Combining Remotely Sensed Optical and Radar Data in kNN-Estimation of Forest Variables

The use of optical and radar data for estimation of forest variables has been investigated and evaluated by employing the k nearest neighbor (kNN) method. The investigation was performed at a test site located in the south of Sweden consisting mainly of Norway spruce and Scots pine forests with standwise stem volume in the range of 0–430 m3 ha–1. The kNN method imputes weighted reference plot variables to areas to be estimated (target areas), facilitating further use of data in forestry planning models. Remotely sensed multispectral optical data from the SPOT-4 XS satellite and radar data from the airborne CARABAS-II VHF SAR sensor were used, separately and combined, to define weights in the kNN algorithm. The weights were inversely proportional to the image feature distance between the reference plot and the target area. The distance metric was defined using regression models based on the image data sources. Positive impact on the accuracies of stem volume and age estimates was found by combining the two image data sources. Stem volume, at stand level, was estimated with a RMSE of 37 m3 ha–1 (22% of the true mean value) using the combination of optical and radar data, compared to 50 m3 ha–1 (30%) for the best single-sensor case in this study. In conclusion, the results indicate that the accuracy of forest variable estimations was substantially improved by using multisensor data.