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Although conflict archaeology is now well established, the archaeological remains of many specific military confrontations are still to be explored. This article reports the results of fieldwork to document the site of the Battle of the Bulge (16 December 1944-25 January 1945). The authors use drone-mounted 1m-resolution LiDAR and very high-resolution simultaneous localisation and mapping (SLAM) methods to reveal more than 940 features within the forested Ardennes landscape, many of which were subsequently visited and confirmed. As well as highlighting the potential of the LiDAR-SLAM method, deployed here (both in this geographic region and in conflict archaeology) for the first time, the survey results emphasise the need for a debate on managing the heritage of a key modern conflict landscape in Europe. Keywords: Belgium, conflict archaeology, Second World War, Battle of the Bulge, SLAM, LiDAR, UAV, Introduction Modern conflict archaeology is a rapidly evolving discipline that deals with the material legacies and complex landscapes of war of recent warfare (Saunders 2012). While in the UK and [...]

Cut-to-length harvesters collect useful information for modeling relationships between forest attributes and airborne laser scanning (ALS) data. However, harvesters operate in mature forests, which may introduce selection biases that can result in systematic errors in harvester data-based forest attribute maps. We fitted regression models (harvester models) for volume (V), height (HL), stem frequency (N), above-ground biomass, basal area, and quadratic mean diameter (QMD) using harvester and ALS data. Performances of the harvester models were evaluated using national forest inventory plots in an 8.7 Mha study area. We estimated biases of large-area synthetic estimators and compared efficiencies of model-assisted (MA) estimators with field data-based direct estimators. The harvester models performed better in productive than unproductive forests, but systematic errors occurred in both. The use of MA estimators resulted in efficiency gains that were largest for HL (relative efficiency, RE = 6.0) and smallest for QMD (RE = 1.5). The bias of the synthetic estimator was largest for N (39%) and smallest for V (1%). The latter was due to an overestimation of deciduous and an underestimation of spruce forests that by chance balanced. We conclude that a probability sample of reference observations may be required to ensure the unbiasedness of estimators utilizing harvester data. Key words: cut-to-length harvester data, model-assisted estimation, national forest inventory, airborne LiDAR, large- area estimation, 1. Introduction The increased availability of wall-to-wall airborne laser scanning (ALS) data during the past 20 years has revolutionized the mapping of forest resources (Maltamo et al. 2021). Many countries [...]

We evaluated area-based approaches (ABAs) to light detection and ranging (lidar) predictions of plot- and stand-level forest attributes (tree count, height, basal area, volume, aboveground biomass, broadleaf/conifer, and diameter at breast height--'diameter'). ABA methods included post-stratification (PS), ordinary least squares (OLSs) regression, k nearest neighbors (kNN), and random forest (RF). This study was conducted on the Savannah River Site in South Carolina, USA. Plot- and stand-level predictions were validated against fixed-radius 0.04 ha (0.1 acre) plots in 49 [approximately equal to] 2.0 ha (5 acre) stands. Our findings demonstrate that lidar can be incorporated operationally into forest inventory systems to provide stand-level inferences for a wide range of forest attributes. Volume predictions for specific diameter classes, however, often fared poorly (root mean squared error (RMSE) > 100%) for the methods we explored, especially for larger (less common) diameter trees. Stand-level results were consistently better than pixel-level results (10-200+ percentage points). kNN and RF performed similarly and better than OLS and PS, but RF was the most robust to model configurations, while kNN has practical advantages such as simultaneous predictions of many attributes. Key words: lidar, forest inventory, stand-level inference, area-based approach, sampling, 1. Introduction Forest inventories provide information needed by land managers to assess forest conditions with respect to objectives such as sustainable yield, preservation, and wildlife habitat. Under ideal conditions, forest [...]

Estimating the volume of standing trees is a fundamental concern in forestry and is typically accomplished using one or more measurements of stem diameter along with formulae that assume geometric primitives. In contrast, technologies such as terrestrial Light Detection And Ranging (LiDAR) can record very detailed spatial information on the actual surface of an object, such as a tree bole. We present a method using LiDAR that provides accurate volume estimates of tree stems, as well as 2D rasters that display details of stem surfaces, which we call the 'unwrapping method.' This method combines the concepts of cylinder fitting, voxelization, and digital elevation models. The method is illustrated and tested using a sample of standing trees, whereby we are able to generate accurate volume estimates from the point cloud, as well as accurate visualization of the scanned stem sections. When compared to volume estimates derived from Huber's, Smalian's, and Newton's formulae, the differences are consistent with previous studies comparing formula-derived volume estimates and water-displacement- derived volume estimates, suggesting the unwrapping method has comparable accuracy to water displacement. Key words: volume estimation, stem volume, LiDAR, QSM, voxelization, Introduction A forest can provide many resources such as wood, fiber, and food, and ecosystem services like water supply and environment mitigation, but for landowners, financial return is one of [...]

Accurate growth and yield projection for plantations is critical for evaluating management decisions and anticipating future yields. Development of site index (SI) models is often costly and can be problematic when new, short-rotation species are introduced, for example, hybrid poplar plantations, which are increasingly common due to their very fast growth and high productivity. Airborne laser scanning (ALS) allows accurate measurement of tree and stand height and is increasingly being used to develop top height models. In this paper, we demonstrate an approach to develop SI models from ALS data across hybrid poplar plantations in Quebec, Canada. We exploit a single time step ALS acquisition to generate top height estimates at 10 m grid level. Using existing information on planting date and management practices, we developed top height models for unique classes of fertilization treatment and clone. The generic models for unfertilized and fertilized stands showed good fit statistics, with [R.sup.2] of 0.71 and 0.82, respectively. Clone-specific models showed similar goodness of fit, with the best model resulting in an [R.sup.2] of 0.89 and relative root mean square error (RMSE) of 16.6%. Analysis of variance (ANOVA) results showed that clone, fertilization status, and the interaction term between clone and fertilization were significant. Our results confirm the development of top height models from a chronosequence of ALS data was successful and offers a new approach to derive SI models in single-species plantation sites. Key words: hybrid poplar, Populus, forest productivity, lidar, site index, plantations, fertilization Une projection precise de la croissance et du rendement des plantations est essentielle si on veut evaluer les decisions de gestion et anticiper les rendements futurs. Le developpement de modeles d'indice de site (IS) est souvent couteux et peut poser des problemes lorsque de nouvelles especes a rotation courte sont introduites, par exemple les plantations de peupliers hybrides, qui sont de plus en plus courantes en raison de leur croissance tres rapide et de leur productivite elevee. Le balayage laser aeroporte (Airborne laser scanning ou ALS) permet de mesurer avec precision la hauteur des arbres et est de plus en plus utilise pour developper des modeles de hauteur de cime. Dans cet article, nous presentons une approche permettant de developper des modeles d'IS a partir de donnees ALS dans des plantations de peupliers hybrides au Quebec, Canada. Nous utilisons une acquisition ALS unique pour generer des estimations de la hauteur de la cime a un niveau de grille de 10 m. En utilisant les informations existantes sur la date de plantation et les pratiques de gestion, nous avons developpe des modeles de hauteur de cime pour des classes uniques de traitement de fertilisation et de clone. Les modeles generiques pour les peuplements non fertilises et fertilises ont montre de bonnes statistiques de correspondance, avec un [R.sup.2] de 0,71 et 0,82, respectivement. Les modeles specifiques aux clones ont montre une qualite comparable de correspondance, le meilleur modele ayant donne un [R.sup.2] de 0,89 et un RMSE relatif de 16,6%. Les resultats de l'analyse de la variance (ANOVA) ont montre que le clone, le statut de fertilisation et le terme d'interaction entre le clone et la fertilisation etaient significatifs. Nos resultats confirment que le developpement de modeles de hauteur de cime a partir d'une chronosequence de donnees ALS a ete couronne de succes et offre une nouvelle approche pour deriver des modeles IS dans des sites de plantation a espece unique. [Traduit par la Redaction] Mots-cles: peuplier hybride, Populus, productivite forestiere, lidar, indice de site, plantations, fertilisation, 1. Introduction Accurate information on forest attributes is crucial for sustainable forest management. Collected during forest inventories, this information is used to assess current as well as projected forest conditions [...]

The variation in point cloud density is driven by many different factors. This variation is expected to affect the quality of the information extracted from the point clouds, however, the extent to which these variations impact the ability to accurately extract and assess geometric features of highways from point cloud data are unknown. This paper investigates the impacts of point density reduction on the extraction and assessment of four critical geometric features. The density of light detection and ranging (LiDAR) data was first reduced and the different features were extracted at varying levels of point density and on a selection of different highway segments in Alberta, Canada. The information obtained at lower point density was then compared to what was obtained at 100% point density. It was found that clearance assessments and sight distance assessments had low sensitivity to reductions in point density (i.e., reducing the point density to as low as 10% of the original data (30 [ppm.sup.2] on the pavement surface) yielded results comparable to what was obtained at 100% density (300 [ppm.sup.2]) In contrast, for cross section slope estimation and curve attribute estimation higher sensitivity to point density was observed. These findings are critical for transportation agencies considering the adoption of LiDAR technology to manage elements of their infrastructure and for researchers developing data processing tools and algorithms for the semantic segmentation of transportation features from remotely sensed point clouds. Key words: point cloud density, sensitivity, clearance assessment, sight distance assessment, cross-section slope estimation, horizontal curve attributes. La variation de la densite des nuages de points est attribuable a de nombreux facteurs. Cette variation devrait avoir une incidence sur la qualite de l'information extraite des nuages de points, mais on ignore dans quelle mesure ces variations influent sur la capacite d'extraire et d'evaluer avec precision les caracteristiques geometriques des routes a partir des donnees des nuages de points. Dans le cadre de cet article, on etudie les impacts de la reduction de la densite des points sur l'extraction et l'evaluation de quatre caracteristiques geometriques critiques. La densite des donnees de detection et de telemetrie par lumiere (LiDAR) a d'abord ete reduite et les differentes caracteristiques ont ete extraites a differents niveaux de densite de points et sur une selection de segments de routes en Alberta, Canada. L'information obtenue a une densite de points inferieure a ensuite ete comparee a celle obtenue a une densite de points de 100 %. On a constate que les evaluations du degagement et de la distance de visibilite etaient peu sensibles a la reduction de la densite des points, c.-a-d. reduire la densite des points a aussi peu que 10 % des donnees originales (30 [ppm.sup.2] sur la surface de la chaussee) a donne des resultats comparables a ceux obtenus a une densite de 100 % (300 [ppm.sup.2]). En revanche, pour l'estimation de la pente de section transversale et de l'attribut de courbe, on a observe une sensibilite plus elevee par rapport a la densite des points. Ces constatations sont essentielles pour les organismes de transport qui envisagent l'adoption de la technologie LiDAR pour gerer des elements de leur infrastructure et pour les chercheurs qui mettent au point des outils et des algorithmes de traitement des donnees pour la segmentation semantique des fonctions de transport a partir de nuages de points de teledetection. [Traduit par la Redaction] Mots-cles : densite de nuage de points, sensibilite, evaluation du degagement, evaluation de la distance de vision, estimation de la pente de section transversal, attributs de la courbe horizontale., 1. Introduction The past few years have seen growing interest in using laser technology for transportation infrastructure management. Transportation agencies are gradually realizing the potential value of using sensor data [...]

Very high point density laser scanning data from unmanned aerial systems (UAS) can be used to segment the trunks of individual trees. Such segmentation (e.g., individual trunk segmentation (ITS)) is useful, for example, in the estimation of diameter at breast height (DBH), which in turn is needed for the estimation of other tree and stand attributes, such as stem volume and diameter of the basal area median tree (DGM). In this paper, we assess the estimation of DBH directly from UAS LiDAR data in open and closed canopy conditions that represent the range of operational conditions encountered in boreal forests in Finland. We also compare trunk-based DBH estimates to corresponding estimates from individual tree crown segmentation (ITC) and fuse the results from the trunk- and crown-based estimates. The results showed that trunk segmentation performed slightly better than ITC in open canopy areas, whereas ITC performed better in closed canopy areas. The DBH prediction error was smaller for ITS (3.0 cm) than ITC (3.9 cm) when considering the trees that were recognized by both methods. We also conclude that a hybrid method, where both segmented tree trunks and tree crowns are fused, considerably increases the number of correctly segmented trees but does not decrease the prediction error associated with DGM compared to using either ITC or ITS individually. Key words: forest inventory, laser scanning, drone, segmentation. Les donnees obtenues par balayage par faisceau laser avec densite de points tres elevee par les systemes d'aeronefs sans pilote (UAS) peuvent etre utilisees pour segmenter les troncs des arbres individuels. Une telle segmentation (p. ex., segmentation d'un tronc individuel (STI)) est utile, par exemple, dans l'estimation du diametre a hauteur d'homme (DHH), laquelle est par ailleurs requise pour l'estimation des attributs des autres arbres et du peuplement forestier, par exemple le volume du tronc et la surface terriere moyenne des arbres (DGM). Dans la presente communication, nous evaluons l'estimation du DHH a partir directement des donnees de l'UAS LiDAR dans des conditions de couvert ouvert et ferme qui representent l'eventail des conditions operationnelles rencontrees dans les forets boreales en Finlande. Nous comparons egalement les estimes du DHH fonde sur les troncs aux estimes correspondants de la segmentation de la couronne des arbres individuels (CAI) et nous fusionnons les resultats des estimes fondes sur les troncs et les couronnes. Les resultats ont indique que la segmentation des troncs fonctionnait un peu mieux que la CAI dans les secteurs de couverts ouverts alors que la CAI fonctionnait mieux dans des secteurs de couverts fermes. L'erreur de prevision du DHH etait plus petite pour la STI (3,0 cm) que la CAI (3,9 cm) lorsqu'on tenait compte des arbres qui etaient reconnus par les deux methodes. Nous concluons egalement qu'une methode hybride oU les troncs et les couronnes des arbres segmentes sont fusionnes accroit considerablement le nombre d'arbres correctement segmentes, mais ne diminue par l'erreur de prevision associee a la DGM comparativement a l'utilisation de la CAI ou de la STI individuellement. [Traduit par la Redaction] Mots-cles : inventaire forestier, balayage par faisceau laser, drone, segmentation., 1. Introduction The rapid development of unmanned aerial systems (UAS) and the miniaturization of optical sensors and light detection and ranging (LiDAR) instruments has been followed by an extensive research [...]

Most forest growth studies using airborne laser scanning (ALS) consider how the changes in forest attributes are observed in repeated ALS data acquisitions, but the prediction of future forest growth from ALS data is still a rarely discussed topic. This study examined the prediction of the periodic annual increment (PAI) of the width of tree rings over a period of 10 years. The requirement for this approach is that ALS data are acquired at the beginning of the growth period. This is followed by field measurements of growth by drilling after a given growth period. The PAI was modelled in terms of ALS metrics by using the principle of the area-based approach. The metrics related to intensity were particularly significant as predictors, whereas the effective leaf area index was not. The root-mean-square error (RMSE) of the predictions was slightly over 21%. Additional field information (soil type, management operations) improved the RMSE by 2.7 percentage units. Key words: area-based approach, diameter increment, forest growth, leaf area index, LiDAR. La plupart des etudes sur l'accroissement forestier utilisant le balayage par laser aeroporte (BLA) tiennent compte de la facon dont les changements des attributs forestiers sont observes dans les acquisitions de donnees repetees du BLA, mais la prevision d'un accroissement forestier futur a partir des donnees du BLA est un theme qui fait encore rarement l'objet de discussions. La presente etude a examine la prevision de l'augmentation annuelle periodique (AAP) de la largeur des anneaux de croissance des arbres sur une periode de 10 ans. La necessite d'adopter cette approche est que les donnees du BLA sont acquises au debut de la periode de croissance. Ceci est suivi de mesures de la croissance sur le terrain en faisant un forage apres une periode donnee de croissance. L'AAP a ete modelee en fonction des mesures du BLA en utilisant le principe de l'approche locale. Les mesures reliees a l'intensite etaient particulierement importantes comme variable explicative alors que l'indice foliaire reel ne l'etait pas. L'EMQ des previsions etait legerement au-dessus de 21%. Des renseignements additionnels sur le terrain (type de sol, operations de gestion) ont ameliore l'EMQ de 2,7 unites de pourcentage. [Traduit par la Redaction] Mots-cles : approche locale, augmentation du diametre, accroissement forestier, indice foliaire, LiDAR., Introduction Data acquired through airborne laser scanning (ALS) are widely used in forestry, with the forest inventories being one of the main applications of this ALS data. The main interest [...]

Accurate measurement of forest growing stock is a prerequisite for implementing climate-smart forestry strategies. This study deals with the use of airborne laser scanning data to assess carbon stock at the tree level. It aims to demonstrate that the combined use of two unsupervised techniques will improve the accuracy of estimation supporting sustainable forest management. Based on the heterogeneity of tree height and point cloud density, we classified 31 forest stands into four complexity categories. The point cloud of each stand was further divided into three horizontal layers, improving the accuracy of tree detection at tree level for which we calculated volume and carbon stock. The average accuracy of tree detection was 0.48. The accuracy was higher for forest stands with lower tree density and higher frequency of large trees, as well as a dense point cloud (0.65). The prediction of carbon stock was higher with a bias ranging from -0.3% to 1.5% and a root mean square error ranging from 0.14% to 1.48%. Key words: tree detection, airborne laser scanning (ALS), forest structure, carbon stock, climate-smart forestry. La mesure precise du capital forestier en croissance est un prerequis a l'implantation de strategies forestieres intelligentes face au climat. Cette etude porte sur l'utilisation de donnees de balayage laser aeroporte pour evaluer le stock de carbone a l'echelle de l'arbre. Elle vise a demontrer que l'utilisation combinee de deux techniques non supervisees va ameliorer la precision de l'estimation sur laquelle s'appuie un amenagement forestier durable. Sur la base de l'heterogeneite de la hauteur des arbres et de la densite du nuage de points, nous avons classe 31 peuplements forestiers dans quatre categories de complexite. Le nuage de points de chaque peuplement a par la suite ete divise en trois couches horizontales, ce qui ameliore la precision de la detection de chacun des arbres pour lesquels nous avons calcule le volume et le stock de carbone. La precision moyenne de la detection des arbres etait de 0,48. La precision etait plus elevee pour les peuplements forestiers qui avaient une plus faible densite et une frequence plus elevee de gros arbres, ainsi qu'un nuage de points dense (0,65). La prediction du stock de carbone etait plus elevee avec un biais allant de -0,3 a 1,5 % et un ecart moyen quadratique (EMQ) entre 0,14 et 1,48 %. [Traduit par la Redaction] Mots-cles: detection des arbres, balayage laser aeroporte (BLA), structure de la foret, stock de carbone, foresterie intelligente face au climat., 1. Introduction In Europe, forests cover about 35% of the total land area (SoEF 2020) and play a significant role in climate change mitigation thanks to their capacity to remove [...]

The value of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), which is the predominant commercial species in the Pacific Northwest, depends on tree verticality; trees with the same dimensions can differ substantially in value due to lean. The objective of this study was to assess the impact of tree leaning on estimation of stem dimensions using high-density terrestrial mobile lidar point clouds. We estimated lean with two metrics: the horizontal distance between stem centers at 1.3 m and 18 m, and the mean of seven successive lean angles along the tree bole (at 1, 3, 5, 7,10,12, and 15 m). For modeling, we used four existing taper equations and three existing volume equations. For trees leaning >2[degrees], we enhanced the existing volume models by including lean as a predictor. Because lean estimates depend on the distribution and number of points describing the stem, we found that including the distance from scanner to tree improved the computed volume. When diameter at breast height was replaced with diameter at heights between 7 and 10 m, the volume models for leaning trees improved significantly, whereas the vertical trees had favorable results with heights between 5 and 15 m. Our study suggests that lean magnitude improves the estimation of stem volume when lean is >2[degrees]. Key words: lidar, lean, taper, volume, mobile laser scanning. La valeur du douglas de Menzies (Pseudotsuga menziesii (Mirb.) Franco), l'essence commerciale dominante dans le Nord-Ouest du Pacifique, depend de la verticalite de la tige; des arbres de meme dimension peuvent avoir une valeur substantiellement differente a cause de l'inclinaison de la tige. L'objectif de cette etude consistait a evaluer l'impact de l'inclinaison des arbres sur l'estimation des dimensions de la tige a l'aide d'un nuage de points haute densite obtenu avec un scanneur lidar mobile terrestre. Nous avons estime l'inclinaison a l'aide de deux mesures : la distance horizontale entre le centre de la tige a 1,3 et 18 m, et la moyenne de sept angles d'inclinaison successifs mesures le long du tronc (a 1, 3, 5, 7,10, 12 et 15 m). Pour la modelisation, nous avons utilise quatre equations existantes defilement et trois equations existantes de volume. Dans le cas des arbres avec une inclinaison superieure a deux degres, nous avons ameliore les modeles de volume existants en y incluant l'inclinaison en tant que predicteur. Parce que les estimations de l'inclinaison dependent de la distribution et du nombre de points qui decrivent la tige, nous avons trouve que le fait d'inclure la distance entre le scanneur et l'arbre ameliore le volume calcule. Les modeles de volume pour les arbres inclines etaient significativement ameliores lorsque le diametre a hauteur de poitrine etait remplace par le diametre a des hauteurs de 7 a 10 m, tandis que les arbres verticaux avaient des resultats favorables avec des hauteurs de 5 a 15 m. Notre etude indique que l'ampleur de l'inclinaison ameliore l'estimation du volume de la tige lorsque l'inclinaison est superieure a deux degres. [Traduit par la Redaction] Mots-cles : lidar, inclinaison, defilement, volume, scanneur lidar mobile., 1. Introduction The deviation from a vertical position of tree stems, known as lean, is of interest to the forestry community as it plays a key role in forest operations [...]

The exchange of mass, momentum, moisture, and energy over horizontally homogeneous, flat terrain is mostly driven by vertical turbulent mixing related to buoyancy or shear. In the boundary layer over [...]

In many countries, airborne laser scanning (ALS) inventories are implemented to produce predictions for stand-level forest attributes. Nevertheless, mature stands are usually field-visited prior to clear-cutting, so some measurements can be conducted on these stands to calibrate the ALS-based predictions. In this paper, we developed a seemingly unrelated multivariate mixed-effects model system that includes component models for basal area, merchantable volume, and sawlog volume for 225 [m.sup.2] cells. We used ALS data and accurately positioned cut-to-length harvester observations from clear-cut stands dominated by Norway spruce (Picea abies (L.) Karst.). Our aim was to study the effect of 1-10 local angle-gauge basal area measurements on the accuracy of predicted merchantable and sawlog volumes. A seemingly unrelated mixed-effect model system was fitted to estimate cross-model correlations in residuals and random effects, which were then utilized to predict all the random effects of the system for calibrated stand-level predictions. The 10 angle-gauge plots decreased the relative root mean square error (RMSE%) of the basal area and merchantable volume predictions from 16.8% to 10.5% and from 15.8% to 11.9%, respectively. Cross-model correlations of the stand effects of sawlog volume with the other responses were low; therefore, the initial RMSE% of ~22% was decreased only marginally by the calibration. Key words: LiDAR, area-based approach, quality, estimated best linear unbiased predictor, harvester data. Dans de nombreux pays, des inventaires par balayage laser aeroporte (BLA) sont mis en reuvre afin de produire des previsions pour les attributs forestiers a l'echelle des peuplements. Neanmoins, les peuplements matures sont generalement visites sur le terrain avant d'etre coupes a blanc, ainsi certaines mesures peuvent etre effectuees sur ces peuplements pour calibrer les previsions basees sur le BLA. Dans cet article, nous avons developpe un systeme de modeles a effets mixtes multivaries sans correlation apparente qui inclut des composantes pour la surface terriere, le volume marchand et le volume en bois de sciage, pour des cellules de 225 [m.sup.2]. Nous avons utilise les donnees de BLA et positionne avec precision les observations des abatteuses-tronconneuses dans des peuplements coupes a blanc domines par l'epicea commun (Picea abies (L.) Karst.). Notre objectif consistait a etudier l'effet de 1 a 10 mesures locales de la surface terriere avec une jauge d'angle sur la precision des volumes marchand et de bois d'reuvre prevus. Un systeme de modeles a effets mixtes sans correlation apparente a ete ajuste pour estimer les correlations croisees des modeles dans les residus et les effets aleatoires, qui ont ensuite ete utilises pour predire tous les effets aleatoires du systeme pour les previsions calibrees a l'echelle des peuplements. L'utilisation des donnees des 10 placettes a la jauge d'angle ont diminue l'erreur quadratique moyenne relative (EQM %) des previsions de la surface terriere et du volume marchand de respectivement 16,8 a 10,5% et de 15,8 a 11,9%. Les correlations croisees entre les modeles d'effets du peuplement pour le volume de bois de sciage et pour les autres reponses etaient faibles : l'EQM % initial d'environ 22% n'a par consequent ete diminue que marginalement par l'etalonnage. [Traduit par la Redaction] Mots-cles : balayage laser aeroporte, approche par surface, qualite, meilleur predicteur lineaire sans biais estime, donnees de l'abatteuse-tronconneuse., Introduction Many remote sensing technologies have proven to be useful in forest inventories around the world. In Norway, for example, almost all forest management inventories are based on airborne laser [...]

High-impact weather is often determined by physical processes in the planetary boundary layer (PBL). For example, the PBL temperature and moisture distributions determine to a large degree the occurrence of [...]

LiDAR-based localization has been widely used for the pose estimation of autonomous vehicles. Since the localization requires a sustainable map reflecting environment changes, a map update framework based on crowd-sourcing measurements has been researched. Unfortunately, a point cloud map occupies too large data size to transmit data in the uploading and downloading of the map update framework. To realize the LiDAR map update framework by reducing the data size, we proposed a novel map update framework using a Geodetic Normal Distribution (GND) map that compresses the point cloud to the normal distributions. The proposed GND map update framework comprises two parts: map change detection based on crowd-sourcing vehicles and map updating based on a map cloud server. GND map changes are detected based on an evidence theory considering geometric relationships between the GND map and crowd-sourcing measurements and uploaded to the map cloud server. Uploaded map changes reproduce representative map changes based on a similarity-based clustering, which are updated into the GND map. The proposed framework was evaluated in simulations and real environments on construction sites. As a result, although partial map changes occurred, the GND map was kept up-to-date through the proposed framework and the localization for autonomous driving was performed successfully., Author(s): Chansoo Kim [1]; Sungjin Cho [2]; Myoungho Sunwoo [3]; Paulo Resende [4]; Benazouz Bradaï [4]; Kichun Jo (corresponding author) [5] 1. Introduction Recently, map-matching localization has been globally researched [...]

Vehicle detection in Intelligent Transportation Systems (ITS) is a key factor ensuring road safety, as it is necessary for the monitoring of vehicle flow, illegal vehicle type detection, incident detection, and vehicle speed estimation. Despite the growing popularity in research, it remains a challenging problem that must be solved. Hardware-based solutions such as radars and LIDAR are been proposed but are too expensive to be maintained and produce little valuable information to human operators at traffic monitoring systems. Software based solutions using traditional algorithms such as Histogram of Gradients (HOG) and Gaussian Mixed Model (GMM) are computationally slow and not suitable for real-time traffic detection. Therefore, the paper will review and evaluate different vehicle detection methods. In addition, a method of utilizing Convolutional Neural Network (CNN) is used for the detection of vehicles from roadway camera outputs to apply video processing techniques and extract the desired information. Specifically, the paper utilized the YOLOv5s architecture coupled with k-means algorithm to perform anchor box optimization under different illumination levels. Results from the simulated and evaluated algorithm showed that the proposed model was able to achieve a mAP of 97.8 in the daytime dataset and 95.1 in the nighttime dataset., Author(s): Othman O. Khalifa [1,2]; Muhammad H. Wajdi [2]; Rashid A. Saeed [3]; Aisha H. A. Hashim [2]; Muhammed Z. Ahmed [4]; Elmustafa Sayed Ali (corresponding author) [5,6] 1. Introduction [...]

A problem in the single-scan setup of terrestrial laser scanning is that some trees are shaded by others and therefore not detected in the scan. A basic estimator for forest characteristics such as tree density or basal area is based on the visible area of a scanner. However, simply compensating for nondetection by the visible area may result in considerable bias even in Poisson forests, especially if the detection of a tree depends on its size. We propose a new estimator that is a generalization of the visible area based estimator. Most importantly, the new estimator allows different detection rules; for example, full or partial visibility of a tree can be required for detection. By a simulation study, it is shown to work adequately in different types of simulated and empirical forests with different detection rules.Key words: terrestrial laser scanning, nondetection, single scan, spatial point pattern, tree density estimator.Un probleme inherent a l'utilisation du balayage laser terrestre avec un seul balayage vient du fait que certains arbres sont caches par d'autres et ne sont donc pas detectes dans l'analyse. Un estimateur de base des caracteristiques de la foret, comme la densite ou la surface terriere, est base sur la zone visible d'un balayage. Cependant, une simple compensation de la non-detection par la zone visible peut entrainer un biais important meme dans les forets repondant a une distribution de Poisson, surtout si la detection d'un arbre depend de sa taille. Nous proposons un nouvel estimateur qui est une generalisation de l'estimateur base sur la zone visible. Plus important encore, le nouvel estimateur permet d'utiliser differentes regles de detection; par exemple, la visibilite requise pour la detection d'un arbre peut etre complete ou partielle. Une etude par simulation, a montre qu'il fonctionne correctement dans differents types de forets, simulees et empiriques, avec differentes regles de detection. [Traduit par la Redaction]Mots-cles : balayage laser terrestre, non detection, balayage unique, configuration spatiale de points, estimateur de la densite d'arbres., 1. IntroductionThere is great interest in replacing at least part of traditional field measurements in forest inventories by terrestrial laser scanning (TLS). As soon as the best practices with TLS [...]

Non-detection of trees is an important issue when using single-scan TLS in forest inventories. A hybrid inference approach is adopted. Quoting from distance sampling, a detection function is assumed, so that the inclusion probability of each tree included within each plot can be determined. A simulation study is performed to compare the TLS-based estimators corrected and uncorrected for non-detection with the Horvitz-Thompson estimator based on conventional plot sampling, in which all the trees within plots are recorded. Results show that single-scan TLS provides more efficient estimators with respect to those provided by the conventional plot sampling in the case of low-density forests when no distance sampling correction is performed. In low-density forests, uncorrected estimators lead to a small bias (1%-6%), increasing with plot size. Therefore, care must be taken in enlarging the plot radius too much. The bias increases in forests with clustered spatial structures and in dense forests, where the bias levels (30%-50%) deteriorate the performance of uncorrected estimators. Even if the bias- corrected estimators prove to be effective in reducing the bias (below 15%), these reductions are not sufficient to outperform conventional plot sampling. Therefore, there is no convenience in using TLS-based estimation in high-density forests.Key words: plot sampling, TLS-based detection, distance sampling, hybrid inference, simulation study.La non-detection des arbres est un probleme important lors de l'utilisation d'un scanner laser terrestre (SLT) a balayage unique pour les inventaires forestiers. Une approche d'inference hybride est adoptee. En se fondant sur l'echantillonnage a distance, on peut deduire une fonction de detection permettant de determiner la probabilite d'inclusion de chaque arbre present dans chaque placette. Une etude par simulation est realisee pour comparer les estimateurs fondes sur le SLT qui ont ete corriges ou non pour la non-detection a l'aide de l'estimateur de Horvitz-Thompson, lequel est base sur un echantillonnage conventionnel de placettes dans lequel tous les arbres des placettes sont enregistres. Les resultats montrent que le SLT a balayage unique produit des estimateurs plus efficaces que ceux provenant d'un echantillonnage conventionnel de placettes dans le cas des forets a faible densite lorsqu'aucune correction d'echantillonnage a distance n'est effectuee. Dans les forets a faible densite, les estimateurs non corriges entrainent un leger biais (de 1 a 6 %), qui augmente avec la taille de la placette. Par consequent, il faut prendre garde de ne pas trop agrandir le rayon de la placette. Le biais augmente dans les forets ayant une structure spatiale regroupee et dans les forets denses, pour lesquelles la taille du biais (de 30 a 50 %) deteriore la performance des estimateurs non corriges. Meme si la correction du biais des estimateurs s'avere efficace pour reduire le biais (inferieur a 15 %), ces reductions ne sont pas suffisantes pour surpasser l'echantillonnage conventionnel de placettes. Par consequent, il n'y a pas d'avantage a utiliser une estimation fondee sur le SLT dans les forets a forte densite. [Traduit par la Redaction]Mots-cles : echantillonnage de placettes, detection par scanner laser terrestre (SLT), echantillonnage a distance, inference hybride, etude par simulation., 1. IntroductionTerrestrial laser scanning (TLS) has been demonstrated to be a promising tool for plot-level field inventories (e.g., Liang et al. 2011; Moskal and Zheng 2012; Kankare et al. 2015; [...]

Archaeology is transforming our view of how ancient Maya societies developed. Use of lidar technology has now led to the discovery that large, monumental structures that aid naked-eye astronomy were built unexpectedly early. Monumental structures that aid naked-eye astronomy., Author(s): Patricia A. McAnany Author Affiliations: Large-scale early Maya sites in Mexico revealed by lidar mapping technology In archaeology, there are few watershed moments, when a technological breakthrough changes everything. [...]

Understanding patterns of aboveground carbon storage across forest types is increasingly important as managers adapt to threats of global change. We combined field measures of aboveground biomass with lidar to model fine-scale biomass in deciduous forests located in two watersheds; one watershed was underlain by sandstone and the other by shale. We measured tree and shrub biomass across three topographic positions for both watersheds and analyzed biomass using mixed models. The watershed underlain by shale had 60% more aboveground biomass than the sandstone watershed. Although spatial patterns of biomass were different across watersheds, both had higher (between about 40% and 55%) biomass values at the toe-slope position than at the ridge-top position. To model fine-scale spatial patterns of biomass, we tested the effectiveness of leaf-on and leaf-off lidar combined with topographic metrics to develop a spatially explicit random forest model of tree and shrub biomass across both watersheds. Leaf-on variables were more important for modeling shrub biomass, while leaf-off variables were more effective at modeling tree biomass. Our model of tree and shrub biomass reflects the distribution of biomass across both watersheds at a fine scale and highlights the potential of abiotic factors such as topography and bedrock to affect carbon storage. Key words: biomass, lidar, understory, carbon, shrub. Il est de plus en plus important de comprendre les profils de sequestration aerienne du carbone qui caracterisent les differents types de foret alors que les gestionnaires s'adaptent aux menaces associees au changement global. Nous avons combine des mesures de biomasse aerienne prises sur le terrain avec le lidar pour modeliser la biomasse a petite echelle dans des forets decidues situees dans deux bassins versants : un des bassins reposait sur du gres et l'autre sur du schiste. Nous avons mesure la biomasse des arbres et des arbustes dans trois positions topographiques dans les deux bassins et analyse la biomasse a l'aide de modeles mixtes. Le bassin qui reposait sur le schiste avait 60 % plus de biomasse aerienne que le bassin reposant sur le gres. Bien que la repartition spatiale de leur biomasse differe, les deux bassins avaient des valeurs de biomasse plus elevees (environ 40 a 55 %) au pied qu'au sommet de la pente. Pour modeliser la repartition spatiale a petite echelle, nous avons teste l'efficacite du lidar en presence et en l'absence du feuillage jumele a des donnees topographiques pour elaborer un modele forestier aleatoire, spatialement explicite, de la biomasse arborescente et arbustive dans les deux bassins. Les variables mesurees en presence du feuillage etaient plus importantes pour modeliser la biomasse des arbustes tandis que les variables mesurees en l'absence du feuillage etaient plus efficaces pour modeliser la biomasse des arbres. Notre modele de biomasse arbustive et arborescente reflete la repartition de la biomasse a petite echelle dans les deux bassins et met en evidence l'influence potentielle des facteurs abiotiques, tels que la topographie et le substrat rocheux, sur la sequestration du carbone. [Traduit par la Redaction] Mots-cles : biomasse, lidar, sous-bois, carbone, arbuste., 1. Introduction Understanding patterns of carbon storage across forest types is increasingly important as managers adapt to the diverse threats of global change. Forests are vital components of the global [...]

The use of LiDAR in the study of gap regimes has seen significant progress in recent years. Researchers have assumed that canopy gaps detected in this manner are ecologically equivalent to gaps sampled in situ by more traditional methods. However, those latter methods usually include canopy gaps only and ignore non-regenerating openings that are produced by causes limiting tree establishment. We developed a predictive model capable of discriminating between canopy gaps and non-regenerating openings using LiDAR-derived data. Selected predictive variables were related to conditions that limit tree establishment such as zones of moisture accumulation and steep slopes or to the resulting vegetation physiognomy. The model was applied to three old-growth maple forests to predict the fractions of canopy openings belonging to these two types. On average, non-regenerating openings represented 19.5% of the total area detected as canopy openings and occupied 1.37% of the sites. Canopy gaps formed 80.5% of the total area in canopy openings and covered 5.71% of sites that were studied. The non-regenerating opening seemed more frequent on thin surficial deposits. The canopy gap fraction was similar among study sites but had lower values than usually reported for temperate deciduous forest. Key words: gap regime, gap fraction, canopy gap detection, microtopography, disturbance regime. L'utilisation du LiDAR pour l'etude des regimes des trouees a connu une importante progression au cours des dernieres annees. Generalement, les chercheurs ont adopte de facon implicite la premisse que les trouees detectees ainsi etaient equivalentes, sur la base de leur ecologie, aux trouees echantillonnees in situ selon des methodes plus traditionnelles. Cependant, ces dernieres tiennent seulement compte des trouees dans la canopee creees par la mort d'au moins un arbre mais non des ouvertures edaphiques qui sont produites par certaines conditions de station empechant l'etablissement et la croissance d'arbres. Nous avons produit un modele predictif capable de distinguer les ouvertures edaphiques des trouees biotiques a partir de variables derivees de donnees LiDAR. Les variables predictives retenues dans le modele sont liees a des conditions de station qui limitent l'etablissement d'arbres, comme les zones d'accumulation d'eau et les pentes fortes, ou sont liees a la physionomie de la vegetation qui en resulte. Le modele a ete applique a trois erablieres anciennes situees dans le sud-ouest du Quebec. Les ouvertures edaphiques y representent 19,5% de la surface totale des trouees et occupent 1,37% de la surface des stations en moyenne. Les trouees biotiques forment 80,5% de la surface totale detectee en trouees et couvrent 5,71% des stations etudiees. La proportion des stations en ouvertures edaphiques semble influencee par le type de depot de surface. La proportion en trouees biotiques est similaire entre les stations etudiees, mais est plus faible que les valeurs habituellement rapportees pour la foret feuillue temperee. Mots-cles: regime des trouees, proportion en trouees, detection des trouees, microtopographie, regime de perturbations., Introduction The natural disturbance regime in the temperate deciduous forest is characterized by gap dynamics, which is the process of continuous creation and subsequent filling of canopy gaps or developmental [...]