Image segment-based spectral features in the estimation of timber volume

Plot- and stand-level errors associated with satellite image-based multisource forest inventory (MSFI) applications have been relatively high. The reasons suggested for that are related to the limited spatial resolution of the image material. The introduction of very high spatial resolution (VHR) images to MSFI applications should, therefore, diminish these errors. The use of VHR images is, however, problematic, because pixel-by-pixel analysis methods are no longer applicable. The paper presents an image segment-based approach to the determination of feature extraction and image analysis units. The study was carried out in Southern Finland and employed a spectrally averaged imaging spectrometer (AISA) image and field data gathered from sample plots. A two-phase segmentation method was applied and a large number of segment-based spectral features was extracted and used as input to a feature selection procedure. Forward selection based on an improvement of RMSE was applied. The performance of segment-based features (SF) was compared to that of reference features (RF) extracted from square-shaped windows. The estimation results revealed that even though the applied segmentation method succeeded well in the determination of units of feature extraction and image analysis, the differences between the performance of SF and RF were small and the plot-level estimation errors remained high. The study suggests that large estimation errors are due to the local nature of the field data and may be diminished using data that is representative at the segment level.