Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery

Spatially-explicit information on forest structure is paramount to estimating aboveground carbon stocks for designing sustainable forest management strategies and mitigating greenhouse gas emissions from deforestation and forest degradation. LiDAR measurements provide samples of forest structure that must be integrated with satellite imagery to predict and to map landscape scale variations of forest structure. Here we evaluate the capability of existing satellite synthetic aperture radar (SAR) with multispectral data to estimate forest canopy height over five study sites across two biomes in North America, namely temperate broadleaf and mixed forests and temperate coniferous forests. Pixel size affected the modelling results, with an improvement in model performance as pixel resolution coarsened from 25 m to 100 m. Likewise, the sample size was an important factor in the uncertainty of height prediction using the Support Vector Machine modelling approach. Larger sample size yielded better results but the improvement stabilised when the sample size reached approximately 10% of the study area. We also evaluated the impact of surface moisture (soil and vegetation moisture) on the modelling approach. Whereas the impact of surface moisture had a moderate effect on the proportion of the variance explained by the model (up to 14%), its impact was more evident in the bias of the models with bias reaching values up to 4 m. Averaging the incidence angle corrected radar backscatter coefficient (γ°) reduced the impact of surface moisture on the models and improved their performance at all study sites, with R2 ranging between 0.61 and 0.82, RMSE between 2.02 and 5.64 and bias between 0.02 and −0.06, respectively, at 100 m spatial resolution. An evaluation of the relative importance of the variables in the model performance showed that for the study sites located within the temperate broadleaf and mixed forests biome ALOS-PALSAR HV polarised backscatter was the most important variable, with Landsat Tasselled Cap Transformation components barely contributing to the models for two of the study sites whereas it had a significant contribution at the third one. Over the temperate conifer forests, Landsat Tasselled Cap variables contributed more than the ALOS-PALSAR HV band to predict the landscape height variability. In all cases, incorporation of multispectral data improved the retrieval of forest canopy height and reduced the estimation uncertainty for tall forests. Finally, we concluded that models trained at one study site had higher uncertainty when applied to other sites, but a model developed from multiple sites performed equally to site-specific models to predict forest canopy height. This result suggest that a biome level model developed from several study sites can be used as a reliable estimator of biome-level forest structure from existing satellite imagery.