Accuracy of High-Resolution Radar Images in the Estimation of Plot-Level Forest Variables

Abstract

In the present study, we used the airborne E-SAR radar to simulate the satellite-borne high-resolution TerraSAR radar data and determined the accuracy of the plot-level forest variable estimates produced. Estimation was carried out using the nonparametric k-nearest neighbour (k-nn) method. Variables studied included mean volume, tree species-specific volumes and their proportions of total volume, basal area, mean height and mean diameter. E-SAR-based estimates were compared with those obtained using aerial photographs and medium-resolution satellite image (Landsat ETM+) recording optical wavelength energy. The study area was located in Kirkkonummi, southern Finland. The relative RMSEs for ESAR were 45%, 29%, 28% and 38% for mean volume, mean diameter, mean height and basal area, respectively. For aerial photographs these were 51%, 26%, 27% and 42%, and for Landsat ETM+ images 58%, 40%, 35% and 49%. Combined datasets outperformed all single-source datasets, with relative RMSEs of 26%, 23%, 33% and 39%. Of the single-source datasets, the E-SAR images were well suited for estimating mean volume, while for mean diameter, mean height and basal area the E-SAR and aerial photographs performed similarly and far better than Landsat ETM+. The aerial photographs succeeded well in the estimation of species-specific volumes and their proportions, but the combined dataset was still significantly better in volume proportions. Due to its good temporal resolution, satellite-borne radar imaging is a promising data source for forest inventories, both in large-area forest inventories and operative forest management planning. Future high-resolution synthetic aperture radar (SAR) images could be combined with airborne laser scanner data when estimating forest or even tree characteristics.