FOREST AREA DERIVATION FROM SENTINEL-1 DATA
Keywords: Classifications, time series, SAR, forest area, full-waveform ALS, LiDAR
Abstract. The recently launched Sentinel-1A provides the high resolution Synthetic Aperture Radar (SAR) data with very high temporal coverage over large parts of European continent. Short revisit time and dual polarization availability supports its usability for forestry applications. The following study presents an analysis of the potential of the multi-temporal dual-polarization Sentinel-1A data for the forest area derivation using the standard methods based on Otsu thresholding and K-means clustering. Sentinel-1 data collected in winter season 2014-2015 over a test area in eastern Austria were used to derive forest area mask with spatial resolution of 10m and minimum mapping unit of 500 m2. The validation with reference forest mask derived from airborne full-waveform laser scanning data revealed overall accuracy of 92 % and kappa statistics of 0.81. Even better results can be achieved when using external mask for urban areas, which might be misclassified as forests when using the introduced approach based on SAR data only. The Sentinel-1 data and the described methods are well suited for forest change detection between consecutive years.