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UBC Theses and Dissertations

Monitoring fireline construction in near real-time with Sentinel-1 Schreiber, Lukas


Near real-time mapping of anthropogenic linear networks (e.g. roads, seismic lines and fireguards) in forests has a range of applications including monitoring rapid management responses to disturbances such as fire. Synthetic aperture radar imagery is well suited for near real time monitoring because microwaves penetrate clouds and smoke, and satellite images are acquired weekly in many parts of the world, assuring regular coverage. In this study, we created maps of fireguard networks constructed during the 2017 wildfires in Alex Fraser Research Forest in interior British Columbia, Canada based on Sentinel-1 SAR image time series and Sentinel-2 image pairs. I developed two methods to summarize the Sentinel-1 backscatter time series in a single summary raster suitable for human interpretation in Google Earth Engine. The first method is to fit a trend line to the backscatter time series for each pixel, and display the value of this line at the start and end of the observation window in red and green. The second is to fit a single-step function and display the left and right tail values along with the R² value of the fit as red, green and blue values. I assessed the utility of these summary images for manual delineation of fireguard networks by simulating the accuracy and timeliness of fireguard detection based on acquisition in near real-time. For reference, I compared these methods with a straightforward before-after analysis of Sentinel-2 multispectral images and with ground truth maps. From the trend line and step function summary images, interpreters detected 22–41% and 24–55% of fireguard length respectively while delineation from multispectral imagery attained a detection rate of 84–86%. Delineation from Sentinel-2 images was most precise with an average deviation of 5–6 metres from the ground truth, followed by the trend image with 8–15 metres deviation and the step image with 11–16 metres. In the best case, a change was detected based on a step image within 6 days. The developed method can be used to monitor linear feature construction where more accurate methods are unavailable.

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