Automated reconstruction of tree and canopy structure for modeling the internal canopy radiation regime

Understanding canopy radiation regimes is critical to successfully modeling vegetation growth and function. For instance, the vertical distribution of photosynthetically active radiation (PAR) affects vegetation growth, informative upon carbon and energy cycling. Availing upon advances in information capture and computing power, geometrically explicit modeling of forest structure becomes increasingly possible. A primary challenge however is acquiring the forest mensuration data required to parameterize these models and the related automation of modeling forest structure. In this research, to address these issues we employ a novel and automated approach that capitalizes upon the rich information afforded by ground-based laser scanning technology. The method is implemented in two steps: in the first step, geometric explicit models of canopy structure are created from the ground-based laser scanning data. These geometric explicit models are used to simulate the vertical range to first hit. In the second step, we derive canopy gap probability from full waveform laser scanning data which have been used in a number of studies for characterization of radiation transmission (Jupp et al., 2009; Yang et al., 2010) and do not require any geometric explicit modeling. The radiative consistency of the geometric explicit models from step 1 is validated against the gap probabilities of step 2. The results show a strong relationship between the radiative transmission properties of the geometric models and canopy gap probabilities at plot level (R = 0.91 to 0.97), while the geometric models suggest the additional benefit to serve as a bridge in scaling between shoot level and canopy level radiation.Keywords: Laser scanning, Explicit geometric, Ray tracing, Canopy structure, Modeling, Photosynthetically active radiatio

A Comparison of Foliage Profiles in the Sierra National Forest Obtained with a Full-Waveform Under-Canopy EVI Lidar System with the Foliage Profiles Obtained with an Airborne Full-Waveform LVIS Lidar System

Foliage profiles retrieved froma scanning, terrestrial, near-infrared (1064 nm), full-waveformlidar, the Echidna Validation Instrument (EVI), agree well with those obtained from an airborne, near-infrared, full-waveform, large footprint lidar, the Lidar Vegetation Imaging Sensor (LVIS). We conducted trials at 5 plots within a conifer stand at Sierra National Forest in August, 2008. Foliage profiles retrieved from these two lidar systems are closely correlated (e.g., r = 0.987 at 100 mhorizontal distances) at large spatial coverage while they differ significantly at small spatial coverage, indicating the apparent scanning perspective effect on foliage profile retrievals. Alsowe noted the obvious effects of local topography on foliage profile retrievals, particularly on the topmost height retrievals. With a fine spatial resolution and a small beam size, terrestrial lidar systems complement the strengths of the airborne lidars by making a detailed characterization of the crowns from a small field site, and thereby serving as a validation tool and providing localized tuning information for future airborne and spaceborne lidar missions

Automated reconstruction of tree and canopy structure for modeling the internal canopy radiation regime

Understanding canopy radiation regimes is critical to successfully modeling vegetation growth and function. For instance, the vertical distribution of photosynthetically active radiation (PAR) affects vegetation growth, informative upon carbon and energy cycling. Availing upon advances in information capture and computing power, geometrically explicit modeling of forest structure becomes increasingly possible. A primary challenge however is acquiring the forest mensuration data required to parameterize these models and the related automation of modeling forest structure. In this research, to address these issues we employ a novel and automated approach that capitalizes upon the rich information afforded by ground-based laser scanning technology. The method is implemented in two steps: in the first step, geometric explicit models of canopy structure are created from the ground-based laser scanning data. These geometric explicit models are used to simulate the vertical range to first hit. In the second step, we derive canopy gap probability from full waveform laser scanning data which have been used in a number of studies for characterization of radiation transmission (Jupp et al., 2009; Yang et al., 2010) and do not require any geometric explicit modeling. The radiative consistency of the geometric explicit models from step 1 is validated against the gap probabilities of step 2. The results show a strong relationship between the radiative transmission properties of the geometric models and canopy gap probabilities at plot level (R = 0.91 to 0.97), while the geometric models suggest the additional benefit to serve as a bridge in scaling between shoot level and canopy level radiation. ?? 2013 Elsevier Inc

A simple technique for co-registration of terrestrial LiDAR observations for forestry applications

Light detection and ranging (LiDAR) from terrestrial platforms provides unprecedented detail about the three-dimensional structure of forest canopies. Although airborne laser scanning is designed to yield a relatively homogeneous distribution of returns, the radial perspective of terrestrial laser scanning (TLS) results in a rapid decrease of number of returns with increasing distance from the instrument. Additionally, when used in forested environments, significant parts of the area under investigation may be obscured by tree trunks and understorey. A possible approach to mitigate this effect is to combine TLS observations acquired at different locations to obtain multiple perspectives of an area under investigation. The denser and more evenly distributed observations then allow a spatially explicit and more comprehensive study of forest characteristics. This study demonstrates a simple approach to combine TLS observations made at multiple locations using bright reference targets as tie-points. Results s..

Comparison of terrestrial and airborne LiDAR in describing stand structure of a thinned lodgepole pine forest

Airborne LiDAR (ALS) has been widely used for measuring canopy structure, but much of the woody components of the canopy are not directly visible with this system. Terrestrial LiDAR (TLS) data may help fill this gap by helping to understand the relationship between above- and below-canopy architecture. In this study, we report on the potential for combining TLS and ALS, thereby focusing on forest inventory and wood quality?related characteristics (such as number and dimension of branches). Our results show that both TLS and ALS were able to describe stand height using the top 10% of LiDAR returns at a high level of precision; however, TLS measurements were negatively biased by approximately 1 m (R 2 = 0.96 and 0.86 for ALS and TLS, respectively; P < 0.05). The distribution of foliage measured by ALS and TLS was strongly related to basal area (R 2 = 0.63 and 0.91 for ALS and TLS, respectively) and stand density (R 2 = 0.89 and 0.72 for ALS and TLS, respectively). Tree-level attributes were more accurately described by TLS (R 2 = 0.63) compared with ALS (R 2 = 0.37) for crown depth and a similar result applied to dbh with R 2 = 0.63 for TLS versus R 2 = 0.43 for ALS

Assessment of standing wood and fiber quality using ground and airborne laser scanning: A review

Accurate information on the wood-quality characteristics of standing timber and logs is needed to optimize the forest production value chain and to assess the potential of forest resources to meet other services. Physical and chemical characteristics of wood vary with both tree and site characteristics. At the tree scale, crown development, stem shape and taper, branch size and branch location, knot size, type and placement, and age all influence wood properties. More broadly, at the stand level, stocking density, moisture, nutrient availability, climate, competition, disturbance, and stand age have also been identified as key determinants of wood quality. Such information is often captured in polygon based forest inventory data. Other terrain-related spatial information, such as elevation, slope and aspect, can improve assessments of site conditions and limitations upon plant growth which impact wood quality. Light Detection And Ranging (LiDAR) is an emerging technology, which directly measures the three-dimensional structure of forest canopies using ground or airborne laser instruments, and can provide highly accurate information on individual-tree and stand-level forest structure. In this paper, we explore the potential of LiDAR and other geospatial information sources to model and predict wood quality based on individual-tree and stand structural metrics. We identify a number of key wood quality attributes (i.e., basic wood density, cell perimeter, cell coarseness, fiber length, and microfibril angle) and demonstrate links between these properties and forest structure and site attributes. Finally, the potential for using LiDAR in combination with other geospatial information sources to predict wood quality in standing timber is discussed. ?? 2011 Elsevier B.V

Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand

Accurate estimates of vegetation structure are important for a large number of applications including ecological modeling and carbon budgets. Light detection and ranging (LiDAR) measures the three-dimensional structure of vegetation using laser beams. Most LiDAR applications today rely on airborne platforms for data acquisitions, which typically record between 1 and 5 “discrete” returns for each outgoing laser pulse. Although airborne LiDAR allows sampling of canopy characteristics at stand and landscape level scales, this method is largely insensitive to below canopy biomass, such as understorey and trunk volumes, as these elements are often occluded by the upper parts of the crown, especially in denser canopies. As a supplement to airborne laser scanning (ALS), a number of recent studies used terrestrial laser scanning (TLS) for the biomass estimation in spatially confined areas. One such instrument is the Echidna® Validation Instrument (EVI), which is configured to fully digitize the returned energy of an emitted laser pulse to establish a complete profile of the observed vegetation elements. In this study we assess and compare a number of canopy metrics derived from airborne and TLS. Three different experiments were conducted using discrete return ALS data and discrete and full waveform observations derived from the EVI. Although considerable differences were found in the return distribution of both systems, ALS and TLS were both able to accurately determine canopy height (? height < 2.5 m) and the vertical distribution of foliage and leaf area (0.86 > r 2 > 0.90, p < 0.01). When using more spatially explicit approaches for modeling the biomass and volume throughout the stands, the differences between ALS and TLS observations were more distinct; however, predictable patterns exist based on sensor position and configuration

Prediction of wood fiber attributes from LiDAR-derived forest canopy indicators

We investigated the potential use of airborne light detection and ranging (LiDAR) data to predict key wood fiber properties from extrinsic indicators in lodgepole pine leading forest stands located in the foothills of central Alberta, Canada. Six wood fiber attributes (wood density, cell perimeter, cell coarseness, mature fiber length, microfibril angle, and modulus of elasticity) were measured at 21 plots, and with use of data reduction techniques, two components of wood properties were derived: wood strength, stiffness, and fiber yield and fiber strength and smoothness. These wood fiber components were then compared with extrinsic indicators of wood characteristic-derived LiDAR-estimated topographic morphology, tree height, and canopy light metrics. The first principal component indicating wood strength and stiffness was significantly correlated to the depth of different canopy zones (or light regimes; r 2 = 0.55, P < 0.05). The second component, related to fiber strength and smoothness, was significantly correlated to the height of the canopy and canopy thickness (r 2 = 0.65, P < 0.05). The results indicate that airborne LiDAR attributes can explain about half of the observed variance in intrinsic wood fiber attributes, which is approximately 5?10% less than that explained by growth-related field-measured variables such as diameter increment and height. This reduction in explained variance can be balanced by the opportunities for much broader spatial characterizations of wood quantity and quality at the stand and landscape levels

Prediction of Wood Fiber Attributes from LiDAR-Derived Forest Canopy Indicators

We investigated the potential use of airborne light detection and ranging (LiDAR) data to predict key wood fiber properties from extrinsic indicators in lodgepole pine leading forest stands located in the foothills of central Alberta, Canada. Six wood fiber attributes (wood density, cell perimeter, cell coarseness, mature fiber length, microfibril angle, and modulus of elasticity) were measured at 21 plots, and with use of data reduction techniques, two components of wood properties were derived: wood strength, stiffness, and fiber yield and fiber strength and smoothness. These wood fiber components were then compared with extrinsic indicators of wood characteristic-derived LiDAR-estimated topographic morphology, tree height, and canopy light metrics. The first principal component indicating wood strength and stiffness was significantly correlated to the depth of different canopy zones (or light regimes; r² = 0.55, P < 0.05). The second component, related to fiber strength and smoothness, was significantly correlated to the height of the canopy and canopy thickness (r² = 0.65, P < 0.05). The results indicate that airborne LiDAR attributes can explain about half of the observed variance in intrinsic wood fiber attributes, which is approximately 5-10% less than that explained by growth-related field-measured variables such as diameter increment and height. This reduction in explained variance can be balanced by the opportunities for much broader spatial characterizations of wood quantity and quality at the stand and landscape levels. FOR. SCI. 59(2):231-242.Keywords: LiDAR, Wood fiber, Light regime, Lodgepole pine, Canopy structur

Nondestructive estimates of above-ground biomass using terrestrial laser scanning

Allometric equations are currently used to estimate above-ground biomass (AGB) based on the indirect relationship with tree parameters. Terrestrial laser scanning (TLS) can measure the canopy structure in 3D with high detail. In this study, we develop an approach to estimate AGB from TLS data, which does not need any prior information about allometry. We compare these estimates against destructively harvested AGB estimates and AGB derived from allometric equations. We also evaluate tree parameters, diameter at breast height (DBH) and tree height, estimated from traditional field inventory and TLS data. Tree height, DBH and AGB data are collected through traditional forest inventory, TLS and destructive sampling of 65 trees in a native Eucalypt Open Forest in Victoria, Australia. Single trees are extracted from the TLS data and quantitative structure models are used to estimate the tree volume directly from the point cloud data. AGB is inferred from these volumes and basic density information and is then compared with the estimates derived from allometric equations and destructive sampling. AGB estimates derived from TLS show a high agreement with the reference values from destructive sampling, with a concordance correlation coefficient (CCC) of 0·98. The agreement between AGB estimates from allometric equations and the reference is lower (CCC = 0·68–0·78). Our TLS approach shows a total AGB overestimation of 9·68% compared to an underestimation of 36·57–29·85% for the allometric equations. The error for AGB estimates using allometric equations increases exponentially with increasing DBH, whereas the error for AGB estimates from TLS is not dependent on DBH. The TLS method does not rely on indirect relationships with tree parameters or calibration data and shows better agreement with the reference data compared to estimates from allometric equations. Using 3D data also enables us to look at the height distributions of AGB, and we demonstrate that 80% of the AGB at plot level is located in the lower 60% of the trees for a Eucalypt Open Forest. This method can be applied in many forest types and can assist in the calibration and validation of broad-scale biomass maps