Talks and Poster Presentations (without Proceedings-Entry):
M. Milenkovic, A. Dostálová, M. Hollaus, W. Wagner, N. Pfeifer:
"Using Full-Waveform Airborne LiDAR to Analyze ASAR Time-Series over Forest";
Poster: ForestSAT 2014,
Riva del Garda, Italy;
Measuring and monitoring forest biomass and biomass change at global and regional scales are one of the biggest challenges in forest inventory research today. Current remote sensing approaches for national-wide biomass mapping are mainly based on optical satellite data (e.g. SPOT, Landsat) and k-nearest-neighbor method. However, these require reference data and are often limited due to different radiometric conditions or by cloud cower. The presented work aims at deeper understanding of forest responses to the satellite synthetic aperture radar (SAR) data to provide improved biomass estimates in the future. The main advantage of the microwave domain is its ability to provide both day and night measurements and to penetrate clouds. The data acquired by the ENVISAT Advanced SAR (ASAR) sensor were analyzed with the help of the calibrated full-waveform airborne LiDAR (Light Detection and Ranging) data. The data were analysed over two regions , with the size of ~75 km2 and ~400 km2 each, and both located in the federal state of Burgenland, the eastern part of Austria. This required an appropriate processing of both data sets as well as derivation of representative products. First results of this comparison are presented here and are showing the tendency of the ASAR dry-reference parameter with the increasing forest fraction derived from the LiDAR data. Comparison with several other products confirmed the potential of the ASAR time-series for large-scale biomass mapping.
This work is part of the project Advanced_SAR (Advanced Techniques for Forest Biomass and Biomass Change Mapping Using Novel Combination of Active Remote Sensing Sensors) that aims at novel approaches, based on active remote sensing data, to improve the current biomass estimates at the mentioned scales.
Microwave Remote Sensing; LiDAR; Time Series; Biomass Mapping
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