Regional-scale mapping of tree cover, height and main phenological tree types using airborne laser scanning data

Airborne Laser Scanning (ALS) data for generating Digital Terrain Models (DTM) at national level are often collected during leaf-off seasons. Leaf-off data are useful for classifying evergreen and deciduous trees since echoes at lower intensity are returned from deciduous trees when compared to evergreen trees. In addition to this, the proportion of echoes from the ground is higher for deciduous trees than for evergreen trees. In this study, we classified land cover, including evergreen and deciduous trees, using a Random Forest classifier based on LiDAR-metrics generated from leaf-off ALS data, and estimated tree cover and tree heights for the whole of Denmark. The results were compared with the CORINE Land Cover (CLC2006) data, percentage of tree cover from MODIS Vegetation Continuous Fields (VCF) and a global tree height map based on ICESat data. Considering tree class alone, deciduous and evergreen trees could be classified with an overall accuracy of 94% using validation data generated using aerial imagery from a 60-km strip across Central Jutland. The lower values of ALS-based percentage tree cover were overestimated and the higher values underestimated by MODIS VCF data, with a root-mean-square (RMS) deviation of 18.26%. The tree heights estimated using ALS data were generally lower than the global estimates of tree height with an RMS deviation of 5.1 m. The ALS intensity values were useful for classifying evergreen and deciduous trees. These findings show that ALS datasets collected for generating national DTMs can be used for tree cover and tree height mapping, as well as for regional classification of trees if data over the whole area are collected within a few months in the leaf-off season. (C) 2014 Elsevier Inc All rights reserved.