Airborne LiDAR Datasets Research Articles

Investigation on the Weighted RANSAC Approaches for Building Roof Plane Segmentation from LiDAR Point Clouds

RANdom SAmple Consensus (RANSAC) is a widely adopted method for LiDAR point cloud segmentation because of its robustness to noise and outliers. However, RANSAC has a tendency to generate false segments consisting of points from several nearly coplanar surfaces. To address this problem, we formulate the weighted RANSAC approach for the purpose of point cloud segmentation. In our proposed solution, the hard threshold voting function which considers both the point-plane distance and the normal vector consistency is transformed into a soft threshold voting function based on two weight functions. To improve weighted RANSAC’s ability to distinguish planes, we designed the weight functions according to the difference in the error distribution between the proper and improper plane hypotheses, based on which an outlier suppression ratio was also defined. Using the ratio, a thorough comparison was conducted between these different weight functions to determine the best performing function. The selected weight function was then compared to the existing weighted RANSAC methods, the original RANSAC, and a representative region growing (RG) method. Experiments with two airborne LiDAR datasets of varying densities show that the various weighted methods can improve the segmentation quality differently, but the dedicated designed weight functions can significantly improve the segmentation accuracy and the topology correctness. Moreover, its robustness is much better when compared to the RG method.

Urban energetics applications and Geomatic technologies in a Smart City perspective*

The management of an urban context in a Smart City perspective requires the development of innovative projects, with new applications in multidisciplinary research areas. They can be related to many aspects of city life and urban management: fuel consumption monitoring, energy efficiency issues, environment, social organization, traffic, urban transformations, etc. Geomatics, the modern discipline of gathering, storing, processing, and delivering digital spatially referenced information, can play a fundamental role in many of these areas, providing new efficient and productive methods for a precise mapping of different phenomena by traditional cartographic representation or by new methods of data visualization and manipulation (e.g. three-dimensional modelling, data fusion, etc.). The technologies involved are based on airborne or satellite remote sensing (in visible, near infrared, thermal bands), laser scanning, digital photogrammetry, satellite positioning and, first of all, appropriate sensor integration (online or offline). The aim of this work is to present and analyse some new opportunities offered by Geomatics technologies for a Smart City management, with a specific interest towards the energy sector related to buildings. Reducing consumption and CO2 emissions is a primary objective to be pursued for a sustainable development and, in this direction, an accurate knowledge of energy consumptions and waste for heating of single houses, blocks or districts is needed. A synoptic information regarding a city or a portion of a city can be acquired through sensors on board of airplanes or satellite platforms, operating in the thermal band. A problem to be investigated at the scale A problem to be investigated at the scale of the whole urban context is the Urban Heat Island (UHI), a phenomenon known and studied in the last decades. UHI is related not only to sensible heat released by anthropic activities, but also to land use variations and evapotranspiration reduction. The availability of thermal satellite sensors is fundamental to carry out multi-temporal studies in order to evaluate the dynamic behaviour of the UHI for a city. Working with a greater detail, districts or single buildings can be analysed by specifically designed airborne surveys. The activity has been recently carried out in the EnergyCity project, developed in the framework of the Central Europe programme established by UE. As demonstrated by the project, such data can be successfully integrated in a GIS storing all relevant data about buildings and energy supply, in order to create a powerful geospatial database for a Decision Support System assisting to reduce energy losses and CO2 emissions. Today, aerial thermal mapping could be furthermore integrated by terrestrial 3D surveys realized with Mobile Mapping Systems through multisensor platforms comprising thermal camera/s, laser scanning, GPS, inertial systems, etc. In this way the product can be a true 3D thermal model with good geometric properties, enlarging the possibilities in respect to conventional qualitative 2D images with simple colour palettes. Finally, some applications in the energy sector could benefit from the availability of a true 3D City Model, where the buildings are carefully described through three-dimensional elements. The processing of airborne LiDAR datasets for automated and semi-automated extraction of 3D buildings can provide such new generation of 3D city models.

Combining airborne hyperspectral and LiDAR data across local sites for upscaling shrubland structural information: Lessons for HyspIRI

Fine-scale variation of vegetation structure in dryland systems, such as the Great Basin in the western US, is critical to understanding ecosystem responses to changing land-use conditions. High resolution airborne hyperspectral (HyMap) and LiDAR datasets acquired across independent collection sites can reduce uncertainty in predictive ecosystem modeling and provide a basis for regional upscaling to satellite observations of structural metrics such as cover and height. In the first part of our study, we combined ground reference and airborne data collected at three sagebrush-steppe locations and used the statistical data mining tool random forests to identify remote sensing variables most relevant to estimating shrub cover. In the second part of our study, we hypothesized that vegetation indices derived from hyperspectral satellite observations would not only reliably predict shrub cover but also be relatable to shrub height; thereby augmenting the collection of vertical structure estimates from future satellite platforms such as ICESAT-2. To test this hypothesis, we simulated HyspIRI observations to derive variables to relate to LiDAR-based estimates of shrub cover and height. We generated the same hyperspectral variables as in the first part of this study but at coarser resolution (60m) and we again used random forests to model shrub cover and height and identify predictors of greatest importance. Overall, combining LiDAR and HyMap datasets at the airborne scale improved shrub cover model results (r2=0.58) compared to LiDAR alone (r2=0.49). Primary shrub cover variables of importance were HIQR (the interquartile range of height of all LiDAR vegetation returns), HMAD (median absolute deviation from median height of all LiDAR vegetation returns), a narrowband index sensitive to anthocyanins, the ratio of LiDAR vegetation returns to total returns, and a red to green ratio. In addition, HyspIRI-simulated narrowband vegetation indices were relatable to LiDAR-derived shrub cover and height variables (r2 ranging from 0.63 to 0.71) with relatively low root mean square error.

Point Density Evaluation of Airborne LiDAR Datasets

Point Density Evaluation of Airborne LiDAR Datasets

Morphotectonic analysis of the long-term surface expression of the 2009 L'Aquila earthquake fault (Central Italy) using airborne LiDAR data

In this paper we present a morphotectonic study of the Paganica–San Demetrio fault system (PSDFS) responsible for the Mw6.1 April 6, 2009 earthquake (L'Aquila, Central Italy).The discrepancy observed between the length of the seismologic–geodetic modeled fault, the limited size of the primary coseismic surface ruptures and the significant morphological expression of the PSDFS stimulated a debate about the maximum rupture length of the PSDFS and its capability to generate larger magnitude events.To image the PSDFS long-term morphological expression and define its surface geometrical arrangement (length, number of fault splays and boundaries), we took advantage of a high-resolution airborne LiDAR dataset. LiDAR topography substantially increased our confidence in detecting even subtle tectonic-controlled morphologies. We define the PSDFS as a ~19km-long fault system that displays a complex structural setting characterized by two different sectors: 1) the Paganica sector to the NW, with a narrow deformation zone, and 2) the San Demetrio sector to SE, where the strain is accommodated by several fault-splays dissecting a wider Quaternary basin. We also defined a first-order hierarchy among the numerous fault splays across the PSDFS.The long-term geomorphic expression of the PSDFS suggests that it ruptured also involving the whole 19km-long structure besides rupturing only small sections, as it occurred in 2009. This suggests a variable slip behavior.Empirical relations applied to this hypothesis allow up to M 6.6 earthquakes along the PSDFS. These results have a critical impact on the seismic hazard assessment of the area when compared with a M 6.1 event as the 2009.

Aboveground biomass estimation with airborne hyperspectral and LiDAR data in Tesinske Beskydy Mountains

The study presents three models for estimation of forest aboveground biomass (AGB) for plot level using different categories of airborne data. The first and the second models estimate AGB from metrics of airborne LiDAR data. The third model estimates AGB from integration of metrics of airborne hyperspectral and LiDAR data. The results are compared with plot level biomass estimated from field measurements. The results show that the best AGB estimate is obtained from the model utilizing a fusion of hyperspectral and LiDAR metrics. Study results expand existing research on the applicability of airborne hyperspectral and LiDAR datasets for AGB assessment. It evidences the efficiency of using a predicting model based on hyperspectral and LiDAR data for study area.

Combining multi-scale geophysical techniques for robust hydro-structural characterisation in catchments underlain by hard rock in post-glacial regions

• Aquifer characterisation should be incorporated in integrated catchment studies. • Interpretation of monitoring data requires accurate sub-surface conceptual models. • Spatial heterogeneity in post-glacial hard rock environments makes this a challenge. • An integrated geophysical methodology at the scale of interest is effective. • Cross-comparison of techniques strengthens accuracy and 3D resolution. Accurate conceptual models of groundwater systems are essential for correct interpretation of monitoring data in catchment studies. In surface-water dominated hard rock regions, modern ground and surface water monitoring programmes often have very high resolution chemical, meteorological and hydrological observations but lack an equivalent emphasis on the subsurface environment, the properties of which exert a strong control on flow pathways and interactions with surface waters. The reasons for this disparity are the complexity of the system and the difficulty in accurately characterising the subsurface, except locally at outcrops or in boreholes. This is particularly the case in maritime north-western Europe, where a legacy of glacial activity, combined with large areas underlain by heterogeneous igneous and metamorphic bedrock, make the structure and weathering of bedrock difficult to map or model. Traditional approaches which seek to extrapolate information from borehole to field-scale are of limited application in these environments due to the high degree of spatial heterogeneity. Here we apply an integrative and multi-scale approach, optimising and combining standard geophysical techniques to generate a three-dimensional geological conceptual model of the subsurface in a catchment in NE Ireland. Available airborne LiDAR, electromagnetic and magnetic data sets were analysed for the region. At field-scale surface geophysical methods, including electrical resistivity tomography, seismic refraction, ground penetrating radar and magnetic surveys, were used and combined with field mapping of outcrops and borehole testing. The study demonstrates how combined interpretation of multiple methods at a range of scales produces robust three-dimensional conceptual models and a stronger basis for interpreting groundwater and surface water monitoring data.

Multivariate statistical analysis of asynchronous lidar data and vegetation models in a neotropical forest

In this paper, we conduct multivariate analyses of similarity amongst lidar instruments and canopy models by exploiting the similarities of binned canopy height profiles to community datasets in that we treat profile bins as “species” to calculate distance matrices between sample units. Canopy profiles were derived from lidar data using the MacArthur–Horn transformation and from field data using a model we developed that uses two sets of allometric equations describing crown shape and tree height and a third from raw field data. We conducted a statistical comparison of seven asynchronous relative vegetation profiles (RVP) derived from different methodologies between the years of 2005 and 2012. We compared three airborne lidar datasets, three modeled profiles from field data, and one terrestrial lidar dataset using pairwise Mantel tests, multi-response permutation procedure (MRPP) and a permutation-based comparison using a similarity index for within plot comparisons. We used the results of MRPP to determine possible drivers of poor agreement between instruments and models and found a moderate relationship between within-plot variability (observed delta) and estimated above ground biomass (r2=0.273, p<0.05), which we attribute to poor model performance on low density/low biomass plots. In addition, correlation analysis of height metrics derived from RVPs resulted in weak correlations at low height percentiles and strong correlations at higher percentiles. Overall, we identified general similarity between lidar profiles using MRPP (lidar only A=0.202, p<0.001), but poorer agreement between lidar and modeled profiles (all profiles A=0.076, p<0.001). We attribute some of these differences to selection of canopy profile models and to approaches used for accounting for canopy occlusion in lidar transformations. In addition, we discuss the possibility of using Mantel tests to estimate temporal scales of vertical structure change in La Selva Biological Station, Costa Rica.

A decision-tree classification for low-lying complex land cover types within the zone of discontinuous permafrost

This study presents a decision-tree (DT) approach to classifying heterogeneous land cover types within a northern watershed located in the zone of discontinuous permafrost using airborne LiDAR and high resolution spectral datasets. Results are compared with a more typically applied supervised classification. Increasing errors in discharge resulting from an inaccurate classification are quantified using a distributed hydrological model.The hierarchical classification was accurate between 88% and 97% of the validation sub-area, whereas the parallelepiped classification was accurate between 38% and 74% of the same area (despite overall accuracy of ~91%, kappa=0.91). Topographical derivatives were best able to explain variations in land cover types (82% to 96%), whilst spectral and vegetation structural derivatives were less accurate. When compared with field measurements, the hierarchical classification of plateau edges (adjacent to a fen) was within 2m of measured, 60% of the time, whilst this occurred only 40% of the time when using a spectral classification. When examining the impacts of land cover classification accuracy on modelled discharge, we find that the length of the Hydrological Response Unit defined by the classification (and subject to varying levels of errors) was linearly related to discharge (m3) such that an increase in permafrost plateau area would increase discharge by 26% of the total. The methodology presented in this paper clarifies previous classification and modelling studies using Landsat and IKONOS data for the same basin. This study greatly improves upon past classifications in the same area, furthers our understanding of the distribution of connected bogs and fens (as conveyors of water to the basin outlet) within the watershed, and current spatial extents of rapidly thawing permafrost plateaus, which are critical for better understanding the impacts of climate change on these northern environments.

The effect of boreal forest canopy to reflectance of snow covered terrain based on airborne imaging spectrometer observations

Optical remote sensing methods for mapping of the seasonal snow cover are often obstructed by the masking effect of forest canopy. Therefore, optical algorithms tend to underestimate the amount of snow cover in forested regions. In this paper, we investigate the influence of boreal forest stand characteristics on the observed scene reflectance under full dry snow cover conditions by applying an advantageous experimental setup combining airborne hyperspectral imaging and LIDAR data sets from a test region in Sodankylä, northern Finland. This is particularly useful to the understanding of the composition of the mixed satellite scene reflectance behavior and it is relation to the natural ground targets’ spectral signatures.At first, we demonstrate the effects of varying forest stand characteristics, including Canopy Cover (CC), Tree Height (TH) and the product of the these parameters referred to as CCxTH, on the reflectance measured by airborne imaging spectrometer AisaDUAL. Then, we analyze the effects of the presence of snow on forest canopy on the observed AisaDUAL data. The analysis of the effects of canopy was enabled by the high resolution LIDAR measurements which provide reference information on forest canopy characteristics. According to the results the change in Canopy Cover, as well as in CCxTH, is related to the observed change in reflectance, as well as to changes in such spectral indices as Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI). Additionally, NDSI was found to vary extensively particularly in dense forests (CC>85%), where the relative variation was over 100%. This should be considered when applying NDSI-based snow mapping methods in the case of forested areas. One notable finding was that the relation between the forest characteristics and reflectance was nearly exponential, while with reflectance indices it was linear. Besides, the results show that NDSI was a more effective parameter in detecting snow on canopy (values deviated 0.3 on average) than NDVI (values deviated 0.3 on average) in all Canopy Cover classes. The difference in NDSI between these two cases, snow-covered and snow-free canopy, increased when the canopy coverage increased.

Shrub characterization using terrestrial laser scanning and implications for airborne LiDAR assessment

Sagebrush-steppe ecosystems across the United States Intermountain West are experiencing major structural and functional changes. Scientists and managers need effective technologies to understand such dynamic changes across broad spatial scales. We tested the capacity of terrestrial laser scanning (TLS) to automatically determine structural information of individual shrubs (principally Artemisia tridentata) and shrub canopies in eastern Washington, USA. Because current airborne LiDAR systems have technological constraints that may limit their utility in shrub-dominated ecosystems, we used the TLS data both in their basic form and to simulate high resolution discrete-return airborne LiDAR data with sample densities of 4 and 16 points m−2. Through spatial wavelet analysis we automatically detected the locations of up to 78% of all individual shrubs identified in the field and up to 88% of shrubs with a crown diameter > 1.5 m. Shrub height and canopy cover derived from TLS data were significantly correlated with field measurements (respectively, r2 = 0.94 and 0.51, p < 0.001, α = 0.05). Automated detection of individual shrub crown area using the high resolution simulated airborne LiDAR dataset was also significantly correlated with field measurements (r2 = 0.47, p ≤ 0.01). Our results indicate that TLS data are useful for automatic quantification of shrub structure and provide a glimpse to the utility of the next generation of small-footprint airborne LiDAR instruments in quantifying shrub biophysical parameters across broad areas.

SENSITIVITY ANALYSIS OF UAV-PHOTOGRAMMETRY FOR CREATING DIGITAL ELEVATION MODELS (DEM)

Abstract. This study evaluates the potential that lies in the photogrammetric processing of aerial images captured by unmanned aerial vehicles. UAV-Systems have gained increasing attraction during the last years. Miniaturization of electronic components often results in a reduction of quality. Especially the accuracy of the GPS/IMU navigation unit and the camera are of the utmost importance for photogrammetric evaluation of aerial images. To determine the accuracy of digital elevation models (DEMs), an experimental setup was chosen similar to the situation of data acquisition during a field campaign. A quarry was chosen to perform the experiment, because of the presence of different geomorphologic units, such as vertical walls, piles of debris, vegetation and even areas. In the experimental test field, 1042 ground control points (GCPs) were placed, used as input data for the photogrammetric processing and as high accuracy reference data for evaluating the DEMs. Further, an airborne LiDAR dataset covering the whole quarry and additional 2000 reference points, measured by total station, were used as ground truth data. The aerial images were taken using a MAVinci Sirius I – UAV equipped with a Canon 300D as imaging system. The influence of the number of GCPs on the accuracy of the indirect sensor orientation and the absolute deviation’s dependency on different parameters of the modelled DEMs was subject of the investigation. Nevertheless, the only significant factor concerning the DEMs accuracy that could be isolated was the flying height of the UAV.

A fine-scale architectural model of trees to enhance LiDAR-derived measurements of forest canopy structure

Forest canopy structure consists of the complex spatial arrangement of the foliage, branches, and boles of trees. Measuring forest canopy structure is an active research question because of its influence on a wide range of biophysical and ecological processes. Light detection and ranging technology (LiDAR), both with airborne and terrestrial instruments, has been used to assess canopy structure and estimate vegetation attributes. Although LiDAR has proven to be an effective tool for mapping the forest canopy, its ability to probe all canopy elements is limited by the signal occlusion that occurs when multiple objects lie between the sensor and target area. This limitation can be alleviated by comparing realistic fine-scale 3D canopy architectural models with terrestrial and airborne LiDAR point-cloud data sets. We propose a method that mitigates the limitations of object occlusion and allows detailed description of 3D canopy structure from small-footprint airborne LiDAR (ALiDAR). Our approach involved reconstructing stem-mapped forest stands with catalogs of terrestrial LiDAR (TLiDAR) scans of individual trees, using an architectural model called L-Architect (LiDAR data to vegetation Architecture). L-Architect has been developed to link TLiDAR scans to tree-level structural attributes that reproduce the branching structure and foliage spatial distribution of a tree according to information gathered from detailed mensurational data. This study has further developed the potential of L-Architect to reconstruct individual trees to model the structure of a stand. The method was evaluated by comparing vertical profiles of material density for a forest stand calculated from TLiDAR scans and L-Architect's canopy model, resulting in a Pearson's correlation coefficient of 0.95. Most individual tree structures were reproduced with high accuracy, with a relative deviation of <10% on tree diameter at breast height (DBH) and total leaf area. The use of the ALiDAR data together with L-Architect allowed quantifying how significant object occlusion affects untreated data. We obtained mean correlations between ALiDAR data sets and L-Architect derived profiles of 0.84 and 0.43 for the vertical and horizontal material distribution, respectively. We demonstrate how L-Architect can be used to make accurate 3D maps of stand components from LiDAR data.

Estimating aboveground biomass in interior Alaska with Landsat data and field measurements

Terrestrial plant biomass is a key biophysical parameter required for understanding ecological systems in Alaska. An accurate estimation of biomass at a regional scale provides an important data input for ecological modeling in this region. In this study, we created an aboveground biomass (AGB) map at 30-m resolution for the Yukon Flats ecoregion of interior Alaska using Landsat data and field measurements. Tree, shrub, and herbaceous AGB data in both live and dead forms were collected in summers and autumns of 2009 and 2010. Using the Landsat-derived spectral variables and the field AGB data, we generated a regression model and applied this model to map AGB for the ecoregion. A 3-fold cross-validation indicated that the AGB estimates had a mean absolute error of 21.8Mg/ha and a mean bias error of 5.2Mg/ha. Additionally, we validated the mapping results using an airborne lidar dataset acquired for a portion of the ecoregion. We found a significant relationship between the lidar-derived canopy height and the Landsat-derived AGB (R2=0.40). The AGB map showed that 90% of the ecoregion had AGB values ranging from 10Mg/ha to 134Mg/ha. Vegetation types and fires were the primary factors controlling the spatial AGB patterns in this ecoregion.

Exploitation of Countrywide Airborne Lidar Dataset for Documentation of Historical Human Activities in Countryside

During three years (2010 – 12) The Czech Office for Surveying, Mapping and Cadastre in cooperation with The Ministry of Defense of the Czech Republic and The Ministry of Agriculture of the Czech Republic are providing mapping of the entire area of the Czech Republic by Airborne laser scanning (ALS) technology. The goal of this project is to derive a highly accurate Digital Terrain Model (DTM) for purposes of administration like detection of flooded areas, orthorectification of areal images etc. Such data set also seems to be an interesting da ta source for mapping of human activities in countryside. Human settlements, agriculture or mining activities left significant scars on natural landscape. These significant man-made structures are a part of so called cultural landscape. Man-made structures include ancient settlements, remains of medieval mining activities or remains of settlements abandoned during 20th century. This article generally presents how to derive information about the man-made structures from raw LiDAR. Examples of significant findings of man-made imprints in countryside are also presented. Goal of this article is not to describe a certain archeological site but to inform about strengths of ALS data to map human activities in countryside, mainly in forested areas.

Integration of GLAS and Landsat TM data for aboveground biomass estimation

Current regional aboveground biomass estimation techniques, such as those that require extensive fieldwork or airborne light detection and ranging (lidar) data for validation, are time and cost intensive. The use of freely available satellite-based data for carbon stock estimation mitigates both the cost and the spatial limitations of field-based techniques. Spaceborne lidar data have been demonstrated as useful for aboveground biomass (AGBM) estimation over a wide range of biomass values and forest types. However, the application of these data is limited because of their spatially discrete nature. Spaceborne multispectral sensors have been used extensively to estimate AGBM, but these methods have been demonstrated as inappropriate for forest structure characterization in high-biomass mature forests. This study uses an integration of ICESat Geospatial Laser Altimeter System (GLAS) lidar and Landsat data to develop methods to estimate AGBM in an area of south-central British Columbia, Canada. We compare estimates with a reliable AGBM map of the area derived from high-resolution airborne lidar data to assess the accuracy of satellite-based AGBM estimates. Further, we use the airborne lidar dataset in combination with forest inventory data to explore the relationship between model error and canopy height, AGBM, stand age, canopy rugosity, mean diameter at breast height (DBH), canopy cover, terrain slope, and dominant species type. GLAS AGBM models were shown to reliably estimate AGBM (R2 = 0.77) over a range of biomass conditions. A partial least squares AGBM model using Landsat input data to estimate AGBM (derived from GLAS) had an R2 of 0.60 and was found to underestimate AGBM by an average of 26 Mg/ha per pixel when applied to areas outside of the GLAS transect. This study demonstrates that Landsat and GLAS data integration are most useful for forests with less than 120 Mg/ha of AGBM, less than 60 years of age, and less than 60% canopy cover. These techniques have high associated error when applied to areas with greater than 200 Mg/ha of AGBM.

Effects of lidar post‐spacing and DEM resolution to mean slope estimation

This paper examines the effect of scale (exhibited by spatial sampling) in modeling mean slope from lidar data using two representations of scale: lidar posting density (i.e. post‐spacing) and DEM resolution (i.e. cell size). The study areas selected include six small (i.e. approximately 3 km2) urban drainage basins in Richland County, SC, USA, which share similar hydrologic characteristics. This research spatially sampled an airborne lidar dataset collected in 2000 at a 2 m nominal posting density to simulate lidar posting density at various post‐spacings, from 2 m through 10 m. DEMs were created from the lidar observations at a corresponding cell size using spatial interpolation. Finally, using these DEMs, a sensitivity analysis between modeled terrain slope and lidar post‐spacing was conducted. Results of the sensitivity analyses showed that the deviation between mean slope and modeled mean slope decreases with finer posting density and DEM resolution. The relationship of mean slope with varying cell sizes and post‐spacing suggests a linear and a logarithmic function, respectively, for all study areas. More importantly, cell size has a greater effect on mean slope than lidar posting density. Implications of these results for lumped hydrologic modeling are then postulated.

The uncertainty in conifer plantation growth prediction from multi-temporal lidar datasets

An evaluation of the use of airborne lidar for multi-temporal forest height growth assessment in a temperate mature red pine ( Pinus resinosa Ait.) plantation over a five-year period is presented. The objective was to evaluate the level of uncertainty in lidar-based growth estimates through time so that the optimal repeat interval necessary for statistically meaningful growth measurements could be evaluated. Four airborne lidar datasets displaying similar survey configuration parameters were collected between 2000 and 2005. Coincident with the 2002 and 2005 acquisitions, field mensuration for 126 trees within 19 plots was carried out. Field measurements of stem height were compared to both coincident plot-level laser pulse return (LPR) height percentile metrics and stand level raster canopy height models (CHM). The average plot-level field heights were found to be 23.8 m (standard deviation ( σ) = 0.4 m) for 2002 and 25.0 m ( σ = 0.6 m) for 2005, with an approximate annual growth rate of 0.4 m/yr ( σ = 0.5 m). The standard deviation uncertainty for field height growth estimates over the three year period was 41% at the plot-level ( n = 19) and 92% at the individual tree level ( n = 126). Of the lidar height percentile metrics tested, the 90th (L90), 95th (L95) and maximum (Lmax) LPR distribution heights demonstrated the highest overall correlations with field-measured tree height. While all lidar-based methods, including raster CHM comparison, tended to underestimate the field estimate of growth, Lmax provided the most robust overall direct estimate (0.32 m/yr, σ = 0.37 m). A single factor analysis of variance demonstrated that there was no statistically significant difference between all plot-level field and Lmax mean growth rate estimates ( P = 0.38) and, further, that there was no difference in Lmax growth rate estimates across the examined time intervals ( P = 0.59). A power function relationship between time interval and the standard deviation of error in growth estimate demonstrated that over a one-year period, the growth uncertainty was in the range of 0.3 m (∼ 100% of total growth) reducing to less than 0.1 m (∼ 6% of total growth) after 5 years. Assuming a 10% uncertainty is acceptable for operational or research-based conifer plantation growth estimates, this can be achieved at a three-year time interval.