Assessing the Robustness of Multispectral Satellite Imagery with LiDAR Topographic Attributes and Ancillary Data to Predict Vertical Structure in a Wet Eucalypt Forest

Remote sensing approaches can be cost-effective for estimating forest structural attributes. This study aims to assess the robustness of multispectral satellite imagery and topographic attributes derived from airborne LiDAR data to predict the density of three vegetation layers in a wet eucalypt forest at the Warra Supersite in Tasmania, Australia. We deployed multiple variables derived from airborne LiDAR data, medium-resolution Landsat-8 Operational Land Imager (OLI) surface reflectance, and high-resolution WorldView-3 satellite imagery. These datasets were combined with topographic attributes extracted from resampled LiDAR data and validated with vegetation density layers extracted from high-density LiDAR. Using spectral bands, indices, texture features, a geology layer, and topographic attributes as predictor variables, we evaluated the predictive power of 13 data schemes at three different pixel sizes (1.6 m, 7.5 m, and 30 m). The schemes of the 30 m Landsat-8 (OLI) dataset provided better model accuracy than the WorldView-3 dataset across all three pixel sizes (R2 values from 0.15 to 0.65) and all three vegetation layers. For predicting the density of the overstorey vegetation, spectral indices (R2 = 0.48) and texture features (R2 = 0.47) were useful, and when both were combined, they produced higher model accuracy (R2 = 0.56) than either dataset alone. Model prediction improved further when all five data sources were included (R2 = 0.65). The best models for mid-storey (R2 = 0.46) and understorey (R2 = 0.44) vegetation had lower predictive capacity than for the overstorey. The models validated using an independent dataset confirmed the robustness. The model accuracies increased with an increase in the number of predictor variables. The spectral indices and texture features derived from the Landsat data products integrated with the low-density LiDAR data can provide valuable information on the forest structure of larger geographical areas for sustainable management and monitoring of forest landscape.