On the use of rapid-scan, low point density terrestrial laser scanning (TLS) for structural assessment of complex forest environments

Forests fulfill an important role in natural ecosystems, e.g., they provide food, fiber, habitat, and biodiversity, all of which contribute to stable ecosystems. Assessing and modeling the structure and characteristics in forests can lead to a better understanding and management of these resources. Traditional methods for collecting forest traits, known as “forest inventory”, is achieved using rough proxies, such as stem diameter, tree height, and foliar coverage; such parameters are limited in their ability to capture fine-scale structural variation in forest environments. It is in this context that terrestrial laser scanning (TLS) has come to the fore as a tool for addressing the limitations of traditional forest structure evaluation methods. However, there is a need for improving TLS data processing methods. In this work, we developed algorithms to assess the structure of complex forest environments – defined by their stem density, intricate root and stem structures, uneven-aged nature, and variable understory - using data collected by a low-cost, portable TLS system, the Compact Biomass Lidar (CBL). The objectives of this work are listed as follow: 1. Assess the utility of terrestrial lidar scanning (TLS) to accurately map elevation changes (sediment accretion rates) in mangrove forest; 2. Evaluate forest structural attributes, e.g., stems and roots, in complex forest environments toward biophysical characterization of such forests; and 3. Assess canopy-level structural traits (leaf area index; leaf area density) in complex forest environments to estimate biomass in rapidly changing environments. The low-cost system used in this research provides lower-resolution data, in terms of scan angular resolution and resulting point density, when compared to higher-cost commercial systems. As a result, the algorithms developed for evaluating the data collected by such systems should be robust to issues caused by low-resolution 3D point cloud data. The data used in various parts of this work were collected from three mangrove forests on the western Pacific island of Pohnpei in the Federated States of Micronesia, as well as tropical forests in Hawai’i, USA. Mangrove forests underscore the economy of this region, where more than half of the annual household income is derived from these forests. However, these mangrove forests are endangered by sea level rise, which necessitates an evaluation of the resilience of mangrove forests to climate change in order to better protect and manage these ecosystems. This includes the preservation of positive sediment accretion rates, and stimulating the process of root growth, sedimentation, and peat development, all of which are influenced by the forest floor elevation, relative to sea level. Currently, accretion rates are measured using surface elevation tables (SETs), which are posts permanently placed in mangrove sediments. The forest floor is measured annually with respect to the height of the SETs to evaluate changes in elevation (Cahoon et al. 2002). In this work, we evaluated the ability of the CBL system for measuring such elevation changes, to address objective #1. Digital Elevation Models (DEMs) were produced for plots, based on the point cloud resulted from co-registering eight scans, spaced 45 degree, per plot. DEMs are refined and produced using Cloth Simulation Filtering (CSF) and kriging interpolation. CSF was used because it minimizes the user input parameters, and kriging was chosen for this study due its consideration of the overall spatial arrangement of the points using semivariogram analysis, which results in a more robust model. The average consistency of the TLS-derived elevation change was 72%, with and RMSE value of 1.36 mm. However, what truly makes the TLS method more tenable, is the lower standard error (SE) values when compared to manual methods (10-70x lower). In order to achieve our second objective, we assessed structural characteristics of the above-mentioned mangrove forest and also for tropical forests in Hawaii, collected with the same CBL scanner. The same eight scans per plot (20 plots) were co-registered using pairwise registration and the Iterative Closest Point (ICP). We then removed the higher canopy using a normal change rate assessment algorithm. We used a combination of geometric classification techniques, based on the angular orientation of the planes fitted to points (facets), and machine learning 3D segmentation algorithms to detect tree stems and above-ground roots. Mangrove forests are complex forest environments, containing above-ground root mass, which can create confusion for both ground detection and structural assessment algorithms. As a result, we needed to train a supporting classifier on the roots to detect which root lidar returns were classified as stems. The accuracy and precision values for this classifier were assessed via manual investigation of the classification results in all 20 plots. The accuracy and precision for stem classification were found to be 82% and 77%, respectively. The same values for root detection were 76% and 68%, respectively. We simulated the stems using alpha shapes in order to assess their volume in the final step. The consistency of the volume evaluation was found to be 85%. This was obtained by comparing the mean stem volume (m3/ha) from field data and the TLS data in each plot. The reported accuracy is the average value for all 20 plots. Additionally, we compared the diameter-at-breast-height (DBH), recorded in the field, with the TLS-derived DBH to obtain a direct measure of the precision of our stem models. DBH evaluation resulted in an accuracy of 74% and RMSE equaled 7.52 cm. This approach can be used for automatic stem detection and structural assessment in a complex forest environment, and could contribute to biomass assessment in these rapidly changing environments. These stem and root structural assessment efforts were complemented by efforts to estimate canopy-level structural attributes of the tropical Hawai’i forest environment; we specifically estimated the leaf area index (LAI), by implementing a density-based approach. 242 scans were collected using the portable low-cost TLS (CBL), in a Hawaii Volcano National Park (HAVO) flux tower site. LAI was measured for all the plots in the site, using an AccuPAR LP-80 Instrument. The first step in this work involved detection of the higher canopy, using normal change rate assessment. After segmenting the higher canopy from the lidar point clouds, we needed to measure Leaf Area Density (LAD), using a voxel-based approach. We divided the canopy point cloud into five layers in the Z direction, after which each of these five layers were divided into voxels in the X direction. The sizes of these voxels were constrained based on interquartile analysis and the number of points in each voxel. We hypothesized that the power returned to the lidar system from woody materials, like branches, exceeds that from leaves, due to the liquid water absorption of the leaves and higher reflectivity for woody material at the 905 nm lidar wavelength. We evaluated leafy and woody materials using images from projected point clouds and determined the density of these regions to support our hypothesis. The density of points in a 3D grid size of 0.1 m, which was determined by investigating the size of the branches in the lower portion of the higher canopy, was calculated in each of the voxels. Note that “density” in this work is defined as the total number of points per grid cell, divided by the volume of that cell. Subsequently, we fitted a kernel density estimator to these values. The threshold was set based on half of the area under the curve in each of the distributions. The grid cells with a density below the threshold were labeled as leaves, while those cells with a density above the threshold were set as non-leaves. We then modeled the LAI using the point densities derived from TLS point clouds, achieving a R2 value of 0.88. We also estimated the LAI directly from lidar data by using the point densities and calculating leaf area density (LAD), which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was found to be 90%. Since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed a semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets, where each of the plots were 30 meter spaced for each subset. LAI model R2 values for these subsets ranged between 0.84 - 0.96. The results bode well for using this method for automatic estimation of LAI values in complex forest environments, using a low-cost, low point density, rapid-scan TLS

Estimation de paramètres structuraux des arbres dans une savane à partir de mesures LiDAR terrestre et d'imagerie à très haute résolution spatiale

This thesis takes its place in a context where information on the biophysical state of forest ecosystems at spatial scales only remote sensing can retrieve is in demand more than ever. In order to provide reliable information using validated approaches, the remote sensing research community recognises the need for new and innovative methods, especially in heterogeneous environments like savannas. The recent emergence of terrestrial LiDAR scanners (TLS) and the increase in the computational capability of computers which allow running ray tracing model simulations with a high level of realism hold great potential to improve our understanding of the processes influencing the radiance measured by satellite sensors. This thesis makes use of these two cutting edge technologies for estimating the spatial distribution of tree leaf area, a key element of modeling radiative transfer processes. The first part of the thesis concerns the development of methods for estimating tridimensional leaf area distribution in a savanna environment from TLS measurements. The methods presented address certain issues related to TLs measures affecting the application of classical theories (the probability of light transmission and the contact frequency) to the estimation of leaf area through indirect means. These issues pertain to the cross-section of laser pulses emitted by a TLS and the occlusion effects caused by the interception of laser pulses by material inside the crown. The developed methods also exploit additional information provided by the active nature of the TLS sensor that is not available to passive sensors like hemispherical photography, i.e. the intensity of a pulse return offers the possibility to distinguish between energy interception by wood and foliage. A simplified approach of this method is presented to promote its use by other research groups. This approach consists of a series of parameterisations and represents a significant gain in terms of the required resources to produce the leaf area, estimates. The second part of the thesis explores the combination of the tree representations generated in the first part with a ray tracing model to simulate the interactions of light with tree crowns. This approach is highly innovative and our study showed its potential to improve our understanding of the factors influencing the radiative environment in a savanna. The methods presented offer a solution to map leaf area at the individual tree scale over large areas from very high spatial resolution imagery

The novel use of proximal photogrammetry and terrestrial LiDAR to quantify the structural complexity of orchard trees

Within the agrifood sector, the production of high yields is a driver for UK orchard husbandry. Currently, orchard tree management is typically a non-discriminatory method with all trees subjected to the same interventions. Previous studies indicate that structural complexity of individual orchard trees is an indicator for future yield, which can guide the management of individual trees. However, data on the structure of individual trees is often limited. This study investigated the suitability of using remote sensing methods to capture data that can be used to quantify tree structure. Descriptive metrics based on the mathematical assessment of self-affinity and dimensionality were applied to the remotely-sensed data to quantify tree structure, and were also analysed for suitability as a predictor of fruit yield. The findings suggest that while proximal photogrammetry is informative, terrestrial LiDAR data can be used to quantify structural complexity most effectively and this approach holds greater potential for informing orchard management

Terrestrial laser scanning to predict canopy area metrics, water storage capacity, and throughfall redistribution in small trees

Urban trees deliver many ecological services to the urban environment, including reduced runoff generation in storms. Trees intercept rainfall and store part of the water on leaves and branches, reducing the volume and velocity of water that reaches the soil. Moreover, trees modify the spatial distribution of rainwater under the canopy. However, measuring interception parameters is a complex task because it depends on many factors, including environmental conditions (rainfall intensity, wind speed, etc.) and tree characteristics (plant surface area, leaf and branch inclination angle, etc.). In the few last decades, remotely sensed data have been tested for retrieving tree metrics, but the use of this derived data for predicting interception parameters are still being developed. In this study, we measured the minimum water storage capacity (Cmin) and throughfall under the canopies of 12 trees belonging to three different species. All trees had their plant surface metrics calculated: plant surface area (PSA), plant area index (PAI), and plant area density (PAD). Trees were scanned with a mobile terrestrial laser scan (TLS) to obtain their individual canopy point clouds. Point clouds were used to calculate canopy metrics (canopy projected area and volume) and TLS-derived surface metrics. Measured surface metrics were then correlated to derived TLS metrics, and the relationship between TLS data and interception parameters was tested. Additionally, TLS data was used in analyses of throughfall distribution on a sub-canopy scale. The significant correlation between the directly measured surface area and TLS-derived metrics validates the use of the remotely sensed data for predicting plant area metrics. Moreover, TLS-derived metrics showed a significant correlation with a water storage capacity parameter (Cmin). The present study supports the use of TLS data as a tool for measuring tree metrics and ecosystem services such as Cmin; however, more studies to understand how to apply remotely sensed data into ecological analyses in the urban environment must be encouraged

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation

Mapping change of functional forest traits and diversity using airborne laser scanning in the canton Aargau 2014-2019

Forests contribute substantially to ecosystem functions and services making their ecological quality valuable. Due to climate change, monitoring diversity is becoming increasingly important to record a possible decline. High functional diversity has been related to a decreasing vulnerability to disturbances like diseases, storms and insect attacks. Remote sensing and especially LiDAR are promising methods to assess functional traits and diversity in forests and have been linked to plant diversity and ecosystem functioning. However, large-scale and multitemporal analyses using LiDAR datasets are just at the beginning. This thesis aims to assess functional forest traits and diversity metrics out of ALS data and to compare them between the years 2014 and 2019. Three morphological traits, namely canopy height, foliage height diversity and plant area index were estimated for the entire forest area of the canton Aargau under defoliated conditions. Then, functional richness and divergence were computed out of the traits. For three subregions of the canton, occlusion in the lower canopy was computed to assess if traits and diversity metrics are influenced. More complex derivations of ALS point clouds, e.g. plant area index, richness or divergence, were found to be more sensitive to external influences like different sensor and flight settings and occluded fractions of the canopy volume. Various spatial patterns of the derived traits and diversity metrics were mapped, e.g. a decrease or smaller increase in steep and high altitude regions. Richness values showed a very large global increase of 123%, which cannot solely be attributed to biotic changes, but is rather caused by the sensitivity to sensor-related factors. The results demonstrate how the development of robust methods for trait and diversity estimations is important. The incorporation of sensor and flight parameters into the estimation methods is crucial for improved performance in multitemporal analyses using ALS point clouds

Using LiDAR data formonitoring vegetation in urban areas

U ovom radu prikazan je način prikupljanja, obrade, analize i interpretacije podataka o urbanoj vegetaciji dobivenih laserskim skeniranjem iz zraka na primjeru Trga Nikole Šubića Zrinskoga u Zagrebu. Cilj ovog diplomskog rada je na temelju podataka dobivenih laserskim skeniranjem iz zraka, prikazati obradu 3D oblaka točaka, klasifikaciju, vizualizaciju i različite načine i mogućnosti uporabe takvih podataka pri praćenju i izmjeri vegetacije urbanih područja. Laserskim skeniranjem iz zraka i obradom prikupljenih podataka dobiven je georeferencirani 3D oblak točaka klasificiran u 4 klase. Dobiveni rezultati obuhvaćaju prikaz oblaka klasificiranih točaka u tri dimenzije koje zadržavaju sve svoje prostorne informacije (x, y, z koordinatu), statističku usporedbu i grafički prikaz podataka dobivenih terenskom izmjerom i laserskim skeniranjem iz zraka te uspostavu GIS baze podataka. Srednja vrijednost visina izmjerenih na terenu iznosi 24,02 m, a ona dobivena iz oblaka točaka 23,0 m. Statistička i grafička obrada su pokazale snažnu povezanost podataka. Koeficijent korelacije između dvije izmjere statistički je značajan i iznosi rs=0,91. LiDAR tehnologija bazirana na laserskom skeniranju s tla ili iz zraka pokazuje se kao široko primjenjiva tehnologija koja svoju upotrebu pronalazi i u analizama vegetacije urbanih područja gdje omogućava dobivanje podataka koji nadopunjuju standardne metode fotointerpretacijeThis paper describes methods used for gathering, processing, analysis and interpretation of data on urban vegetation gained by laser scanning from the air, based on the example from Nikola Šubić Zrinski square in Zagreb. The goal of this thesis was to show processing of 3D point cloud data, classification, visualization and different methods and possibilities for implementing such data in measuring and monitoring vegetation in urban areas, based on the laser scanning from the air. Laser scanning from the air and analysis of gathered data resulted in georeferenced 3D point cloud, classified into 4 classes. Obtained results include the demonstration of classified point cloud data in three dimensions, keeping all the spatial information (x, y, z coordinates), statistical comparison and graphical display of data obtained from the field measuring and laser scanning from the air, and formation of GIS database. Average tree height form field measurements equated to 24,02 meters, while the average height obtained from the point cloud equated to 23,0 meters. Statistical and graphical analysis demonstrated strong correlation between data. Correlation coefficient between two measuring was statistically significant and equated to rs=0,91. LiDAR technology, based on laser scanning from the air or from the ground, proved to be widely applicable technology that also finds usage in urban areas vegetation analysis where it enables data gathering which complements with standard photointerpretation methods

Using LiDAR data formonitoring vegetation in urban areas

U ovom radu prikazan je način prikupljanja, obrade, analize i interpretacije podataka o urbanoj vegetaciji dobivenih laserskim skeniranjem iz zraka na primjeru Trga Nikole Šubića Zrinskoga u Zagrebu. Cilj ovog diplomskog rada je na temelju podataka dobivenih laserskim skeniranjem iz zraka, prikazati obradu 3D oblaka točaka, klasifikaciju, vizualizaciju i različite načine i mogućnosti uporabe takvih podataka pri praćenju i izmjeri vegetacije urbanih područja. Laserskim skeniranjem iz zraka i obradom prikupljenih podataka dobiven je georeferencirani 3D oblak točaka klasificiran u 4 klase. Dobiveni rezultati obuhvaćaju prikaz oblaka klasificiranih točaka u tri dimenzije koje zadržavaju sve svoje prostorne informacije (x, y, z koordinatu), statističku usporedbu i grafički prikaz podataka dobivenih terenskom izmjerom i laserskim skeniranjem iz zraka te uspostavu GIS baze podataka. Srednja vrijednost visina izmjerenih na terenu iznosi 24,02 m, a ona dobivena iz oblaka točaka 23,0 m. Statistička i grafička obrada su pokazale snažnu povezanost podataka. Koeficijent korelacije između dvije izmjere statistički je značajan i iznosi rs=0,91. LiDAR tehnologija bazirana na laserskom skeniranju s tla ili iz zraka pokazuje se kao široko primjenjiva tehnologija koja svoju upotrebu pronalazi i u analizama vegetacije urbanih područja gdje omogućava dobivanje podataka koji nadopunjuju standardne metode fotointerpretacijeThis paper describes methods used for gathering, processing, analysis and interpretation of data on urban vegetation gained by laser scanning from the air, based on the example from Nikola Šubić Zrinski square in Zagreb. The goal of this thesis was to show processing of 3D point cloud data, classification, visualization and different methods and possibilities for implementing such data in measuring and monitoring vegetation in urban areas, based on the laser scanning from the air. Laser scanning from the air and analysis of gathered data resulted in georeferenced 3D point cloud, classified into 4 classes. Obtained results include the demonstration of classified point cloud data in three dimensions, keeping all the spatial information (x, y, z coordinates), statistical comparison and graphical display of data obtained from the field measuring and laser scanning from the air, and formation of GIS database. Average tree height form field measurements equated to 24,02 meters, while the average height obtained from the point cloud equated to 23,0 meters. Statistical and graphical analysis demonstrated strong correlation between data. Correlation coefficient between two measuring was statistically significant and equated to rs=0,91. LiDAR technology, based on laser scanning from the air or from the ground, proved to be widely applicable technology that also finds usage in urban areas vegetation analysis where it enables data gathering which complements with standard photointerpretation methods

Using LiDAR data formonitoring vegetation in urban areas

U ovom radu prikazan je način prikupljanja, obrade, analize i interpretacije podataka o urbanoj vegetaciji dobivenih laserskim skeniranjem iz zraka na primjeru Trga Nikole Šubića Zrinskoga u Zagrebu. Cilj ovog diplomskog rada je na temelju podataka dobivenih laserskim skeniranjem iz zraka, prikazati obradu 3D oblaka točaka, klasifikaciju, vizualizaciju i različite načine i mogućnosti uporabe takvih podataka pri praćenju i izmjeri vegetacije urbanih područja. Laserskim skeniranjem iz zraka i obradom prikupljenih podataka dobiven je georeferencirani 3D oblak točaka klasificiran u 4 klase. Dobiveni rezultati obuhvaćaju prikaz oblaka klasificiranih točaka u tri dimenzije koje zadržavaju sve svoje prostorne informacije (x, y, z koordinatu), statističku usporedbu i grafički prikaz podataka dobivenih terenskom izmjerom i laserskim skeniranjem iz zraka te uspostavu GIS baze podataka. Srednja vrijednost visina izmjerenih na terenu iznosi 24,02 m, a ona dobivena iz oblaka točaka 23,0 m. Statistička i grafička obrada su pokazale snažnu povezanost podataka. Koeficijent korelacije između dvije izmjere statistički je značajan i iznosi rs=0,91. LiDAR tehnologija bazirana na laserskom skeniranju s tla ili iz zraka pokazuje se kao široko primjenjiva tehnologija koja svoju upotrebu pronalazi i u analizama vegetacije urbanih područja gdje omogućava dobivanje podataka koji nadopunjuju standardne metode fotointerpretacijeThis paper describes methods used for gathering, processing, analysis and interpretation of data on urban vegetation gained by laser scanning from the air, based on the example from Nikola Šubić Zrinski square in Zagreb. The goal of this thesis was to show processing of 3D point cloud data, classification, visualization and different methods and possibilities for implementing such data in measuring and monitoring vegetation in urban areas, based on the laser scanning from the air. Laser scanning from the air and analysis of gathered data resulted in georeferenced 3D point cloud, classified into 4 classes. Obtained results include the demonstration of classified point cloud data in three dimensions, keeping all the spatial information (x, y, z coordinates), statistical comparison and graphical display of data obtained from the field measuring and laser scanning from the air, and formation of GIS database. Average tree height form field measurements equated to 24,02 meters, while the average height obtained from the point cloud equated to 23,0 meters. Statistical and graphical analysis demonstrated strong correlation between data. Correlation coefficient between two measuring was statistically significant and equated to rs=0,91. LiDAR technology, based on laser scanning from the air or from the ground, proved to be widely applicable technology that also finds usage in urban areas vegetation analysis where it enables data gathering which complements with standard photointerpretation methods

Étude de la structure et de la dynamique des houppiers à partir de données de LIDAR terrestre

RÉSUMÉ: La structure de la canopée joue plusieurs rôles importants dans le fonctionnement des écosystèmes forestiers. Elle est définie par l’emplacement, la taille et la forme des arbres qui la compose. Elle peut donc être étudiée à l’échelle du peuplement entier, ou à celle des arbres individuels. Toutefois, les dimensions, la complexité et la longévité des arbres rendent l’étude de leur structure difficile. Depuis une dizaine d’années, des technologies comme le LiDAR (Light Detection And Ranging) ont fait leur apparition dans le monde de l’écologie et de l’aménagement des forêts. Ce type d’outil fournit une représentation tridimensionnelle (3D) très précise de la structure de la canopée. L’objectif global de ma thèse était d’étudier la structure et la dynamique des houppiers d’arbres individuels à l’aide du LiDAR terrestre (LiDAR-t) dans un contexte de réponse à la diversité du peuplement. Dans le premier chapitre, l’objectif était d’étudier l’effet de la mixité sur la structure du houppier de l’érable à sucre et sur la pression de compétition qu’il subit. De nouvelles métriques de structure du houppier et des indices de compétition ont été développées à partir de données de LiDAR-t. Les résultats montrent que la pression de compétition est moins forte en peuplement mixte et que les érables occupent l’espace de façon plus efficace. Ces résultats illustrent la grande plasticité du houppier de l’érable à sucre. Par ailleurs, ils soutiennent l’importance d’un aménagement des forêts plus complexe où la diversité spécifique peut entrainer une complémentarité des traits de houppier et une meilleure exploitation de l’espace. Finalement, la capacité du LiDAR-t à décrire la structure des houppiers et la compétition pour la lumière est mise en évidence au travers de l’approche utilisée. Dans le deuxième chapitre, l’objectif était de quantifier des profils verticaux des feuilles et du bois à partir de données de LiDAR-t et de comparer la forme des distributions entre espèces (érable à sucre et sapin baumier) et types de peuplement (pur et mixte). Une méthode a été mise au point pour discriminer la matière foliaire de la matière ligneuse à partir d’une approche géométrique sur le nuage de point. Les résultats montrent que la distribution du feuillage de l’érable à sucre est plus basse en peuplement mixte qu’en peuplement pur. L’inverse est observé pour le sapin baumier. Ceci suggère une fois de plus que l’érable à sucre est avantagé en peuplement mixte par rapport au peuplement pur. Ce n’est en revanche pas le cas pour le sapin baumier. Finalement les avantages et les limites de la méthode de séparation du feuillage et du bois sont soulevés. L’objectif du troisième chapitre était de mettre au point une méthode permettant de quantifier les changements du houppier des arbres à partir de données multitemporelles de LiDAR-t. Le principe de la méthode est de récupérer tous les points du nuage de points au temps tx qui se trouvent au-delà de la limite du houppier au temps t0 (défini par une enveloppe). L’approche a été appliquée à titre d’exemple pour quantifier la réponse d’érable à sucre et de sapin baumier aux trouées. Les résultats montrent que les érables à sucre ont une réponse beaucoup plus forte que les sapins baumiers. Par ailleurs, les deux espèces ont tendance à ouvrir leur houppier en réponse au dégagement des compétiteurs et à réoccuper l’espace vers le bas. Ces résultats révèlent une fois de plus l’importante plasticité de l’érable à sucre et l’importance de quantifier les changements de la végétation dans toutes les directions. Finalement, les applications potentielles de la méthode à d’autres espèces et à la dynamique des trouées sont discutées. Au travers de ces trois chapitres, ma thèse de doctorat a permis de relever plusieurs défis méthodologiques liés à l’utilisation du LiDAR-t en forêt. À partir de ces méthodes, ma thèse a répondu à des questions d’écologie et du développement des arbres. Ainsi, des approches statiques comparant, des indices de compétition, des métriques 3D et des profils de houppier à un instant donné dans différents types de peuplement ont été utilisées dans le premier et deuxième chapitre. Une approche dynamique permettant un suivi précis de la réoccupation de l’espace par les houppiers a été utilisée dans le troisième chapitre. Ces approches ont permis de caractériser l’occupation de l’espace des deux espèces en fonction de l’environnement local. La plasticité importante du houppier des érables à sucre et les effets bénéfiques de la diversité du peuplement sur son développement ont été mis en évidence. Le sapin baumier montre une réponse à la diversité du peuplement plus mitigé. Ces résultats soulèvent alors plusieurs questions et ouvrent des perspectives de recherche quant à l’effet de la diversité sur l’occupation de l’espace à l’échelle du peuplement. -- Mot(s) clé(s) en français : LiDAR-t, peuplement mixte, plasticité, compétition pour la lumière, structure du houppier, développement du houppier, distribution vertical. -- ABSTRACT: The canopy structure plays many roles in the processes occurring in forest ecosystems. Canopy structure can be defined as the position, size and shape of the tree crowns that compose it. It can be studied at the stand or at the tree scale. The dimensions, complexity and longevity of trees make it hard to study the canopy. In the last decade, LiDAR (Light Detection and Ranging) technologies have increased in popularity in forest ecology and management studies. These tools offer a very accurate three-dimensional representation of the canopy. In the context of tree response to stand diversity, the objective of my thesis was to study the structure and the dynamics of tree crowns using terrestrial LiDAR data (t-LiDAR). The objective of the first chapter was to study the effect of mixing on the competition for light and on sugar maple tree crown structure. New crown metrics and competition indices were developed using t-LiDAR data. Results show that competitive pressure is lower in mixed stands than in pure ones. Moreover, sugar maple occupies the space more efficiently in mixed stands. These results revealed the high plasticity of sugar maple tree crowns and highlighted the potential advantages of managing forests in a more complex way, in order to optimize the use of the canopy space. Finally, our approach underlines the t-LiDAR efficiency to quantify tree crown structure and competition for light. The objective of the second chapter was to quantify vertical distribution profiles of the leaves and wood using t-LiDAR data. The distributions between two species (sugar maple and balsam fir) and between two types of stands (pure and mixed) were compared. We developed a method to separate woody from leafy material from the point cloud using a geometrical approach. Results on sugar maple show that the foliage distribution is lower in the crown in mixed stands than in pure ones and the opposite behaviour was observed for balsam fir. This suggests, once again, that sugar maple can take advantage of the diversity in mixed stands. This is, however, not the case for balsam fir. Finally, advantages and limitations of the wood/leaf separation method were discussed. The objective of the third chapter was to develop a method to quantify crown changes using multi-temporal t-LiDAR data. The idea of the approach was to extract all the points at time tx outside the crown hull of t0. The method was used to quantify sugar maple and balsam fir response to gap formation. Results show that sugar maple has a stronger response than balsam fir to canopy opening and that both species reoccupy the space downward after a gap formation. These results highlight once again the high tree crown plasticity of sugar maple and the importance to quantify changes in all directions. Finally, the potential applications of the method to other species and to study gap dynamics were discussed. My PhD thesis has faced important methodological challenges in t-LiDAR data treatment in forest science. The proposed developments enabled me to answer questions about tree development and ecology. In the first and second chapters, static approaches were used to compare at a given time vertical distributions, three-dimensional metrics and competition indices in different stand types. In the third chapter, a dynamic approach was proposed to accurately follow the space colonization of tree crowns. These approaches quantified canopy space occupation of the two studied species in various local environments. The high plasticity of sugar maple and its positive response to mixing in terms of space occupation was highlighted. Balsam fir responses were, on the other hand, not as strong. These results brings up questions and opens research perspectives about the positive effect of diversity at the stand scale. -- Mot(s) clé(s) en anglais : t-LiDAR, Mixed stands, plasticity, competition for light, crown structure, crown development, vertical distribution