Vegetation Characteristics Using Multi-return Terrestrial Laser Scanner

Distinguishing vegetation characteristics in a terrestrial laser scanner dataset is an interesting issue for environmental assessment. Methods for filtering vegetation points to distinguish them from ground class have been widely studied mostly on datasets derived from airborne laser scanner, less so for terrestrial laser scanners (TLS). Recent developments in terrestrial laser sensors – further ranges, faster acquisition and multiple return echoes for some models – has risen interest for surface modelling applications. The downside of TLS is that a typical dataset has a very dense cloud, with obvious side-effects on post-processing time. Here we use a scan from a sensor which provides evaluation of multiple target echoes providing with more than 70 million points on our study area. The area presents a complex set of features ranging from dense vegetation undergrowth to very steep and uneven terrain. The method consists on a first step which subsets the original points to define ground candidates by taking into account the ordinal return number and the amplitude. Next a custom progressive morphological filter (closing operation) is applied on ground candidate points using multidimensional (varying resolutions) grids and a structure element to determine cell values. Vegetation density mapping over the area is then estimated using a weighted ration of point counts in the tri-dimensional space over each cell. The overall result is a pipeline for processing TLS points clouds with minimal user interaction, producing a Digital Terrain Model (DTM), a Digital Surface Model (DSM) a vegetation density map and a derived canopy height model (CHM). Results on DTM show an accuracy (RMSE) of 0.307 m with a mean error of 0.0573 m compared to a control DTM extracted from Terrascan’s progressive triangulation procedure. The derived CHM was tested over 30 tree heights resulting in 27 trees having an absolute error value below 0.2 m (three were just below 0.7 m).