Detection of forest windstorm damages with multitemporal sar data—a case study: Finland

Erkki Tomppo (Corresponding Author), Ghasem Ronoud, Oleg Antropov, Harri Hytönen, Jaan Praks

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)
79 Downloads (Pure)


The purpose of this study was to develop methods to localize forest windstorm dam-ages, assess their severity and estimate the total damaged area using space-borne SAR data. The development of the methods is the first step towards an operational system for near-real-time windstorm damage monitoring, with a latency of only a few days after the storm event in the best case. Windstorm detection using SAR data is not trivial, particularly at C-band. It can be expected that a large-area and severe windstorm damage may affect backscatter similar to clear cutting operation, that is, decrease the backscatter intensity, while a small area damage may increase the backscatter of the neighboring area, due to various scattering mechanisms. The remaining debris and temporal variation in the weather conditions and possible freeze–thaw transitions also affect observed backscatter changes. Three candidate windstorm detection methods were suggested, based on the improved k-nn method, multinomial logistic regression and support vector machine classification. The approaches use multitemporal ESA Sentinel-1 C-band SAR data and were evaluated in Southern Finland using wind damage data from the summer 2017, together with 27 Sentinel-1 scenes acquired in 2017 and other geo-referenced data. The stands correctly predicted severity category corresponded to 79% of the number of the stands in the validation data, and already 75 % when only one Sentinel-1 scene after the damage was used. Thus, the damaged forests can potentially be localized with proposed tools within less than one week after the storm damage. In this study, the achieved latency was only two days. Our preliminary results also indicate that the damages can be localized even without separate training data.

Original languageEnglish
Article number383
Pages (from-to)1-28
Number of pages28
JournalRemote Sensing
Issue number3
Publication statusPublished - 1 Feb 2021
MoE publication typeA1 Journal article-refereed


  • Boreal forest
  • C-band
  • Genetic algorithm
  • Improved k-NN
  • Multinomial logistic regression
  • Sentinel-1
  • Support vector machine
  • Synthetic aperture radar
  • Windstorm damage


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