Abstract

Forest structure attributes produced from terrestrial laser scanning (TLS) rely on normalisation of the point cloud values from sensor coordinates to height above ground. One method to do this is through the derivation of an accurate and repeatable digital elevation model (DEM) from the TLS point cloud that is used to adjust the height. The primary aim of this paper was to test a number of TLS scan configurations, filtering options and output DEM grid resolutions (from 0.02 m to 1.0 m) to define a best practice method for DEM generation in sub-tropical forest environments. The generated DEMs were compared to both total station (TS) spot heights and a 1-m DEM generated from airborne laser scanning (ALS) to assess accuracy. The comparison to TS spot heights found that a DEM produced using the minimum elevation (minimum Z value) from a point cloud derived from a single scan had mean errors >1 m for DEM grid resolutions <0.2 m at a 25-m plot radius. At a 1-m grid resolution, the mean error was 0.19 m. The addition of a filtering approach that combined a median filter with a progressive morphological filter and a global percentile filter was able to reduce mean error of the 0.02-m grid resolution DEM to 0.31 m at a 25-m plot radius using all returns. Using multiple scan positions to derive the DEM reduced the mean error for all DEM methods. Our results suggest that a simple minimum Z filtering DEM method using a single scan at the grid resolution of 1 m can produce mean errors <0.2 m, but for a small grid resolution, such as 0.02 m, a more complex filtering approach and multiple scan positions are required to reduced mean errors. The additional validation data provided by the 1-m ALS DEM showed that when using the combined filtering method on a point cloud derived from a single scan at the plot centre, errors between 0.1 and 0.5 m occurred in the TLS DEM for all tested grid resolutions at a plot radius of 25 m. These findings present a protocol for DEM production from TLS data at a range of grid resolutions and provide an overview of factors affecting DEMs produced from single and multiple TLS scan positions.

Highlights

  • Extraction of forest structure attributes, such as stem density, diameter at breast height (DBH), basal area (BA), tree height, biomass, plant area index (PAI) and canopy density from LiDAR, has become an important method to inventory and monitor forest resources, and for informing forest management and conservation policy development [1,2,3,4,5]

  • The comparison of error statistics for the minimum Z digital elevation model (DEM) produced from the single-centre scan (Scan Position 1) using all returns showed that errors were higher at the 50-m range for all pixel sizes, than at the 25-m range; and that the smaller the pixel size, the larger the error for both the 25-m and

  • At a 50-m plot radius, the mean error was greater than 1 m and the root mean squared error (RMSE) greater than 2 m for all tested pixel sizes

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Summary

Introduction

Extraction of forest structure attributes, such as stem density, diameter at breast height (DBH), basal area (BA), tree height, biomass, plant area index (PAI) and canopy density from LiDAR (lightRemote Sens. 2017, 9, 843; doi:10.3390/rs9080843 www.mdpi.com/journal/remotesensingRemote Sens. 2017, 9, 843 detection and ranging), has become an important method to inventory and monitor forest resources, and for informing forest management and conservation policy development [1,2,3,4,5]. Terrestrial LiDAR, known as terrestrial laser scanning (TLS), can be used to measure and estimate these forest structure attributes at single-tree and plot levels [3,6,7]. Measuring forest structure attributes, such as understorey height and cover, first requires normalisation of TLS point heights to height above ground. Studies estimating forest structure attributes from TLS often apply algorithms developed for DEM generation with ALS data, even though there are significant differences in point density and viewing geometry between these two types of LiDAR, which may mean that DEM development methods from ALS are not best suited to TLS datasets [20,21].

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