Active and passive microwave remote sensing of boreal forests

Novel inversion methods are presented for active and passive satelliteborne microwave remote sensing. The objectives are biomass estimation, forest and land-cover-type recognition in boreal forests. A new adaptive inversion method for active sensors was developed for the forest block-wise stem volume estimation from satelliteborne radar images (e.g. JERS-1, ERS-1 SAR and RADARSAT). The inversion results with L-band and/or C-band synthetic aperture radar (SAR) images showed promising accuracies: the relative retrieval rms error varied from 25% to 5% as the size of the forest area varied from 5 to 30 000 h (the forest stem volume varied from 0 to 300 m(3)/ha). The textural information of a seasonal set of satelliteborne radar images was studied with the first- and second-order statistical measures. The multitemporal approach was beneficial for the textural measures in forest and land-cover-type recognition. Based on the SAR image texture, the overall classification accuracy for seven land-cover types was 65%, while with the SAR image intensity, the classification accuracy was 50%, respectively. In the forest-type classification based on the SAR image texture and intensity, the overall classification accuracy for four forest types was 66%, while with the intensity alone the accuracy was 40%, respectively. With the passive microwave sensor (e.g. satelliteborne SSM/I radiometer),the mixed pixel approach was employed for stem volume (biomass) and forest coverage fraction estimation. The results obtained, show that the pixel-wise fractions of water, non-forested, and forested area can be estimated with a rms errors of around 10% units. A new stem volume inversion method for wintertime SSM/I data achieved promising accuracies, the rms error was from 13 to 19 m(3)/ha/pixel (25 km x 25 km) which was 15-16% of the mean stem volume. In the test area, the stem volume ranged from 40 to 60 m(3)/ha/pixel. (C) 2002 International Astronautical Federation. Published by Elsevier Science Ltd. All fights reserved.