Combining SPOT 5 imagery with plotwise and standwise forest data to estimate volume and biomass in mountainous coniferous site

In this study, regression-based prediction of volume and aboveground biomass (AGB) of coniferous forests in a mountain test site was conducted. Two datasets - one with applied topographic correction and one without applied topographic correction - consisting of four spectral bands and six vegetation indices were generated from SPOT 5 multispectral image. The relationships between these data and ground data from field plots and national forest inventory polygons were examined. Strongest correlations of volume and AGB were observed with the near infrared band, regardless of the topographic correction. The maximal correlation coefficients when using plotwise data were -0.83 and -0.84 for the volume and AGB, respectively. The maximal correlation with standwise data was -0.63 for both parameters. The SCS+C topographic correction did not significantly affect the correlations between spectral data and forest parameters, but visually removed much of the topographically induced shading. Simple linear regression models resulted in relative RMSE of 32-33% using the plotwise data, and 43-45% using the standwise data. The importance of the source and the methodology used to obtain ground data for the successful modelling was pointed out.