We used LiDAR data to generate three spatial models (DTM: Digital Terrain Model, DSM: Digital Surface Model and nDSM: Normalized Digital Surface Model) and two statistics to characterize the forest stands (density of dominant trees per hectare and their average height). This paper aims to identify and characterize woodlands and classify buildings according to their fire risk, for a 36 km2 area in Forcarei (Pontevedra, Spain). Galicia is a region in NW Spain which is usually affected by a high number of forest fires, and it should meet the current regulations regarding the distance between forests and buildings. To a better understanding of the forest features and to explore the potential of remote sensing for such analysis, it was further exploited Digital Terrain Model (DTM), Digital Surface Model (DSM), and Canopy Height Model (CHM) derivation. This study aims to present a processing approach for the determination of each tree’s position (X and Y location, as well as tree height) and its dimensions (crown diameter, area and volume) using geometrically accurate 3D point clouds (data sets were collected in a forested area in Argeș County, Romania). Nowadays, the airborne LiDAR (Light Detection And Ranging) system offers high horizontal resolution as well as vertical dimension information, making it possible to estimate both three-dimensional characteristics of individual trees and to identify the distribution of forest resources in the region. In this context, the three-dimensional (3D) measurement of vegetation structures plays an important role in having an efficient forest inventory and management. The impact of anthropogenic actions on the environment and climate has recently increased the need to map the afforested areas.
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