm damages vary by the areal extent of the damaged forest patches andalso by severity. Furthermore, forest structures and imaging conditions vary wherefore uncertainty quantities are not necessarily comparable. Preliminary tests showed that it could also be possible to develop an unsupervised method for windstorm damage monitoring, that is, to detect the changes without a specific training data. Detection of the windstorm damage is a demanding task.