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RSS FeedsRemote Sensing, Vol. 11, Pages 115: Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data (Remote Sensing)

 
 

10 january 2019 21:00:12

 
Remote Sensing, Vol. 11, Pages 115: Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data (Remote Sensing)
 




Storm events are capable of causing windthrow to large forest areas. A rapid detection of the spatial distribution of the windthrown areas is crucial for forest managers to help them direct their limited resources. Since synthetic aperture radar (SAR) data is acquired largely independent of daylight or weather conditions, SAR sensors can produce temporally consistent and reliable data with a high revisit rate. In the present study, a straightforward approach was developed that uses Sentinel-1 (S-1) C-band VV and VH polarisation data for a rapid windthrow detection in mixed temperate forests for two study areas in Switzerland and northern Germany. First, several S-1 acquisitions of approximately 10 before and 30 days after the storm event were radiometrically terrain corrected. Second, based on these S-1 acquisitions, a SAR composite image of before and after the storm was generated. Subsequently, after analysing the differences in backscatter between before and after the storm within windthrown and intact forest areas, a change detection method was developed to suggest potential locations of windthrown areas of a minimum extent of 0.5 ha—as is required by the forest management. The detection is based on two user-defined parameters. While the results from the independent study area in Germany indicated 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 great potential to rapidly detect the locations of windthrow in mixed temperate forests within a short time (approx. two weeks) after a storm event. Furthermore, the two adjustable parameters allow a flexible application of the method tailored to the user’s needs.


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9 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 117: Full-Waveform LiDAR Fast Analysis of a Moderately Turbid Bay in Western France (Remote Sensing)
Remote Sensing, Vol. 11, Pages 116: A Unified Model for Multi-Frequency PPP Ambiguity Resolution and Test Results with Galileo and BeiDou Triple-Frequency Observations (Remote Sensing)
 
 
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