Calibration of full-waveform airborne laser scanning data for 3D object segmentation

Phd ThesisAirborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic object recognition and extraction processes. 3D object segmentation is considered as one of the major research topics in the field of laser scanning for feature recognition and object extraction applications. The demand for automatic segmentation has significantly increased with the emergence of full-waveform (FWF) ALS, which potentially offers an unlimited number of return echoes. FWF has shown potential to improve available segmentation and classification techniques through exploiting the additional physical observables which are provided alongside the standard geometric information. However, use of the FWF additional information is not recommended without prior radiometric calibration, taking into consideration all the parameters affecting the backscattered energy. The main focus of this research is to calibrate the additional information from FWF to develop the potential of point clouds for segmentation algorithms. Echo amplitude normalisation as a function of local incidence angle was identified as a particularly critical aspect, and a novel echo amplitude normalisation approach, termed the Robust Surface Normal (RSN) method, has been developed. Following the radar equation, a comprehensive radiometric calibration routine is introduced to account for all variables affecting the backscattered laser signal. Thereafter, a segmentation algorithm is developed, which utilises the raw 3D point clouds to estimate the normal for individual echoes based on the RSN method. The segmentation criterion is selected as the normal vector augmented by the calibrated backscatter signals. The developed segmentation routine aims to fully integrate FWF data to improve feature recognition and 3D object segmentation applications. The routine was tested over various feature types from two datasets with different properties to assess its potential. The results are compared to those delivered through utilizing only geometric information, without the additional FWF radiometric information, to assess performance over existing methods. The results approved the potential of the FWF additional observables to improve segmentation algorithms. The new approach was validated against manual segmentation results, revealing a successful automatic implementation and achieving an accuracy of 82%

Complexity and Dynamics of Semi-Arid Vegetation Structure, Function and Diversity Across Spatial Scales from Full Waveform Lidar

Semi-arid ecosystems cover approximately 40% of the earth’s terrestrial landscape and show high dynamicity in ecosystem structure and function. These ecosystems play a critical role in global carbon dynamics, productivity, and habitat quality. Semi-arid ecosystems experience a high degree of disturbance that can severely alter ecosystem services and processes. Understanding the structure-function relationships across spatial extents are critical in order to assess their demography, response to disturbance, and for conservation management. In this research, using state-of-the-art full waveform lidar (airborne and spaceborne) and field observations, I developed a framework to assess the complexity and dynamics of vegetation structure, function and diversity across spatial scales in a semi-arid ecosystem. Difficulty in differentiating low stature vegetation from bare ground is the key remote sensing challenge in semi-arid ecosystems. In this study, I developed a workflow to differentiate key plant functional types (PFTs) using both structural and biophysical variables derived from the full waveform lidar and an ensemble random forest technique. The results revealed that waveform lidar pulse width can clearly distinguish shrubs from bare ground. The models showed PFT classification accuracy of 0.81–0.86% and 0.60–0.70% at 10 m and 1 m spatial resolutions, respectively. I found that structural variables were more important than the biophysical variables to differentiate the PFTs in this study area. The study further revealed an overlap between the structural features of different PFTs (e.g. shrubs from trees). Using structural features, I derived three main functional traits (canopy height, plant area index and foliage height diversity) of shrubs and trees that describe canopy architecture and light use efficiency of the ecosystem. I evaluated the trends and patterns of functional diversity and their relationship with non-climatic abiotic factors and fire disturbance. In addition to the fine resolution airborne lidar, I used simulated large footprint spaceborne lidar representing the newly launched Global Ecosystem Dynamics Investigation system (GEDI, a lidar sensor on the International Space Station) to evaluate the potential of capturing functional diversity trends of semi-arid ecosystems at global scales. The consistency of diversity trends between the airborne lidar and GEDI confirmed GEDI’s potential to capture functional diversity. I found that the functional diversity in this ecosystem is mainly governed by the local elevation gradient, soil type, and slope. All three functional diversity indices (functional richness, functional evenness and functional divergence) showed a diversity breakpoint near elevations of 1500 m – 1700 m. Functional diversity of fire-disturbed areas revealed that the fires in our study area resulted in a more even and less divergent ecosystem state. Finally, I quantified aboveground biomass using the structural features derived from both the airborne lidar and GEDI data. Regional estimates of biomass can indicate whether an ecosystem is a net carbon sink or source as well as the ecosystem’s health (e.g. biodiversity). Further, the potential of large footprint lidar data to estimate biomass in semi-arid ecosystems are not yet fully explored due to the inherent overlapping vegetation responses in the ground signals that can be affected by the ground slope. With a correction to the slope effect, I found that large footprint lidar can explain 42% of variance of biomass with a RMSE of 351 kg/ha (16% RMSE). The model estimated 82% of the study area with less than 50% uncertainty in biomass estimates. The cultivated areas and the areas with high functional richness showed the highest uncertainties. Overall, this dissertation establishes a novel framework to assess the complexity and dynamics of vegetation structure and function of a semi-arid ecosystem from space. This work enhances our understanding of the present state of an ecosystem and provides a foundation for using full waveform lidar to understand the impact of these changes to ecosystem productivity, biodiversity and habitat quality in the coming decades. The methods and algorithms in this dissertation can be directly applied to similar ecosystems with relevant corrections for the appropriate sensor. In addition, this study provides insights to related NASA missions such as ICESat-2 and future NASA missions such as NISAR for deriving vegetation structure and dynamics related to disturbance

Processing and analysis of airborne fullwaveform laser scanning data for the characterization of forest structure and fuel properties

Tesis por compendio[ES] Esta tesis aborda el desarrollo de métodos de procesado y análisis de datos ALSFW para la caracterización de la estructura vertical del bosque y, en particular, del sotobosque. Para responder a este objetivo general, se establecieron seis objetivos específicos: En primer lugar, se analiza la influencia de la densidad de pulso, de los parámetros de voxelización (tamaño de vóxel y valor de asignación) y de los métodos de regresión sobre los valores de las métricas ALSFW y sobre la estimación de atributos de estructura del bosque. Para ello, se redujo aleatoriamente la densidad de pulsos y se modificaron los parámetros de voxelización, obteniendo los valores de las métricas ALSFW para las diferentes combinaciones de parámetros. Estas mismas métricas ALSFW se emplearon para la estimación de atributos de la estructura del bosque mediante diferentes métodos de regresión. En segundo lugar, se integran métodos de procesado y análisis de datos ALSFW en una nueva herramienta llamada WoLFeX (Waveform Lidar for Forestry eXtraction) que incluye los procesos de recorte, corrección radiométrica relativa, voxelización y extracción de métricas a partir de los datos ALSFW, así como nuevas métricas descriptoras del sotobosque. En tercer lugar, se evalúa la influencia del ángulo de escaneo utilizado en la adquisición de datos ALS y la corrección radiométrica en la extracción de métricas ALSFW y en la estimación de atributos de combustibilidad forestal. Para ello, se extrajeron métricas ALSFW con y sin corrección radiométrica relativa y empleando diferentes ángulos de escaneo. En cuarto lugar, se caracteriza la oclusión de la señal a lo largo de la estructura vertical del bosque empleando y comparando tres tipos diferentes de láser escáner (ALSFW, ALSD y láser escáner terrestre: TLS, por sus siglas en inglés), determinando así sus limitaciones en la detección de material vegetativo en dos ecosistemas forestales diferenciados: el boreal y el mediterráneo. Para cuantificar la oclusión de la señal a lo largo de la estructura vertical del bosque se propone un nuevo parámetro, la tasa de reducción del pulso, basada en el porcentaje de haces láser bloqueados antes de alcanzar una posición dada. En quinto lugar, se evalúa la forma en que se detectan y determinan las clases de densidad de sotobosque mediante los diferentes tipos de ALS. Se compararon los perfiles de distribución vertical en los estratos inferiores descritos por el ALSFW y el ALSD con respecto a los descritos por el TLS, utilizando este último como referencia. Asimismo, se determinaron las clases de densidad de sotobosque aplicando la curva Lorenz y el índice Gini a partir de los perfiles de distribución vertical descritos por ALSFW y ALSD. Finalmente, se aplican y evalúan las nuevas métricas ALSFW basadas en la voxelización, utilizando como referencia los atributos extraídos a partir del TLS, para estimar la altura, la cobertura y el volumen del sotobosque en un ecosistema mediterráneo.[EN] This thesis addresses the development of ALSFW processing and analysis methods to characterize the vertical forest structure, in particular, the understory vegetation. To answer this overarching goal, a total of six specific objectives were established: Firstly, the influence of pulse density, voxel parameters (i.e., voxel size and assignation value) and regression methods on ALSFW metric values and on estimates of forest structure attributes are analyzed. To do this, pulse density was randomly reduced and voxel parameters modified, obtaining ALSFW metric values for the different parameter combinations. These ALSFW metrics were used to estimate forest structure attributes with different regression methods. Secondly, a set of ALSFW data processing and analysis methods are integrated in a new software named WoLFeX (Waveform Lidar for Forestry eXtraction), including clipping, relative radiometric correction, voxelization and ALSFW metric extraction, and proposing new metrics for understory vegetation. Thirdly, the influence of the scan angle of ALS data acquisition and radiometric correction on the extraction of ALSFW metrics and on modeling forest fuel attributes is assessed. To do this, ALSFW metrics were extracted applying and without applying relative radiometric correction and using different scan angles. Fourthly, signal occlusion is characterized along the vertical forest structure using and comparing three different laser scanning configurations (ALSFW, ALSD and terrestrial laser scanning: TLS), determining their limitations in the detection of vegetative material in two contrasted forest ecosystems: boreal and Mediterranean. To quantify signal occlusion along the vertical forest structure, a new parameter based on the percentage of laser beams blocked prior to reach a given location, the rate of pulse reduction, is proposed. Fifthly, the assessment of how understory vegetation density classes are detected and determined by different ALS configurations is done. Vertical distribution profiles at the lower strata described by ALSFW and ALSD are compared with those described by TLS as reference. Moreover, understory vegetation density classes are determined by applying the Lorenz curve and Gini index from the vertical distribution profiles described by ALSFW and ALSD. Finally, the new proposed voxel-based ALSFW metrics are applied and evaluated, using TLS-based attributes as a reference, to estimate understory height, cover and volume in a Mediterranean ecosystem.[CA] Aquesta tesi aborda el desenvolupament de mètodes de processament i anàlisi de dades ALSFW per a la caracterització de l'estructura vertical del bosc i, en particular, del sotabosc. Per a respondre a aquest objectiu general, s'establiren sis objectius específics: En primer lloc, s'analitza la influència de la densitat de pols, dels paràmetres de voxelització (grandària de vóxel i valor d'assignació) i dels mètodes de regressió sobre els valors de les mètriques ALSFW i sobre l'estimació dels atributs d'estructura del bosc. Per a això, es reduí aleatòriament la densitat de polsos i es modificaren els paràmetres de voxelització, obtenint els valors de les mètriques ALSFW per a les diferents combinacions de paràmetres. Aquestes mètriques ALSFW s'empraren per a l'estimació d'atributs de l'estructura del bosc mitjançant diferents mètodes de regressió. En segon lloc, s'integraren mètodes de processament i d'anàlisi de dades ALSFW en una nova eina anomenada WoLFeX (Waveform Lidar for Forestry eXtraction) que inclou el processos de retallada, correcció radiomètrica relativa, voxelització i extracció de mètriques a partir de les dades ALSFW, així com noves mètriques descriptores del sotabosc. En tercer lloc, s'avalua la influència de l'angle de escaneig emprat en l'adquisició de les dades ALS i la correcció radiomètrica en l'extracció de mètriques ALSFW i en l'estimació d'atributs de combustibilitat forestal. Per a això, s'extragueren mètriques ALSFW amb i sense correcció radiomètrica relativa i emprant diferents angles d'escaneig. En quart lloc, es caracteritza l'oclusió del senyal al llarg de l'estructura vertical del bosc emprant i comparant tres tipus diferents de làser escàner (ALSFW, ALSD i làser escàner terrestre: TLS, per les seues sigles en anglès), determinant així les seues limitacions en la detecció de material vegetatiu en dos ecosistemes diferenciats: un boreal i un mediterrani. Per a quantificar l'oclusió del senyal al llarg de l'estructura vertical del bosc es proposa un nou paràmetre, la taxa de reducció del pols, basada en el percentatge de rajos làser bloquejats abans d'arribar a una posició donada. En cinquè lloc, s'avalua la manera en la qual es detecten i determinen les classes de densitat de sotabosc mitjançant els diferents tipus d'ALS. Es compararen els perfils de distribució vertical en estrats inferiors descrits per l'ALSFW i l'ALSD respecte als descrits pel TLS, emprant aquest últim com a referència. A més a més, es determinaren les classes de densitat de sotabosc aplicant la corba Lorenz i l'índex Gini a partir dels perfils de distribució vertical descrits per l'ALSFW i l'ALSD. Finalment, s'apliquen i avaluen les noves mètriques ALSFW basades en la voxelització, emprant com a referència els atributs extrets a partir del TLS, per a estimar l'alçada, la cobertura i el volum del sotabosc en un ecosistema mediterrani.Crespo Peremarch, P. (2020). Processing and analysis of airborne fullwaveform laser scanning data for the characterization of forest structure and fuel properties [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/153715TESISCompendi

Mobile Laser Scanning – System development, performance and applications

Osajulkaisut: Publication 1: Antero Kukko, Sanna Kaasalainen, and Paula Litkey. 2008. Effect of incidence angle on laser scanner intensity and surface data. Applied Optics, volume 47, number 7, pages 986-992. doi:10.1364/AO.47.000986 Publication 2: Antero Kukko and Juha Hyyppä. 2009. Small-footprint laser scanning simulator for system validation, error assessment, and algorithm development. Photogrammetric Engineering and Remote Sensing, volume 75, number 9, pages 1177-1189. Publication 3: Antero Kukko, Constantin-Octavian Andrei, Veli-Matti Salminen, Harri Kaartinen, Yuwei Chen, Petri Rönnholm, Hannu Hyyppä, Juha Hyyppä, Ruizhi Chen, Henrik Haggrén, Iisakki Kosonen, and Karel Čapek. 2007. Road environment mapping system of the Finnish Geodetic Institute - FGI ROAMER -. In: Petri Rönnholm, Hannu Hyyppä, and Juha Hyyppä (editors). Proceedings of the ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007. Espoo, Finland. 12-14 September 2007. International Society for Photogrammetry and Remote Sensing. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, volume 36, part 3 / W52, pages 241-247. ISSN 1682-1777. Publication 4: Antero Kukko, Harri Kaartinen, Juha Hyyppä, and Yuwei Chen. 2012. Multiplatform mobile laser scanning: Usability and performance. Sensors, volume 12, number 9, pages 11712-11733. doi:10.3390/s120911712 Publication 5: Harri Kaartinen, Juha Hyyppä, Antero Kukko, Anttoni Jaakkola, and Hannu Hyyppä. 2012. Benchmarking the performance of mobile laser scanning systems using a permanent test field. Sensors, volume 12, number 9, pages 12814-12835. doi:10.3390/s120912814 Publication 6: P. Alho, A. Kukko, H. Hyyppä, H. Kaartinen, J. Hyyppä, and A. Jaakkola. 2009. Application of boat-based laser scanning for river survey. Earth Surface Processes and Landforms, volume 34, number 13, pages 1831-1838. doi:10.1002/esp.1879 Publication 7: Matti Vaaja, Juha Hyyppä, Antero Kukko, Harri Kaartinen, Hannu Hyyppä, and Petteri Alho. 2011. Mapping topography changes and elevation accuracies using a mobile laser scanner. Remote Sensing, volume 3, number 3, pages 587-600. doi:10.3390/rs3030587Laser scanning is a surveying technique used for mapping topography, vegetation, urban areas and infrastructure, ice, and other targets of interest. Its application on a terrestrial mobile platform is a promising method for effectively collecting three-dimensional data for complex environments and for producing model information for location-based services necessitating rapidly collected and up-to-date data. Development of mobile laser scanning (MLS) systems for such purposes is presented in this study. Different aspects of this technology were analyzed in laboratory experiments, simulations and field tests, in order to understand their effects on the ranging, intensity and point cloud data, especially in terms of point distribution and accuracy. In order to validate the performance of the developed ROAMER and AKHKA MLS systems, various three-dimensional mapping tasks were performed during an international benchmarking test, as well as in the field in numerous projects. The results showed that the proposed systems can reliably provide accurate data. It has also been shown that the various modalities of the systems allow data acquisition in numerous application scenarios and environments not previously possible. MLS improves the data output compared to terrestrial laser scanning (TLS) and outperforms airborne laser scanning (ALS) in ranging precision and point density. As a result, MLS is well suited to fill the gap between these two previously dominant 3D data acquisition techniques.Laserkeilaus on mittaustekniikka, jota käytetään maaston topografian kasvillisuuden, rakennettujen alueiden, infrastruktuurin, jään ja muiden kohteiden kartoitukseen. Tekniikan soveltaminen liikkuvalle alustalle on lupaava menetelmä monimuotoisten ympäristöjen tehokkaaseen kolmiulotteiseen mittaamiseen ja mallinnustiedon tuottamiseen paikkatietopalveluihin, jotka edellyttävät tiedon nopeaa hankintaa ja ajantasaisuutta. Tässä tutkimuksessa kehitettiin liikkuvia laserkeilausjärjestelmiä (MLS). Eri tekijöiden vaikutuksia etäisyys- ja intensiteettihavaintoihin, pistejakaumaan ja tarkkuuteen selvitettiin laboratoriokokein, simuloimalla ja koetöin. Tutkimuksessa kehitettyjen ROAMER ja AKHKA MLS-järjestelmien suorituskykyä kolmiulotteisen mittaustiedon tuottamiseen erilaisissa kartoitustehtävissä tutkittiin kansainvälisessä vertailututkimuksessa kaupunkitestikentän avulla, mutta lisäksi käytännön sovelluksissa useassa eri projektissa. Tutkimuksen tulokset osoittavat, että kehitetyt MLS järjestelmät tuottavat tarkkaa tietoa luotettavasti. Järjestelmien monikäyttöisyys mahdollistaa aineistonhankinnan eri sovellustapauksissa ja ympäristöissä tavalla, joka ei ole aikaisemmin ollut mahdollista. Liikkuva laserkeilaus parantaa merkittävästi mittauksen tehokkuutta maalaserkeilaukseen verrattuna, ja ylittää lentolaserkeilauksen suorituskyvyn etäisyysmittauksen tarkkuudessa ja pistetiheydessä. Liikkuva laserkeilaus tarjoaakin näitä kahta aikaisemmin vallitsevaa 3D-mittausteknologiaa hyvin täydentävän kartoitusmenetelmän

Applications of multi-spectral lidar: river channel bathymetry and canopy vegetation indices

This thesis investigates the potential of new state-of-the-art multi-spectral (ms) lidar technology and develops methodologies for applications in water, wetlands, and forest resource monitoring. The scope of the thesis is split into two parts – lidar bathymetry and lidar radiometry. The first topic addresses the need for urban river environment bathymetry and refining of ms lidar data processing routines in complex riparian environments. The second topic presents a framework of experiments to investigate ms lidar radiometry. As a result, a new routine for bathymetric correction was developed. The consistency of spectral vegetation indices (SVIs) through a variety of altitudes was investigated. Radiometric targets were constructed and, after radiometric calibration, comparability of reflectance values derived from ms lidar data to available spectral libraries was shown. Finally, forest plot-level canopy vertical SVI profiles were developed while attempting to understand attenuation losses due to sub-footprint reflectors within the canopy by means of additional complex radiometric targets

A Nonparametric Approach to Segmentation of Ladar Images

The advent of advanced laser radar (ladar) systems that record full-waveform signal data has inspired numerous inquisitions which aspire to extract additional, previously unavailable, information about the illuminated scene from the collected data. The quality of the information, however, is often related to the limitations of the ladar camera used to collect the data. This research project uses full-waveform analysis of ladar signals, and basic principles of optics, to propose a new formulation for an accepted signal model. A new waveform model taking into account backscatter reflectance is the key to overcoming specific deficiencies of the ladar camera at hand, namely the ability to discern pulse-spreading effects of elongated targets. A concert of non-parametric statistics and familiar image processing methods are used to calculate the orientation angle of the illuminated objects, and the deficiency of the hardware is circumvented. Segmentation of the various ladar images performed as part of the angle estimation, and this is shown to be a new and effective strategy for analyzing the output of the AFIT ladar camera

The Algorithm Theoretical Basis Document for the Derivation of Range and Range Distributions from Laser Pulse Waveform Analysis for Surface Elevations, Roughness, Slope, and Vegetation Heights

The primary purpose of the GLAS instrument is to detect ice elevation changes over time which are used to derive changes in ice volume. Other objectives include measuring sea ice freeboard, ocean and land surface elevation, surface roughness, and canopy heights over land. This Algorithm Theoretical Basis Document (ATBD) describes the theory and implementation behind the algorithms used to produce the level 1B products for waveform parameters and global elevation and the level 2 products that are specific to ice sheet, sea ice, land, and ocean elevations respectively. These output products, are defined in detail along with the associated quality, and the constraints, and assumptions used to derive them

A Signal processing approach for preprocessing and 3d analysis of airborne small-footprint full waveform lidar data

The extraction of structural object metrics from a next generation remote sensing modality, namely waveform light detection and ranging (LiDAR), has garnered increasing interest from the remote sensing research community. However, a number of challenges need to be addressed before structural or 3D vegetation modeling can be accomplished. These include proper processing of complex, often off-nadir waveform signals, extraction of relevant waveform parameters that relate to vegetation structure, and from a quantitative modeling perspective, 3D rendering of a vegetation object from LiDAR waveforms. Three corresponding, broad research objectives therefore were addressed in this dissertation. Firstly, the raw incoming LiDAR waveform typically exhibits a stretched, misaligned, and relatively distorted character. A robust signal preprocessing chain for LiDAR waveform calibration, which includes noise reduction, deconvolution, waveform registration, and angular rectification is presented. This preprocessing chain was validated using both simulated waveform data of high fidelity 3D vegetation models, which were derived via the Digital Imaging and Remote Sensing Image Generation (DIRSIG) modeling environment and real small-footprint waveform LiDAR data, collected by the Carnegie Airborne Observatory (CAO) in a savanna region of South Africa. Results showed that the preprocessing approach significantly increased our ability to recover the temporal signal resolution, and resulted in improved waveform-based vegetation biomass estimation. Secondly, a model for savanna vegetation biomass was derived using the resultant processed waveform data and by decoding the waveform in terms of feature metrics for woody and herbaceous biomass estimation. The results confirmed that small-footprint waveform LiDAR data have significant potential in the case of this application. Finally, a 3D image clustering-based waveform LiDAR inversion model was developed for 1st order (principal branch level) 3D tree reconstruction in both leaf-off and leaf-on conditions. These outputs not only contribute to the visualization of complex tree structures, but also benefit efforts related to the quantification of vegetation structure for natural resource applications from waveform LiDAR data

Extraction of Vegetation Biophysical Structure from Small-Footprint Full-Waveform Lidar Signals

The National Ecological Observatory Network (NEON) is a continental scale environmental monitoring initiative tasked with characterizing and understanding ecological phenomenology over a 30-year time frame. To support this mission, NEON collects ground truth measurements, such as organism counts and characterization, carbon flux measurements, etc. To spatially upscale these plot-based measurements, NEON developed an airborne observation platform (AOP), with a high-resolution visible camera, next-generation AVIRIS imaging spectrometer, and a discrete and waveform digitizing light detection and ranging (lidar) system. While visible imaging, imaging spectroscopy, and discrete lidar are relatively mature technologies, our understanding of and associated algorithm development for small-footprint full-waveform lidar are still in early stages of development. This work has as its primary aim to extend small-footprint full-waveform lidar capabilities to assess vegetation biophysical structure. In order to fully exploit waveform lidar capabilities, high fidelity geometric and radio-metric truth data are needed. Forests are structurally and spectrally complex, which makes collecting the necessary truth challenging, if not impossible. We utilize the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, which provides an environment for radiometric simulations, in order to simulate waveform lidar signals. The first step of this research was to build a virtual forest stand based on Harvard Forest inventory data. This scene was used to assess the level of geometric fidelity necessary for small-footprint waveform lidar simulation in broadleaf forests. It was found that leaves have the largest influence on the backscattered signal and that there is little contribution to the signal from the leaf stems and twigs. From this knowledge, a number of additional realistic and abstract virtual “forest” scenes were created to aid studies assessing the ability of waveform lidar systems to extract biophysical phenomenology. We developed an additive model, based on these scenes, for correcting the attenuation in backscattered signal caused by the canopy. The attenuation-corrected waveform, when coupled with estimates of the leaf-level reflectance, provides a measure of the complex within-canopy forest structure. This work has implications for our improved understanding of complex waveform lidar signals in forest environments and, very importantly, takes the research community a significant step closer to assessing fine-scale horizontally- and vertically-explicit leaf area, a holy grail of forest ecology