Classification of rural landscapes from low-density lidar data: is it theoretically possible?

Lidar technology has become an important data source in 3D terrain modelling. In Spain, the National Plan for Aerial Orthophotography will soon release public low-density lidar data (0.5–1 pulses/m2) for most of the country territory. Taking advantage of this fact, this article experimentally assesses the possibility of classifying a rural landscape into eight classes using multitemporal and multidensity lidar data and analyses the effect of point density on classification accuracy. Two statistical methods (transformed divergence and the Jeffries–Matusita distance) were used to assess the possibility of discriminating the eight classes and to determine which data layers were best suited for classification purposes. The results showed that ‘dirt road’ cannot be discriminated from ‘bare earth’ and that the possibility of discriminating ‘bare earth’, ‘pavement’, and ‘low vegetation’ decreases when using densities below 4 pulses/m2. Two non-parametric tests, the Kruskal–Wallis test and the Friedman test, were used to strengthen the results by assessing their statistical significance. According to the results of the Kruskal–Wallis test, lidar point density does not significantly affect the classification, whereas the results of the Friedman test show that bands could be considered as the only parameter affecting the possibility of discriminating some of the classes, such as ‘high vegetation’. Finally, the J48 algorithm was used to perform cross-validation in order to obtain the most familiar quantitative values in the international literature (e.g. overall accuracy). Mean overall accuracy was around 85% when the eight classes were considered and increased up to 95% when ‘dirt road’ was disregarded.