Combining low-density LiDAR and satellite images to discriminate species in mixed Mediterranean forest

Abstract

Key message: Using a combination of remote sensing data,Pinus pinasterAit. andPinus pineaL. were distinguished at individual tree level in mixed Mediterranean stands with over 95% accuracy. This approach is easily applicable over large areas, enhancing the economic value of non-wood forest products, stone pine nuts, and resin, and aiding forest managers to accurately predict this production. Context: The discrimination of tree species at individual level in mixed Mediterranean forest based on remote sensing is a field which has gained greater importance. In these stands, the capacity to predict the quality and quantity of non-wood forest products is particularly important due to the very different goods the two species produce. Aims: To assess the potential of using low-density airborne LiDAR data combined with high-resolution Pleiades images to discriminate two different pine species in mixed Mediterranean forest (Pinus pinea L. and Pinus pinaster Ait.) at individual tree level. Methods: A Random Forest model was trained using plots from the pure stand dataset, determining which LiDAR and satellite variables allow us to obtain better discrimination between groups. The model constructed was then validated by classifying individuals in an independent set of pure and mixed stands. Results: The model combining LiDAR and Pleiades data provided greater accuracy (83.3% and 63% in pure and mixed validation stands, respectively) than the models which only use one type of covariables. Conclusion: The automatic crown delineation tool developed allows two very similar species in mixed Mediterranean conifer forest to be discriminated using continuous spatial information at the surface: Pleiades images and open source LiDAR data. This approach is easily applicable over large areas, enhancing the economic value of non-wood forest products and aiding forest managers to accurately predict production.