The present study addresses the tree counting of a Eucalyptus plantation, the most widely planted hardwood in the world. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) was used for the estimation of Eucalyptus trees. LiDAR-based estimation of Eucalyptus is a challenge due to the irregular shape and multiple trunks. To overcome this difficulty, the layer of the point cloud containing the stems was automatically classified and extracted according to the height thresholds, and those points were horizontally projected. Two different procedures were applied on these points. One is based on creating a buffer around each single point and combining the overlapping resulting polygons. The other one consists of a two-dimensional raster calculated from a kernel density estimation with an axis-aligned bivariate quartic kernel. Results were assessed against the manual interpretation of the LiDAR point cloud. Both methods yielded a detection rate (DR) of 103.7% and 113.6%, respectively. Results of the application of the local maxima filter to the canopy height model (CHM) intensely depends on the algorithm and the CHM pixel size. Additionally, the height of each tree was calculated from the CHM. Estimates of tree height produced from the CHM was sensitive to spatial resolution. A resolution of 2.0 m produced a R2 and a root mean square error (RMSE) of 0.99 m and 0.34 m, respectively. A finer resolution of 0.5 m produced a more accurate height estimation, with a R2 and a RMSE of 0.99 and 0.44 m, respectively. The quality of the results is a step toward precision forestry in eucalypt plantations.

Research on cement-based materials is trying to exploit the synergies that nanomaterials can provide. This paper describes the findings reported in the last decade on the improvement of these materials regarding, on the one hand, their mechanical performance and, on the other hand, the new properties they provide. These features are mainly based on the electrical and chemical characteristics of nanomaterials, thus allowing cement-based elements to acquire “smart” functions. In this paper, we provide a quantitative approach to the reinforcements achieved to date. The fundamental concepts of nanoscience are introduced and the need of both sophisticated devices to identify nanostructures and techniques to disperse nanomaterials in the cement paste are also highlighted. Promising results have been obtained, but, in order to turn these advances into commercial products, technical, social and standardisation barriers should be overcome. From the results collected, it can be deduced that nanomaterials are able to reduce the consumption of cement because of their reinforcing effect, as well as to convert cement-based products into electric/thermal sensors or crack repairing materials. The main obstacle to foster the implementation of such applications worldwide is the high cost of their synthesis and dispersion techniques, especially for carbon nanotubes and graphene oxide.

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