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 m<sup>2</sup>. 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.
Highlights
Since 1991 radar data are available on a continuous basis from different sensors (e.g. ERS-1, ERS-2, JERS, SIR-C/X-Synthetic Aperture Radar (SAR), RADARSAT, SRTM, EnviSAT-ASAR, RADARSAR-II, LIGHTSAR, ALOS-PALSAR, TerraSAR-X)
The main advantages are the capability for mapping vegetation cover in regions characterized by frequent cloud cover as for example tropical and boreal forests and the provision of time series data with high calibration stability as e.g. achieved with ERS-1 and ERS-2 data
The objective of this paper is to show first results of delineated forest areas from multi-temporal Sentinel-1 data from an Austrian study site
Summary
Since 1991 radar data are available on a continuous basis from different sensors (e.g. ERS-1, ERS-2, JERS, SIR-C/X-SAR, RADARSAT, SRTM , EnviSAT-ASAR, RADARSAR-II, LIGHTSAR, ALOS-PALSAR, TerraSAR-X). In addition to being almost insensitive to weather conditions, SAR data is a useful data source providing information on the structure and moisture status that is complementary to the information provided by optical remote sensing (Toan et al, 1998) These strengths were used in a multitude of studies e.g. on forest mapping (Dontchenko et al, 1999; Dwyer et al, 2000; Quegan et al, 2000; Sgrenzaroli et al, 2002; Strozzi et al, 1998; Wagner et al, 2003), forest change detection (Gimeno et al, 2002; Hese and Schmullius, 2003; Saatchi et al, 1997) and biomass measurements (Dobson et al, 1992; Toan et al, 1992; Toan and Floury, 1998; Wagner et al, 2003). Due to the high temporal coverage of Sentinel-1 data with an acquisition up to every three days in central Europe, the advantage of multi- and hyper-temporal. The results are validated with a reference forest mask derived from airborne full-waveform laser scanning data
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