Radar backscattering of forest stands

Research on the potential applications of microwave remote sensing in agriculture is conducted in the Netherlands by the ROVE team. Since active microwave remote sensing, featuring its all-weather capability, also seems to be a promising tool for forest classification, especially on a global scale, the Wageningen Agricultural University started a new working group in co-operation with the ROVE team in order to explore this field of application. Results of four X-band SLAR flights have been analysed. The digital radar images obtained are accurately corrected both geometrically and radiometrically and indicate gamma values instead of arbitrary grey tones. Radar signatures, showing seasonal and angular effects, of 16 classes of forest stands have been derived from the images. Special attention has been paid to the statistical properties of the radar signatures and their impact on classification accuracy. Several interesting phenomena have been observed indicating effects of vegetation structure on radar backscattering. One of the test areas is a young forest in the Oost-Flevoland polder featuring a substantial variety of species; parcels are relatively large, rectangular in shape, homogeneous in structure and age and with pure species stands making this an ideal test site. Another test area is an old forest located at the Veluwe. Much variation in age is present here, which made it possible to determine relationships between tree age and radar backscatter for several coniferous tree species. The initial work as presented here clearly demonstrates the appropriateness of X-band ability in the classification of (Dutch) forests. Theoretical considerations suggest that a multitemporal approach is likely to give the most accurate results of tree-type classification. A classification simulation yielded overall error fractions ranging from 10 to 16 per cent at the Oost-Flevoland polder test area and 14 to 28 per cent at the Veluwe test area. This can be demonstrated in multitemporal radar images as well as in actual classified images.