USING THE TWO-LEVEL MODEL WITH TANDEM-X FOR LARGE-SCALE FOREST MAPPING - Persson, Henrik J.; Soja, Maciej J.; Fransson, Johan E. S.; Ulander, Lars (IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019)
This study applies the two-level model to predict stem volume (VOL), presented as wall-to-wall rasters. The SAR data were acquired with the TanDEM-X system and 518 scenes covered the entire Sweden. For comparison, a multiple linear regression model is also evaluated. Compared to earlier studies, the model parameters are fitted separately for each satellite scene. The prediction accuracy at the stand-level is evaluated using field inventoried reference stands within one scene, located in Northern Sweden and provided by a Swedish forest company. The results from the two models were similar, with an RMSE of 34.8 m(3)/ha and 32.9 m(3)/ha at the stand-level, respectively, and the corresponding biases were 14.3 m(3)/ha and 12.1 m(3)/ha. The error is significantly lower, compared to a previous study (52-65 m(3)/ha) where a universal multiple linear regression model was used for all scenes. It can be concluded, that using model parameters fitted at the local scene appears to improve the prediction performance in terms of RMSE, but no significant difference could be determined between predictions based on the two-level model or multiple linear regression, evaluated in this study.