Data Assimilation of Growing Stock Volume Using a Sequence of Remote Sensing Data from Different Sensors

Airborne Laser Scanning (ALS) has implied a disruptive transformation of how data are gathered for forest management planning in Nordic countries. We show this study that the accuracy ALS predictions growing stock volume can be maintained and even improved over time if they forecasted assimilated with more frequent but less accurate remote sensing sources like satellite images, digital photogrammetry, InSAR. obtained these results by introducing important methodological adaptations to assimilation compared previous forestry studies Sweden. On test site southwest Sweden (58°27?N, 13°39?E), we evaluated performance extended Kalman filter proposed modified accounts error correlations. also applied classical calibration predictions. developed methods using dataset nine different acquisitions remotely sensed from mix sensors four years, starting ending ALS-based volume. The showed calibrated performed better than standard at endpoint prediction based on an (25.0% RMSE), new (27.5% RMSE).