Light Detection And Ranging Point Density Research Articles

Evaluating the sensitivity of forest structural diversity characterization to LiDAR point density

AbstractRecent expansion in data sharing has created unprecedented opportunities to explore structure–function linkages in ecosystems across spatial and temporal scales. However, characteristics of the same data product, such as resolution, can change over time or spatial locations, as protocols are adapted to new technology or conditions, which may impact the data's potential utility and accuracy for addressing end user scientific questions. The National Ecological Observatory Network (NEON) provides data products for users from 81 sites and over a planned 30‐year time frame, including discrete‐return light detection and ranging (LiDAR) from an airborne observation platform. LiDAR is a well‐established and increasingly available remote sensing technology for measuring three‐dimensional characteristics of ecosystem and landscape structure, including forest structural diversity. The LiDAR product that NEON provides can vary in point density from 2 to 25+ pt/m2 depending on the instrument and acquisition date. We used NEON LiDAR from five forested sites to (1) identify the minimum point density at which structural diversity metrics can be robustly estimated across forested sites from different ecoclimatic zones in the United States and (2) to test the effects of variable point density on the estimation of a suite of structural diversity metrics and multivariate structural complexity types within and across forested sites. Twelve of 16 structural diversity metrics were sensitive to LiDAR point density in at least one of the five NEON forested sites. The minimum point density to reliably estimate the metrics ranged from 2.0 to 7.5 pt/m2, but our results indicate that point densities above 7–8 pt/m2 should provide robust measurements of structural diversity in forests for temporal or spatial comparisons. The delineation of multivariate structural complexity types from a suite of 16 structural diversity metrics was robust within sites and across forest types for a LiDAR point density of 4 pt/m2 and above. This study shows that different metrics of structural diversity can vary in their sensitivity to the resolution of LiDAR data and that users of these open‐source data products should consider the point density of their data and use caution in metric selection when making spatial or temporal comparisons from these datasets.

Effects of UAV-LiDAR and Photogrammetric Point Density on Tea Plucking Area Identification

High-cost data collection and processing are challenges for UAV LiDAR (light detection and ranging) mounted on unmanned aerial vehicles in crop monitoring. Reducing the point density can lower data collection costs and increase efficiency but may lead to a loss in mapping accuracy. It is necessary to determine the appropriate point cloud density for tea plucking area identification to maximize the cost–benefits. This study evaluated the performance of different LiDAR and photogrammetric point density data when mapping the tea plucking area in the Huashan Tea Garden, Wuhan City, China. The object-based metrics derived from UAV point clouds were used to classify tea plantations with the extreme learning machine (ELM) and random forest (RF) algorithms. The results indicated that the performance of different LiDAR point density data, from 0.25 (1%) to 25.44 pts/m2 (100%), changed obviously (overall classification accuracies: 90.65–94.39% for RF and 89.78–93.44% for ELM). For photogrammetric data, the point density was found to have little effect on the classification accuracy, with 10% of the initial point density (2.46 pts/m2), a similar accuracy level was obtained (difference of approximately 1%). LiDAR point cloud density had a significant influence on the DTM accuracy, with the RMSE for DTMs ranging from 0.060 to 2.253 m, while the photogrammetric point cloud density had a limited effect on the DTM accuracy, with the RMSE ranging from 0.256 to 0.477 m due to the high proportion of ground points in the photogrammetric point clouds. Moreover, important features for identifying the tea plucking area were summarized for the first time using a recursive feature elimination method and a novel hierarchical clustering-correlation method. The resultant architecture diagram can indicate the specific role of each feature/group in identifying the tea plucking area and could be used in other studies to prepare candidate features. This study demonstrates that low UAV point density data, such as 2.55 pts/m2 (10%), as used in this study, might be suitable for conducting finer-scale tea plucking area mapping without compromising the accuracy.

A dense background representation method for traffic surveillance based on roadside LiDAR

The performance of traditional video-based traffic surveillance systems is susceptible to illumination variation and perspective distortion. This has been a significant motivation in recent years for research into Light Detection and Ranging (LiDAR)-based traffic surveillance systems, as LiDAR is insensitive to both factors. The first step in LiDAR data processing involves effective extraction of moving foreground objects from a referenced background. However, existing methods only detect a static background based on LiDAR point density or relative distance. In this research, we develop a novel dense background representation model (DBRM) for stationary roadside LiDAR sensors to detect both static and dynamic backgrounds, for freeway traffic surveillance purposes. Background objects tend to be stationary in space and time. DBRM utilizes this property to detect two types of background: both static and dynamic. While the static background is represented by fixed structures, the dynamic background – which may be characterized by quasi-static objects such as tree foliage – is modeled by mixtures of Gaussian probability distributions. Experiments were carried out in two different scenarios to compare the proposed model with two other state-of-the-art models. The results demonstrate the effectiveness, robustness, and detail-preserving advantages of the proposed model.

A Multi-Threshold Segmentation for Tree-Level Parameter Extraction in a Deciduous Forest Using Small-Footprint Airborne LiDAR Data

The development of new approaches to tree-level parameter extraction for forest resource inventory and management is an important area of ongoing research, which puts forward high requirements for the capabilities of single-tree segmentation and detection methods. Conventional methods implement segmenting routine with same resolution threshold for overstory and understory, ignoring that their lidar point densities are different, which leads to over-segmentation of the understory trees. To improve the segmentation accuracy of understory trees, this paper presents a multi-threshold segmentation approach for tree-level parameter extraction using small-footprint airborne LiDAR (Light Detection And Ranging) data. First, the point clouds are pre-processed and encoded to canopy layers according to the lidar return number, and multi-threshold segmentation using DSM-based (Digital Surface Model) method is implemented for each layer; tree segments are then combined across layers by merging criteria. Finally, individual trees are delineated, and tree parameters are extracted. The novelty of this method lies in its application of multi-resolution threshold segmentation strategy according to the variation of LiDAR point density in different canopy layers. We applied this approach to 271 permanent sample plots of the University of Kentucky’s Robinson Forest, a deciduous canopy-closed forest with complex terrain and vegetation conditions. Experimental results show that a combination of multi-resolution threshold segmentation based on stratification and cross-layer tree segments merging method can provide a significant performance improvement in individual tree-level forest measurement. Compared with DSM-based method, the proposed multi-threshold segmentation approach strongly improved the average detection rate (from 52.3% to 73.4%) and average overall accuracy (from 65.2% to 76.9%) for understory trees. The overall accuracy increased from 75.1% to 82.6% for all trees, with an increase of the coefficient of determination R2 by 20 percentage points. The improvement of tree detection method brings the estimation of structural parameters for single trees up to an accuracy level: For tree height, R2 increased by 5.0 percentage points from 90% to 95%; and for tree location, the mean difference decreased by 23 cm from 105 cm to 82 cm.

Effects of LiDAR Point Density on Extraction of Traffic Signs: A Sensitivity Study

Datasets collected using light detection and ranging (LiDAR) technology often consist of dense point clouds. However, the density of the point cloud could vary depending on several different factors including the capabilities of the data collection equipment, the conditions in which data are collected, and other features such as range and angle of incidence. Although variation in point density is expected to influence the quality of the information extracted from LiDAR, the extent to which changes in density could affect the extraction is unknown. Understanding such impacts is essential for agencies looking to adopt LiDAR technology and researchers looking to develop algorithms to extract information from LiDAR. This paper focuses specifically on understanding the impacts of point density on extracting traffic signs from LiDAR datasets. The densities of point clouds are first reduced using stratified random sampling; traffic signs are then extracted from those datasets at different levels of point density. The precision and accuracy of the detection process was assessed at the different levels of point cloud density and on four different highway segments. In general, it was found that for signs with large panels along the approach on which LiDAR data were collected, reducing the point cloud density by up to 70% of the original point cloud had minimal impacts on the sign detection rates. Results of this study provide practical guidance to transportation agencies interested in understanding the tradeoff in price, quality, and coverage, when acquiring LiDAR equipment for the inventory of traffic signs on their transportation networks.

Spectral mapping methods applied to LiDAR data: Application to fuel type mapping

Originally developed to classify multispectral and hyperspectral images, spectral mapping methods were used to classify Light Detection and Ranging (LiDAR) data to estimate the vertical structure of vegetation for Fuel Type (FT) mapping. Three spectral mapping methods generated spatially comprehensive FT maps for Cabañeros National Park (Spain): (1) Spectral Mixture Analysis (SMA), (2) Spectral Angle Mapper (SAM), and (3) Multiple Endmember Spectral Mixture Analysis (MESMA). The Vegetation Vertical Profiles (VVPs) describe the vertical distribution of the vegetation and are used to define each FT endmember in a LiDAR signature library. Two different approaches were used to define the endmembers, one based on the field data collected in 1998 and 1999 (Approach 1) and the other on exploring spatial patterns of the singular FT discriminating factors (Approach 2). The overall accuracy is higher for Approach 2 and with best results when considering a five-FT model rather than a seven-FT model. The agreement with field data of 44% for MESMA and SMA and 40% for SAM is higher than the 38% of the official Cabañeros National Park FTs map. The principal spatial patterns for the different FTs were well captured, demonstrating the value of this novel approach using spectral mapping methods applied to LiDAR data. The error sources included the time gap between field data and LiDAR acquisition, the steep topography in parts of the study site, and the low LiDAR point density among others.

A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas

Filtering of airborne light detection and ranging (LiDAR) data is a challenging task in vegetated mountain areas. Environmental features and LiDAR data characteristics have significant impacts on the performance of filtering algorithms. This study aims to determine the effects of topographic and environmental features such as slope, canopy cover, elevation variability, and LiDAR point density on five widely used filtering algorithms, including multi-scale curvature classification (MCC), interpolation-based filtering (IBF) algorithm, morphological filtering (MF) algorithm, progressive triangulated irregular network densification filtering (PTDF) algorithm, and slope-based filtering (SBF). The results show that the performances of these filtering algorithms are all significantly influenced by the chosen factors, but the dominant influential factor varies with algorithms. The MCC works well in steep and dense forests; IBF and MCC outperform the rest of filtering algorithms in areas with steep terrain but low vegetation coverage; and PTDF is more reliable for low-density LiDAR data. Our results can provide guidance for choosing the appropriate filtering algorithm based on the specific topographic and environmental features of a study area.

Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery

Canopy cover is an important forest structure parameter for many applications in ecology, hydrology, and forest management. Light detection and ranging (LiDAR) is a promising tool for estimating canopy cover because it can penetrate forest canopy. Various algorithms have been developed to calculate canopy cover from LiDAR data. However, little attention was paid to evaluating how different factors, such as estimation algorithm, LiDAR point density and scan angle, influence canopy cover estimates; and how LiDAR-derived canopy cover differs from estimates using traditional methods, such as field measurements, aerial and satellite imagery. In this study, we systematically compared canopy cover estimations from LiDAR data, quick field measurements, aerial imagery, and satellite imagery using different algorithms. The results show that LiDAR-derived canopy cover estimates are marginally influenced by the estimation algorithms. LiDAR data with a point density of 1 point/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> can generate comparable canopy cover estimates to data with a higher density. The uncertainty of canopy cover estimates from LiDAR data increased drastically as scan angles exceed 12°. Plot-level canopy cover estimates derived from quick field measurements do not have strong correlation with LiDAR-derived estimations. Both the aerial imagery-derived and satellite imagery-derived canopy cover estimates are comparable to LiDAR-derived canopy cover estimates at the forest stand scale, but tend to be overestimated in sparse forests and be underestimated in dense forests, particularly for the aerial imagery-derived estimates. The results from this study can provide practical guidance for the selection of data sources, sampling schemes, and estimation methods in regional canopy cover mapping.

Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters.

Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.

Estimating aboveground carbon using airborne LiDAR in Cambodian tropical seasonal forests for REDD+ implementation

We developed an empirical model to estimate aboveground carbon density with variables derived from airborne Light Detection and Ranging (LiDAR) in tropical seasonal forests in Cambodia, and assessed the effects of LiDAR pulse density on the accurate estimation of aboveground carbon density. First, we tested the applicability of variables used for estimating aboveground carbon density with the original LiDAR pulse density data (26 pulse m−2). Aboveground carbon density was regressed against variables derived from airborne LiDAR. Three individual height variable models were developed along with a canopy density model, and three other models combined canopy height and canopy density variables. The influence of forest type on model accuracy was also assessed. Next, the relationship between pulse density and estimation accuracy was investigated using the best regression model. The accuracy of the models were compared based on seven LiDAR point densities consisting of 0.25, 1, 2, 3, 4, 5 and 10 pulse m−2. The best model was obtained using the single mean canopy height (MCH) model (R2 = 0.92) with the original pulse density data. The relationship between MCH and aboveground carbon density was found to be consistent under different forest types. The differences between predicted and measured residual mean of squares of deviations were less than 1.5 Mg C ha−1 between each pulse density. We concluded that aboveground carbon density can be estimated using MCH derived from airborne LiDAR in tropical seasonal forests in Cambodia even with a low pulse density of 0.25 pulse m−2 without stratifying the study area based on forest type.

Effects of LiDAR point density and landscape context on estimates of urban forest biomass

Light Detection and Ranging (LiDAR) data is being increasingly used as an effective alternative to conventional optical remote sensing to accurately estimate aboveground forest biomass ranging from individual tree to stand levels. Recent advancements in LiDAR technology have resulted in higher point densities and improved data accuracies accompanied by challenges for procuring and processing voluminous LiDAR data for large-area assessments. Reducing point density lowers data acquisition costs and overcomes computational challenges for large-area forest assessments. However, how does lower point density impact the accuracy of biomass estimation in forests containing a great level of anthropogenic disturbance? We evaluate the effects of LiDAR point density on the biomass estimation of remnant forests in the rapidly urbanizing region of Charlotte, North Carolina, USA. We used multiple linear regression to establish a statistical relationship between field-measured biomass and predictor variables derived from LiDAR data with varying densities. We compared the estimation accuracies between a general Urban Forest type and three Forest Type models (evergreen, deciduous, and mixed) and quantified the degree to which landscape context influenced biomass estimation. The explained biomass variance of the Urban Forest model, using adjusted R2, was consistent across the reduced point densities, with the highest difference of 11.5% between the 100% and 1% point densities. The combined estimates of Forest Type biomass models outperformed the Urban Forest models at the representative point densities (100% and 40%). The Urban Forest biomass model with development density of 125m radius produced the highest adjusted R2 (0.83 and 0.82 at 100% and 40% LiDAR point densities, respectively) and the lowest RMSE values, highlighting a distance impact of development on biomass estimation. Our evaluation suggests that reducing LiDAR point density is a viable solution to regional-scale forest assessment without compromising the accuracy of biomass estimates, and these estimates can be further improved using development density.

Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs

Automatic extraction of building roofs from remote sensing data is important for many applications, including 3D city modeling. This paper proposes a new method for automatic segmentation of raw LIDAR (light detection and ranging) data. Using the ground height from a DEM (digital elevation model), the raw LIDAR points are separated into two groups. The first group contains the ground points that form a “building mask”. The second group contains non-ground points that are clustered using the building mask. A cluster of points usually represents an individual building or tree. During segmentation, the planar roof segments are extracted from each cluster of points and refined using rules, such as the coplanarity of points and their locality. Planes on trees are removed using information, such as area and point height difference. Experimental results on nine areas of six different data sets show that the proposed method can successfully remove vegetation and, so, offers a high success rate for building detection (about 90% correctness and completeness) and roof plane extraction (about 80% correctness and completeness), when LIDAR point density is as low as four points/m2. Thus, the proposed method can be exploited in various applications.

Detection of large above-ground biomass variability in lowland forest ecosystems by airborne LiDAR

Abstract. Quantification of tropical forest above-ground biomass (AGB) over large areas as input for Reduced Emissions from Deforestation and forest Degradation (REDD+) projects and climate change models is challenging. This is the first study which attempts to estimate AGB and its variability across large areas of tropical lowland forests in Central Kalimantan (Indonesia) through correlating airborne light detection and ranging (LiDAR) to forest inventory data. Two LiDAR height metrics were analysed, and regression models could be improved through the use of LiDAR point densities as input (R2 = 0.88; n = 52). Surveying with a LiDAR point density per square metre of about 4 resulted in the best cost / benefit ratio. We estimated AGB for 600 km of LiDAR tracks and showed that there exists a considerable variability of up to 140% within the same forest type due to varying environmental conditions. Impact from logging operations and the associated AGB losses dating back more than 10 yr could be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat classification for a 1 million ha study area where AGB values were based on site-specific field inventory data, regional literature estimates, and default values by the Intergovernmental Panel on Climate Change (IPCC) showed an overestimation of 43%, 102%, and 137%, respectively. The results show that AGB overestimation may lead to wrong greenhouse gas (GHG) emission estimates due to deforestation in climate models. For REDD+ projects this leads to inaccurate carbon stock estimates and consequently to significantly wrong REDD+ based compensation payments.

Classification of tree species based on structural features derived from high density LiDAR data

Automated tree species classification using high density airborne light detection and ranging (LiDAR) data will support more precise forest inventory but further research is required to improve the associated methods. Most existing methods rely on geometric and vertical distribution features, which often do not accurately represent the internal foliage and branch patterns of an individual tree. Our study objective was to develop novel algorithms to characterize internal structures of an individual tree crown and to test their effectiveness for use in classifying tree species. We derived several LiDAR features to describe the three-dimensional texture, foliage clustering degree relative to tree envelop, foliage clustering scale, and gap distribution of an individual tree in both horizontal and vertical directions. Features were selected using a genetic algorithm and then tree species were classified using linear discriminant analysis based on the selected features. The four species, sugar maple (Acer saccharum Marsh.), trembling aspen (Populus tremuloides Michx.), jack pine (Pinus banksiana Lamb.) and eastern white pine (Pinus strobus L.), were classified with an overall accuracy of 77.5% and a Kappa coefficient of 0.7. The results demonstrate the significance of the derived structural features as aids to classify tree species. Our investigation also showed a positive linear correlation (R2=0.88) between LiDAR point density and species classification accuracy.

A framework for automated assessment of post-earthquake building damage using geospatial data

Based on the triangulated irregular network (TIN) model, barycentric coordinates and random points, a new method was developed for more accurate characterization of 3D shape signatures of buildings using Light Detection and Ranging (LiDAR) data and Geographic Information System (GIS). The new method was applied to four simulated building models with flat, pent, gable and hip roofs to test the detection of changes in 3D building shapes in a post-earthquake scenario: (1) a three-storey building model becomes two-storey after losing the first floor; (2) a severe damage of a two-storey building model; and (3) a total collapse of a two-storey building model. The new method was then applied to real LiDAR data from four buildings in Harris County, TX, USA. All the changes in 3D shapes of the models and real buildings were successfully detected using 3D shape signatures. Sensitivity analyses were carried out to test the influence of LiDAR point density and new points in TIN triangles on 3D shape signatures. The results suggest that LiDAR point density of 0.5 point m−2 or higher can generate stable 3D shape signatures of buildings, and that the density of the new points in TIN triangles does not affect 3D shape signatures significantly. Built upon the new method and results, a framework was proposed for an automated assessment of post-earthquake building damage using geospatial data. A flowchart was presented to provide more details of the framework, and the advantages and limitations of the framework were also discussed. It is expected that the framework can be effectively applied to post-earthquake building damage assessment and other disaster scenarios that involve major changes in 3D building shapes.

LiDAR mapping of canopy gaps in continuous cover forests: A comparison of canopy height model and point cloud based techniques

In continuous cover forest systems, canopy gaps are created by management activities with an aim of encouraging natural regeneration and of increasing structural heterogeneity. Light Detection and Ranging (LiDAR) may provide a more accurate means to assess gap distribution than ground survey, allowing more effective monitoring. This paper presents a new approach to gap delineation, based on identifying gaps directly from the point cloud and avoiding the need for interpolation of returns to a canopy height model (CHM). Areas of canopy are identified through local maxima identification, filtering and clustering of the point cloud, with gaps subsequently delineated in a GIS environment. When compared to field surveyed gap outlines, the algorithm has an overall accuracy of 88% for data with a high LiDAR point density (11.4 returns per m2) and accuracy of up to 77% for lower density data (1.2 returns per m2). The method provides an increase in overall and Producer's accuracy of 4 and 8% respectively, over a method based on the use of a CHM. The estimation of total gap area is improved by, on average, 16% over the CHM based approach. Results indicate that LiDAR data can be used accurately to delineate gaps in managed forests, potentially allowing more accurate and spatially explicit modelling of understorey light conditions.

Modelling vertical error in LiDAR-derived digital elevation models

A hybrid theoretical–empirical model has been developed for modelling the error in LiDAR-derived digital elevation models (DEMs) of non-open terrain. The theoretical component seeks to model the propagation of the sample data error (SDE), i.e. the error from light detection and ranging (LiDAR) data capture of ground sampled points in open terrain, towards interpolated points. The interpolation methods used for infilling gaps may produce a non-negligible error that is referred to as gridding error. In this case, interpolation is performed using an inverse distance weighting (IDW) method with the local support of the five closest neighbours, although it would be possible to utilize other interpolation methods. The empirical component refers to what is known as “information loss”. This is the error purely due to modelling the continuous terrain surface from only a discrete number of points plus the error arising from the interpolation process. The SDE must be previously calculated from a suitable number of check points located in open terrain and assumes that the LiDAR point density was sufficiently high to neglect the gridding error. For model calibration, data for 29 study sites, 200×200 m in size, belonging to different areas around Almeria province, south-east Spain, were acquired by means of stereo photogrammetric methods. The developed methodology was validated against two different LiDAR datasets. The first dataset used was an Ordnance Survey (OS) LiDAR survey carried out over a region of Bristol in the UK. The second dataset was an area located at Gador mountain range, south of Almería province, Spain. Both terrain slope and sampling density were incorporated in the empirical component through the calibration phase, resulting in a very good agreement between predicted and observed data (R2=0.9856;p<0.001). In validation, Bristol observed vertical errors, corresponding to different LiDAR point densities, offered a reasonably good fit to the predicted errors. Even better results were achieved in the more rugged morphology of the Gador mountain range dataset. The findings presented in this article could be used as a guide for the selection of appropriate operational parameters (essentially point density in order to optimize survey cost), in projects related to LiDAR survey in non-open terrain, for instance those projects dealing with forestry applications.

A Comparison of Statistical Methods for Estimating Forest Biomass from Light Detection and Ranging Data

Abstract Strong regression relationships between light detection and ranging (LIDAR) metrics and indices of forest structure have been reported in the literature. However, most papers focus on empirical results and do not consider LIDAR metric selection and biological interpretation explicitly. In this study, three different variable selection methods (stepwise regression, principle component analysis [PCA], and Bayesian modeling averaging [BMA]) were compared using LIDAR data from three study sites: Capitol Forest in western Washington State, Mission Creek in central Washington State, and Kenai Peninsula in south central Alaska. Separate aboveground biomass regression models were developed for each site as well as common models using three study sites simultaneously. Final biomass models have R2 values ranging from 0.67 to 0.88 for three study sites. PCA indicates that three LIDAR metrics (mean height, coefficient variation of height, and canopy LIDAR point density) explain the majority of variation contained within a larger set of metrics. Within each study area, forest biomass models using these three predictor variables had similar R2 values as the stepwise and BMA regression models. Individual site models using these three variables are recommended because these models are straightforward in terms of model form and biological interpretation and are easily adopted for application.