Light Detection And Ranging Applications Research Articles

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration.

In addition to precise 3D coordinates, most light detection and ranging (LIDAR) systems also record “intensity”, loosely defined as the strength of the backscattered echo for each measured point. To date, LIDAR intensity data have proven beneficial in a wide range of applications because they are related to surface parameters, such as reflectance. While numerous procedures have been introduced in the scientific literature, and even commercial software, to enhance the utility of intensity data through a variety of “normalization”, “correction”, or “calibration” techniques, the current situation is complicated by a lack of standardization, as well as confusing, inconsistent use of terminology. In this paper, we first provide an overview of basic principles of LIDAR intensity measurements and applications utilizing intensity information from terrestrial, airborne topographic, and airborne bathymetric LIDAR. Next, we review effective parameters on intensity measurements, basic theory, and current intensity processing methods. We define terminology adopted from the most commonly-used conventions based on a review of current literature. Finally, we identify topics in need of further research. Ultimately, the presented information helps lay the foundation for future standards and specifications for LIDAR radiometric calibration.

Improving models for environmental applications of LiDAR: Novel approaches based on soft computing

This work proposes novel methodologies to improve the use of Light Detection And Ranging (LiDAR) for environmental purposes, especially for thematic mapping (LiDAR only or fused with other remote sensors) and the estimation of forest variables. The m

Application of airborne LiDAR to investigate rates of recession in rocky coast environments

Coastal cliff erosion is a widespread problem that threatens property and infrastructure along many of the world’s coastlines. Rates of erosion used for shoreline management are generally based on analysis of historic maps and aerial photographs which, in rocky coast environments, does not wholly capture the detail in the processes and the failures occurring across the cliff face. This study uses airborne LiDAR (Light Detection and Ranging) data to gain a quantitative understanding of cliff erosion along rocky coastline where recession rates are relatively low (c. 0.1 m yr−1). It was found that three-dimensional volumetric changes on the cliff face and linear rates of retreat can be reliably calculated from consecutive digital elevation models (DEMs) several years apart. Furthermore, the accuracy of the data on sloping surfaces was tested by applying a threshold below which data that could be construed as error were removed. Using a vertical change threshold of 0.5 m had limited effect on the computed rates of retreat. The spatial variability in recession rates around the coastline was considered in terms of the relationship with the varying boundary conditions (rock mass characteristics, cliff geometries, beach morphology) and forcing parameters (wave climate and wave exposure). Recession rates were statistically correlated with significant wave height (H s ), rock mass characteristics (GSI) and the ratio between the two (GSI/H s ). The current method of assessing rocky cliff recession using maps and aerial photographs tends to not only miss the detail in the three-dimensional nature of the cliff evolution, but may also be too coarse a resolution to capture the small scale changes that contribute to the overall failure. LiDAR data, although limited in its temporal extent due to it being a relatively new technology, is a suitable method of evaluating cliff erosion on a time scale of 3–4 years and provides additional insight into the process occurring in slowly eroding environments.

Methodology for the assessment of PV capacity over a city region using low-resolution LiDAR data and application to the City of Leeds (UK)

An assessment of roof-mounted PV capacity over a local region can be accurately calculated by established roof segmentation algorithms using high-resolution light detection and ranging (LiDAR) datasets. However, over larger city regions often only low-resolution LiDAR data is available where such algorithms prove unreliable for small rooftops. A methodology optimised for low-resolution LiDAR datasets is presented, where small and large buildings are considered separately. The roof segmentation algorithm for small buildings, which are typically residential properties, assigns a roof profile to each building from a catalogue of common profiles after identifying LiDAR points within the building footprint. Large buildings, such as warehouses, offer a more diverse range of roof profiles but geometric features are generally large, so a direct approach is taken to segmentation where each LiDAR point within the building footprint contributes a separate roof segment. The methodology is demonstrated by application to the city region of Leeds, UK. Validation by comparison to aerial photography indicates that the assignment of an appropriate roof profile to a small building is correct in 81% of cases.

Habitat Classification of Temperate Marine Macroalgal Communities Using Bathymetric LiDAR

Here, we evaluated the potential of using bathymetric Light Detection and Ranging (LiDAR) to characterise shallow water (<30 m) benthic habitats of high energy subtidal coastal environments. Habitat classification, quantifying benthic substrata and macroalgal communities, was achieved in this study with the application of LiDAR and underwater video groundtruth data using automated classification techniques. Bathymetry and reflectance datasets were used to produce secondary terrain derivative surfaces (e.g., rugosity, aspect) that were assumed to influence benthic patterns observed. An automated decision tree classification approach using the Quick Unbiased Efficient Statistical Tree (QUEST) was applied to produce substrata, biological and canopy structure habitat maps of the study area. Error assessment indicated that habitat maps produced were primarily accurate (>70%), with varying results for the classification of individual habitat classes; for instance, producer accuracy for mixed brown algae and sediment substrata, was 74% and 93%, respectively. LiDAR was also successful for differentiating canopy structure of macroalgae communities (i.e., canopy structure classification), such as canopy forming kelp versus erect fine branching algae. In conclusion, habitat characterisation using bathymetric LiDAR provides a unique potential to collect baseline information about biological assemblages and, hence, potential reef connectivity over large areas beyond the range of direct observation. This research contributes a new perspective for assessing the structure of subtidal coastal ecosystems, providing a novel tool for the research and management of such highly dynamic marine environments.

Quantitative analysis of woodpecker habitat using high-resolution airborne LiDAR estimates of forest structure and composition

Light detection and ranging (LiDAR) technology has the potential to radically alter the way researchers and managers collect data on wildlife–habitat relationships. To date, the technology has fostered several novel approaches to characterizing avian habitat, but has been limited by the lack of detailed LiDAR-habitat attributes relevant to species across a continuum of spatial grain sizes and habitat requirements. We demonstrate a novel three-step approach for using LiDAR data to evaluate habitat based on multiple habitat attributes and accounting for their influence at multiple grain sizes using federally endangered red-cockaded woodpecker (RCW; Picoides borealis) foraging habitat data from the Savannah River Site (SRS) in South Carolina, USA. First, we used high density LiDAR data (10returns/m2) to predict detailed forest attributes at 20-m resolution across the entire SRS using a complementary application of nonlinear seemingly unrelated regression and multiple linear regression models. Next, we expanded on previous applications of LiDAR by constructing 95% joint prediction confidence intervals to quantify prediction error at various spatial aggregations and habitat thresholds to determine a biologically and statistically meaningful grain size. Finally, we used aggregations of 20-m cells and associated confidence interval boundaries to demonstrate a new approach to produce maps of RCW foraging habitat conditions based on the guidelines described in the species' recovery plan. Predictive power (R2) of regression models developed to populate raster layers ranged from 0.34 to 0.81, and prediction error decreased as aggregate size increased, but minimal reductions in prediction error were observed beyond 0.64-ha (4×4 20-m cells) aggregates. Mapping habitat quality while accounting for prediction error provided a robust method to determine the potential range of habitat conditions and specific attributes that were limiting in terms of the amount of suitable habitat. The sequential steps of our analytical approach provide a useful framework to extract detailed and reliable habitat attributes for a forest-dwelling habitat specialist, broadening the potential to apply LiDAR in conservation and management of wildlife populations.

Application of Short-Range LIDAR in Early Alerting for Low-Level Windshear and Turbulence at Hong Kong International Airport

Hong Kong Observatory currently uses a series of meteorological instruments, including long-range LIDAR (light detection and ranging) systems, to provide alerting services of low-level windshear and turbulence for Hong Kong International Airport. For some events that are smaller in spatial dimensions and are rapidly changing, such as low altitude windshear and turbulence associated with buildings or man-made structures, it would be necessary to involve meteorological instruments that offer greater spatial resolution. Therefore, the Observatory has set up a short-range LIDAR on the roof of the AsiaWorld-Expo during the summers over the past several years, conducting field research on the feasibility of strengthening early alerting for windshear and turbulence over the north runway’s eastern arrival runway (Runway 25RA) and developing an automated early alerting algorithm. This paper takes the pilot reports for Runway 25RA during the 2013 field research as verification samples, using different thresholds for radial wind velocity spatial and temporal changes detected by the short-range LIDAR to calculate the relative operating characteristic (ROC) curve, and analyzes its early alerting performance.

Perspectives and prospects of LiDAR in forest ecosystem monitoring and modeling

Light Detection And Ranging (LiDAR) is an active remote sensing technology for acquiring high resolution 3D terrain and vegetation structure parameters over multiple tempo-spatial scales, which can help to accurately monitor and model forest ecosystem dynamics. Consequently it plays an important role in understanding the impact of such dynamics on the carbon cycle and global climate change, and promoting biodiversity conservation. In this paper, we review the concepts and recent developments of LiDAR and explore the application of LiDAR in generating terrain models (e.g. digital elevation and digital surface models) and retrieving forest biophysical parameters (e.g. individual tree locations, leaf area index, volumes, biomass and carbon stocks). Finally, we present challenges for LiDAR in forestry applications, and suggest three major research issues for future study. We believe that there is an inevitable trend to constructing a digital ecosystem research network via combining spaceborne, airborne, and ground-based sensor measurements in an integrated platform, where LiDAR can provide an accurate and efficient solution for capturing 3D environmental variables. Thus, LiDAR will contribute to improving the relationship between man and nature in decision-making processes and eventually realizing a harmonious coexistence.

Ultraviolet Fluorescence LiDAR (UFL) as a Measurement Tool for Water Quality Parameters in Turbid Lake Conditions

Despite longstanding contributions to oceanography, similar use of fluorescence light detection and ranging (LiDAR) in lake settings is not routine. The potential for ship-mounted, multispectral Ultraviolet Fluorescence LiDAR (UFL) to provide rapid, high-resolution data in variably turbid and productive lake conditions are investigated here through a series of laboratory tank and field measurements carried out on Lake Balaton, Hungary. UFL data, calibrated empirically to a set of coinciding conventionally-analyzed samples, provide simultaneous estimates of three important parameters-chlorophyll a(chla), total suspended matter (TSM) and colored dissolved organic matter (CDOM). Successful UFL retrievals from both laboratory and field measurements were achieved for chla (0.01–378 mg∙m−3; R = 0.83–0.92), TSM (0.1–130 g∙m−3; R = 0.90–0.96) and CDOM (0.003–0.125 aCDOM(440); R = 0.80–0.97). Fluorescence emission at 685 nm is shown through tank measurements to display robust but distinct relationships with chla concentration for the two cultured algae species investigated (cyanobacteria, Cylindrospermopsis raciborskii, and chlorophyta, Scenedesmus armatus). The ratio between fluorescence emissions measured at 650 nm, related to the phycocyanin fluorescence maximum, to that at 685 nm is demonstrated to effectively distinguish these two species. Validation through both laboratory measurements and field measurements confirmed that site specific calibration is necessary. This study presents the first known assessment and application of ship-mounted fluorescence LiDAR in freshwater lake conditions and demonstrates the use of UFL in measuring important water quality parameters despite the more complicated hydro-optic conditions of inland waters.

Status and prospects for LiDAR remote sensing of forested ecosystems

The science associated with the use of airborne and satellite Light Detection and Ranging (LiDAR) to remotely sense forest structure has rapidly progressed over the past decade. LiDAR has evolved from being a poorly understood, potentially useful tool to an operational technology in a little over a decade, and these instruments have become a major success story in terms of their application to the measurement, mapping, or monitoring of forests worldwide. Invented in 1960, the laser and, a short time later, LiDAR, were found in research and military laboratories. Since the early 2000s, commercial technological developments coupled with an improved understanding of how to manipulate and analyze large amounts of collected data enabled notable scientific and application developments. A diversity of rapidly developing fields especially benefit from communications offered through conferences such as SilviLaser, and LiDAR has been no different. In 2002 the SilviLaser conference series was initiated to bring together those interested in the development and application of LiDAR for forested environments. Now, a little over a decade later, commercial use of LiDAR is common. In this paper – using the deliberations of SilviLaser 2012 as a source of information – we aim to capture aspects of importance to LiDAR users in the forest ecosystems community and to also point to key emerging issues as well as some remaining challenges.

Extraction of Mangrove Biophysical Parameters Using Airborne LiDAR

Tree parameter determinations using airborne Light Detection and Ranging (LiDAR) have been conducted in many forest types, including coniferous, boreal, and deciduous. However, there are only a few scientific articles discussing the application of LiDAR to mangrove biophysical parameter extraction at an individual tree level. The main objective of this study was to investigate the potential of using LiDAR data to estimate the biophysical parameters of mangrove trees at an individual tree scale. The Variable Window Filtering (VWF) and Inverse Watershed Segmentation (IWS) methods were investigated by comparing their performance in individual tree detection and in deriving tree position, crown diameter, and tree height using the LiDAR-derived Canopy Height Model (CHM). The results demonstrated that each method performed well in mangrove forests with a low percentage of crown overlap conditions. The VWF method yielded a slightly higher accuracy for mangrove parameter extractions from LiDAR data compared with the IWS method. This is because the VWF method uses an adaptive circular filtering window size based on an allometric relationship. As a result of the VWF method, the position measurements of individual tree indicated a mean distance error value of 1.10 m. The individual tree detection showed a kappa coefficient of agreement (K) value of 0.78. The estimation of crown diameter produced a coefficient of determination (R2) value of 0.75, a Root Mean Square Error of the Estimate (RMSE) value of 1.65 m, and a Relative Error (RE) value of 19.7%. Tree height determination from LiDAR yielded an R2 value of 0.80, an RMSE value of 1.42 m, and an RE value of 19.2%. However, there are some limitations in the mangrove parameters derived from LiDAR. The results indicated that an increase in the percentage of crown overlap (COL) results in an accuracy decrease of the mangrove parameters extracted from the LiDAR-derived CHM, particularly for crown measurements. In this study, the accuracy of LiDAR-derived biophysical parameters in mangrove forests using the VWF and IWS methods is lower than in coniferous, boreal, pine, and deciduous forests. An adaptive allometric equation that is specific for the level of tree density and percentage of crown overlap is a solution for improving the predictive accuracy of the VWF method.

Airborne LiDAR application to karstic areas: the example of Trieste province (north-eastern Italy) from prehistoric sites to Roman forts

The Trieste Karst, at the northernmost shore of the Adriatic Sea, is rich in prehistoric caves and protohistoric hill forts. Most of these archaeological sites were already identified in the second half of the 19th century when large parts of the area were almost without vegetation coverage for the effect of sheep breeding and exploitation of wood resources. Only a few open-air archaeological sites have been discovered in recent years due to the lack of systematic archaeological surveys and reforestation.Airborne LiDAR (light detection and ranging) data, originally acquired for environmental monitoring over the Friuli Venezia Giulia region (north-eastern Italy), have been recently analysed by means of free open source softwares for archaeological prospection of the Trieste Karst area. The LiDAR derived images have allowed identifying numerous unknown fortified structures ranging from prehistory to Roman time within a complex archaeological landscape that includes possible funerary barrows, agricultural terraces and other structures. The discovery of a probable Roman republican fort is particularly significant since similar structures, almost unknown in Italy, find comparison only with later examples of military forts from Roman provinces.The discovery of prehistoric, protohistoric and Roman fortified sites reported in this paper shows that airborne LiDAR remote sensing represents a revolution in landscape archaeology and archaeological mapping of karstic areas. This technique can provide unexpected results even in relatively urbanized territories investigated for a long time.

Rediscovering the Discovery Outcrop: The Promises and Pitfalls of LiDAR Technology in Mineral Exploration

ABSTRACT An increasing number of mineral discoveries rely on remote sensing methods such as airborne geophysics and hyperspectral imaging. The relatively new technology of Light Detection and Ranging (LiDAR), whereby surface outcrop patterns suggestive of economic mineralization can be identified, has the potential to join other remote sensing techniques employed by the exploration geologist. Successful application of LiDAR relies on rigorous, high-quality data collected under strict QA/QC standards and is most useful for delineating linear features such as faults or resistant rock types such as silicification. If used judiciously, LiDAR can join the toolbox of the modern exploration geologist working in heavily vegetated areas that contain many of the most prospective terrains left on Earth.

ANALYSIS AND APPLICATION OF LIDAR WAVEFORM DATA USING A PROGRESSIVE WAVEFORM DECOMPOSITION METHOD

Abstract. Due to rich information of a full waveform of airborne LiDAR (light detection and ranging) data, the analysis of full waveform has been an active area in LiDAR application. It is possible to digitally sample and store the entire reflected waveform of small-footprint instead of only discrete point clouds. Decomposition of waveform data, a key step in waveform data analysis, can be categorized to two typical methods: 1) the Gaussian modelling method such as the Non-linear least-squares (NLS) algorithm and the maximum likelihood estimation using the Exception Maximization (EM) algorithm. 2) pulse detection method—Average Square Difference Function (ASDF). However, the Gaussian modelling methods strongly rely on initial parameters, whereas the ASDF omits the importance of parameter information of the waveform. In this paper, we proposed a fast algorithm—Progressive Waveform Decomposition (PWD) method to extract local maxims and fit the echo with Gaussian function, and calculate other parameters from the raw waveform data. On the one hand, experiments are implemented to evaluate the PWD method and the results demonstrate its robustness and efficiency. On the other hand, with the PWD parametric analysis of the full-waveform instead of a 3D point cloud, some special applications are investigated afterward.

The Application of LiDAR to Assessment of Rooftop Solar Photovoltaic Deployment Potential in a Municipal District Unit

A methodology is provided for the application of Light Detection and Ranging (LiDAR) to automated solar photovoltaic (PV) deployment analysis on the regional scale. Challenges in urban information extraction and management for solar PV deployment assessment are determined and quantitative solutions are offered. This paper provides the following contributions: (i) a methodology that is consistent with recommendations from existing literature advocating the integration of cross-disciplinary competences in remote sensing (RS), GIS, computer vision and urban environmental studies; (ii) a robust methodology that can work with low-resolution, incomprehensive data and reconstruct vegetation and building separately, but concurrently; (iii) recommendations for future generation of software. A case study is presented as an example of the methodology. Experience from the case study such as the trade-off between time consumption and data quality are discussed to highlight a need for connectivity between demographic information, electrical engineering schemes and GIS and a typical factor of solar useful roofs extracted per method. Finally, conclusions are developed to provide a final methodology to extract the most useful information from the lowest resolution and least comprehensive data to provide solar electric assessments over large areas, which can be adapted anywhere in the world.

Measurement and Monitoring of Barrier Island Forest Sensitivity to Ecohydrological Change Using LIDAR Remote Sensing

O'Connell, M.J.; Shugart, H.H., and Okin, G.S., 2012. Measurement and monitoring of barrier island forest sensitivity to ecohydrological change using LIDAR remote sensing. Both sea-level rise and the attendant changes in wave overwash events can greatly modify or destroy coastal terrestrial ecosystems; thus, it is essential to have a priori identification of particularly sensitive forests (and other ecosystems). This article reports an application of airborne LIDAR (light detection and ranging) to study vulnerable coastal forests—the important case of natural mid-Atlantic maritime pine forests. We investigate the Experimental Advanced Airborne Research LIDAR (EAARL) for its accuracy in making detailed forest structural measurements, producing inferential statistics for remote monitoring, and detecting changes in barrier island forest ecohydrology. Results show that EAARL can be deployed with high confidence in loblolly pine (Pinus taeda L) ecosystems for baseline biophysical descriptions. EAARL measurements predict plot-level volume, height indices, and basal area with coefficients of determination ranging from 0.72 to 0.95. EAARL data also provide one-dimensional vertical metrics: maximum canopy height, height of peak canopy density, and bare earth elevation at standard errors of 1.8, 1.3, and 0.3 m, respectively. LIDAR-determined structural data are significantly correlated with environmental gradients on Assateague Island, thus providing an important reference state for continued ecological study. EAARL data model the plant area index (PAI) at an r2 of 0.73 over the study area. Soil-water availability is estimated using the depth to water table, and a soil texture index is correlated with PAI at rs = 0.54, p = 0.004. This basic ecohydrological relationship is monitored in a conceptual model predicting the structural changes expected with rising water over landscapes. The model predicts forest structural decline for most of the study area with incrementally higher sea surfaces. It is expected that the methods and theories developed here can be adapted to the LIDAR-based monitoring of similar habitats.

Use of LIDAR in landslide investigations: a review

This paper presents a short history of the appraisal of laser scanner technologies in geosciences used for imaging relief by high-resolution digital elevation models (HRDEMs) or 3D models. A general overview of light detection and ranging (LIDAR) techniques applied to landslides is given, followed by a review of different applications of LIDAR for landslide, rockfall and debris-flow. These applications are classified as: (1) Detection and characterization of mass movements; (2) Hazard assessment and susceptibility mapping; (3) Modelling; (4) Monitoring. This review emphasizes how LIDAR-derived HRDEMs can be used to investigate any type of landslides. It is clear that such HRDEMs are not yet a common tool for landslides investigations, but this technique has opened new domains of applications that still have to be developed.

Airborne LiDAR, archaeology, and the ancient Maya landscape at Caracol, Belize

Advances in remote sensing and space-based imaging have led to an increased understanding of past settlements and landscape use, but – until now – the images in tropical regions have not been detailed enough to provide datasets that permitted the computation of digital elevation models for heavily forested and hilly terrain. The application of airborne LiDAR (light detection and ranging) remote sensing provides a detailed raster image that mimics a 3-D view (technically, it is 2.5-D) of a 200 sq km area covering the settlement of Caracol, a long-term occupied (600 BC-A.D. 250–900) Maya archaeological site in Belize, literally “seeing” though gaps in the rainforest canopy. Penetrating the encompassing jungle, LiDAR-derived images accurately portray not only the topography of the landscape, but also, structures, causeways, and agricultural terraces – even those with relatively low relief of 5–30 cm. These data demonstrate the ability of the ancient Maya to modify, radically, their landscape in order to create a sustainable urban environment. Given the time and intensive effort involved in producing traditional large-scale maps, swath mapping LiDAR is a powerful cost-efficient tool to analyze past settlement and landscape modifications in tropical regions as it covers large study areas in a relatively short time. The use of LiDAR technology, as illustrated here, will ultimately replace traditional settlement mapping in tropical rainforest environments, such as the Maya region, although ground verification will continue to be necessary to test its efficacy.

Application of LIDAR to resolving bedrock structure in areas of poor exposure: An example from the STEEP study area, southern Alaska

An airborne, LIDAR (light detection and ranging) data set is used to map bedrock structure in an area with poor outcrop and indistinct stratigraphy. A 1-m, bare-earth filtered digital elevation model (DEM) is used to construct multiple shaded-relief images that show subtle topographic features suggestive of folded strata. Preliminary geologic interpretations exported to a field GIS (geographic information system) with GPS (global positioning system) identified the features as resistant beds beneath vegetation, and allowed construction of a regional map of bedding and fault traces. Bedding traces and faults were then analyzed with several 3D software applications. The ability to precisely map these features in 3D allowed projection onto cross-section lines, which helped define the structure as detachment folds cut by younger high-angle faults. Comparison between aerial photography and shaded-relief images of the bare-earth DEM indicates that structures beneath open forest and shrub are better revealed by shaded-relief images. The ability to create hillshade images using different sun angles supplements structural mapping with aerial photography, even in open tundra with no significant vegetation canopy. Aerial photography would generally be more informative in areas of extensive outcrop, and draping aerial photography on a LIDAR DEM would produce spectacular visualization capabilities. These techniques should yield good results where well-bedded rocks lie beneath thin soil cover and open canopy forest but may not be successful in deeply weathered tropical forest or glaciated areas where glacial sculpting and deposition obscure bedrock structure. We propose a mapping workflow that includes image processing of hillshade images, together with 3D visualization and mapping methods.

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 (Δ height r2 > 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.