Bibliographie
77 références — synchronisée depuis la bibliothèque Zotero BRASNAH
Forest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile Laser Scanning
Forests
The application of static terrestrial laser scanning (TLS) in forest inventories is becoming more effective. Nevertheless, the occlusion effect is still limiting the processing efficiency to extract forest attributes. The use of a mobile laser scanner (MLS) would reduce this occlusion. In this study, we assessed and compared a hand-held mobile laser scanner (HMLS) with two TLS approaches (single scan: SS, and multi scan: MS) for the estimation of several forest parameters in a wide range of forest types and structures. We found that SS is competitive to extract the ground surface of forest plots, while MS gives the best result to describe the upper part of the canopy. The whole cross-section at 1.3 m height is scanned for 91% of the trees (DBH > 10 cm) with the HMLS leading to the best results for DBH estimates (bias of ´0.08 cm and RMSE of 1.11 cm), compared to no fully-scanned trees for SS and 42% fully-scanned trees for MS. Irregularities, such as bark roughness and non-circular cross-section may explain the negative bias encountered for all of the scanning approaches. The success of using MLS in forests will allow for 3D structure acquisition on a larger scale and in a time-efficient manner.
Octrees optimisés grâce au code de Morton
Le champignon, allié de l'arbre et de la forêt
CNPF
Le champignon évoque avant tout la tradition bien ancrée du ramassage des espèces comestibles qui attire chaque automne, sous bois, une population toujours plus nombreuse d amateurs en quête de bonne fortune. Au-delà de ces pratiques d ailleurs réglementées qui ne sont pas sans créer des conflits d usage, le champignon est bien plus qu un simple objet de convoitise : c est un être vivant aux particularités si originales qu il appartient désormais à une classification séparée du monde végétal : le règne fongique. Grégaire ou solitaire, tantôt en symbiose avec les arbres hôtes, tantôt tributaire de la matière organique contenue dans les litières forestières ou le bois mort, tantôt parasite de l arbre qu il contribue à fragiliser, le champignon représente un élément essentiel de l écosystème forestier. Sous les différentes formes et couleurs qu ils arborent lorsqu ils émergent, les champignons participent fortement à la richesse de la biodiversité sylvestre. Sans eux et les liaisons intimes qui les relient aux arbres, la forêt ne serait pas ce qu elle est. Ce fascicule l expose de manière simple sous une focale très ouverte. Mieux connaître l univers secret et souterrain des champignons, qui se révèle à nous uniquement quand ils fructifient, constitue un préalable qui aidera à les respecter, les protéger, les favoriser par une sylviculture appropriée, tout en tirant le meilleur parti de leurs contributions au bon fonctionnement de la forêt et de la valeur ajoutée d une récolte raisonnée
Diversité des espèces en forêt: pourquoi et comment l'intégrer dans la gestion ? se familiariser avec l'indice de biodiversité potentielle, IBP
CNPF-IDF
Les forêts sont composées d une grande diversité d organismes vivants. Les espèces de végétaux, d animaux, de champignons et d organismes unicellulaires s y comptent par milliers. Les interactions entre ces organismes sont extrêmement nombreuses et indispensables à l équilibre dynamique de la forêt, contribuant ainsi à sa productivité. La prise en compte de la diversité des espèces constitue une étape fondamentale vers une gestion forestière durable. Cette diversité, désormais mieux connue en forêt, dépend fortement de la composition en essences du peuplement, de la complexité de la forêt et de son ancienneté. Afin de faciliter la prise en compte de la diversité des espèces en forêt, un outil de diagnostic et d aide à la gestion a été créé : l Indice de Biodiversité Potentielle (IBP). Il s appuie sur les relations connues entre les caractéristiques de la forêt et les espèces qui y sont liées, au travers de dix facteurs clés. Pourquoi ces dix facteurs ont-ils été choisis dans l IBP ? De quelle manière influent-ils sur la biodiversité forestière ? Comment tenir compte des espèces forestières dans la gestion ? Ce document propose de répondre à ces questions à travers une présentation simplifiée des dix facteurs de l IBP. Pédagogique et richement illustré, intégrant les connaissances récentes sur la biodiversité, ce document pratique permet de découvrir l IBP et donne des éléments de réponse pragmatiques pour prendre en compte la biodiversité dans la gestion. Ce document est destiné aux propriétaires forestiers qui souhaitent connaître et intégrer la biodiversité dans leur gestion quotidienne. Plus largement, il apporte des éléments de réponse à tous ceux qui s interrogent sur la biodiversité en milieu forestier, sans être spécialistes de ces espaces : agriculteurs, utilisateurs occasionnels de la forêt
Estimer le volume total d'un arbre, quelles que soient l'essence, la taille, la sylviculture, la station
LASzip
Photogrammetric Engineering & Remote Sensing
Airborne laser scanning technology (LiDAR) makes it easy to collect large amounts of point data that sample the elevation of the terrain beneath. The LAS format has become the de facto standard for storing and distributing the acquired points. As the sampling density of LiDAR increases so does the size of the resulting files. Typical LAS files contain tens to hundreds of millions points today, but soon billions will be commonplace.
Mathematics for 3D game programming and computer graphics
Course Technology, Cengage Learning
FOREST SPECIES CLASSIFICATION AND TREE CROWN DELINEATION USING QUICKBIRD IMAGERY
Efficient forest management requires detailed knowledge of forest stands, including species information and individual tree parameters. Remote sensing data are increasingly being used to investigate forest classification at both coarse and fine levels. In this paper, we first examined the capability of QuickBird multispectral imagery for species level forest classification using eCognition software and a rule-based classification with the assistance of ancillary topographic data. We then applied a local maximum filter and watershed segmentation algorithm to perform tree identification and tree crown delineation using the QuickBird panchromatic band. The QuickBird imagery used in the study was acquired over Heiberg Memorial Forest in Tully, New York on 9 August 2004.
OpenGIS® Implementation Standard for Geographic information - Simple feature access - Part 2: SQL option
LIDAR Applications in Forestry – An Overview
The objectives of this paper are to provide the interested reader with an overview of LIDAR applications in forestry and to summarize the current state-of-the-art. We discuss the history of LIDAR and early applications of LIDAR in forest mapping and inventory. As with most LIDAR applications, efforts to use LIDAR in a forestry context are new—1996 is an early citation. Our summary of LIDAR research focuses on, but is not limited to, two major conferences: the "International workshop on three-dimensional analysis of forestry structure and terrain using LIDAR technology” held at the Pacific Forestry Center in March, 2002 and the “ScandLaser scientific workshop on airborne laser scanning of forests" held in September, 2003 at the Swedish University of Agricultural Science. We also summarize current, on-going efforts as they were reported in a survey of LIDAR providers, researchers, and users conducted by the authors. Research in Europe, North America, and Australia is highlighted. An extensive bibliography including website citations is included.
La mesure des arbres et des peuplements forestiers
Presses agronomiques de Gembloux
Tarif de cubage à décroissance variable pour les arbres sur pied
Jean Chaudé
Cubage sur pied, selon la norme française B-53015 du 31 mai 1945
A comparison between TLS and UAS LiDAR to represent eucalypt crown fuel characteristics
ISPRS Journal of Photogrammetry and Remote Sensing
Advances in fire behaviour modelling provide a catalyst for the development of next generation fuel inputs. Fire simulations underpin risk and consequence mapping and inform decisions regarding ecological and social impacts of different fire regimes. Unoccupied Aerial Systems (UAS) carrying Light Detection and Ranging (LiDAR) sensors have been proposed as a source of structural information with potential for describing fine fuel properties. Whilst these systems have been shown to be capable of describing general vegetation distribution, the ability to distinguish between vegetation elements that contribute to fire spread and those that do not (such as large woody elements) is yet to be explored. This study evaluates the ability of UAS LiDAR point clouds to provide a description of crown fuel elements in eucalypt trees. This is achieved through comparison with dense Terrestrial Laser Scanning (TLS) that were manually attributed with a fuel description. Using the TLSeparation package TLS and UAS LiDAR point clouds achieved 84.6% and 81.1% overall accuracy respectively in the separation of crown fuel and wood in nine reference trees. When applying the same separation process across a 30 by 50 m plot consisting of approximately 75 trees, total canopy fuel volume was found to be strongly correlated between the TLS and UAS LiDAR point clouds (r: 0.96, RMSE: 1.53 m3). A lower canopy base height and greater distance between crown fuel regions within each crown supported visual inspection of the point clouds that TLS point clouds were able to represent the crown to a greater extent than UAS LiDAR point clouds. Despite these differences it is likely that a less complete representation of canopy fuel such as that generated from UAS LiDAR point clouds will suitably represent the crown and canopy fuel objects effectively for fire behaviour modelling purposes. The research presented in this manuscript highlights the potential of TLS and UAS LiDAR point clouds to provide repeatable, accurate 3D characterisation of canopy fuel properties.
Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data
Remote Sensing of Environment
The use of laser scanning acquired from the air, or ground, holds great potential for the assessment of forest structural attributes, beyond conventional forest inventory. The use of full-waveform airborne laser scanning (ALSFW) data allows for the extraction of detailed information in different vertical strata compared to discrete ALS (ALSD). Terrestrial laser scanning (TLS) can register lower vertical strata, such as understory vegetation, without issues of canopy occlusion, however is limited in its acquisition over large areas. In this study we examine the ability of ALSFW to characterize understory vegetation (i.e. maximum and mean height, cover, and volume), verified using TLS point clouds in a Mediterranean forest in Eastern Spain. We developed nine fullwaveform metrics to characterize understory vegetation attributes at two different scales (3.75 m square subplots and circular plots with a radius of 15 m); with, and without, application of a height filter to the data. Four understory vegetation attributes were estimated at plot level with high R2 values (mean height: R2 = 0.957, maximum height: R2 = 0.771, cover: R2 = 0.871, and volume: R2 = 0.951). The proportion of explained variance was slightly lower at 3.75 m side cells (mean height: R2 = 0.633, maximum height: R2 = 0.470, cover: R2 = 0.581, and volume R2 = 0.651). These results indicate that Mediterranean understory vegetation can be estimated and accurately mapped over large areas with ALSFW. The future use of these types of predictions includes the estimation of ladder fuels, which drive key fire behavior in these ecosystems.
Completing <span style="font-variant:small-caps;">3D</span> point clouds of individual trees using deep learning
Methods in Ecology and Evolution
Abstract In close‐range remote sensing data collected in a forest, occlusion often causes incomplete or sparse point cloud representations of individual trees, impeding accurate 3D reconstruction of tree architecture and estimation of tree height and volume. Recent developments in deep learning (DL) for 3D data have produced approaches for point cloud completion, which could potentially be applied to trees. We explored the potential of a DL approach to fill gaps in dense point clouds representing the main structures of deciduous trees by applying an existing transformer‐based completion model (PoinTr). Complete point clouds are required as training data, but even dense terrestrial laser scanning (TLS) data sets contain gaps caused by occlusion, making it nearly impossible to acquire such data. We therefore investigated the ability of point cloud completion models trained on a range of synthetic data sets to handle occlusion patterns in real‐world point clouds. Despite the limited data set, we successfully fine‐tuned a general pre‐trained completion model to fill gaps within 1 m 3 segments of tree point clouds. Fine‐tuning on synthetic tree data improved the model's ability to complete tree objects compared with training on diverse artificial objects. However, the quality of the predictions was influenced by the level of sophistication of the synthetic data. Our results demonstrate that incorporating even limited real‐world TLS data during training can considerably improve completion results but may introduce additional noise in the predictions. 3D point cloud completion with DL has the potential to improve and fill gaps in point clouds of individual trees, facilitating further steps in the processing and analysis of 3D forest data.
Opaque voxel-based tree models for virtual laser scanning in forestry applications
Remote Sensing of Environment
Virtual laser scanning (VLS), the simulation of laser scanning in a computer environment, is a useful tool for field campaign planning, acquisition optimisation, and development and sensitivity analyses of algorithms in various disciplines including forestry research. One key to meaningful VLS is a suitable 3D representation of the objects of interest. For VLS of forests, the way trees are constructed influences both the performance and the realism of the simulations. In this contribution, we analyse how well VLS can reproduce scans of individual trees in a forest. Specifically, we examine how different voxel sizes used to create a virtual forest affect point cloud metrics (e.g., height percentiles) and tree metrics (e.g., tree height and crown base height) derived from simulated point clouds. The level of detail in the voxelisation is dependent on the voxel size, which influences the number of voxel cells of the model. A smaller voxel size (i.e., more voxels) increases the computational cost of laser scanning simulations but allows for more detail in the object representation. We present a method that decouples voxel grid resolution from final voxel cube size by scaling voxels to smaller cubes, whose surface area is proportional to estimated normalised local plant area density. Voxel models are created from terrestrial laser scanning point clouds and then virtually scanned in one airborne and one UAV-borne simulation scenario. Using a comprehensive dataset of spatially overlapping terrestrial, UAV-borne and airborne laser scanning field data, we compare metrics derived from simulated point clouds and from real reference point clouds. Compared to voxel cubes of fixed size with the same base grid size, using scaled voxels greatly improves the agreement of simulated and real point cloud metrics and tree metrics. This can be largely attributed to reduced artificial occlusion effects. The scaled voxels better represent gaps in the canopy, allowing for higher and more realistic crown penetration. Similarly high accuracy in the derived metrics can be achieved using regular fixed-sized voxel models with notably finer resolution, e.g., 0.02 m. But this can pose a computational limitation for running simulations over large forest plots due to the ca. 50 times higher number of filled voxels. We conclude that opaque scaled voxel models enable realistic laser scanning simulations in forests and avoid the high computational cost of small fixed-sized voxels.
Artificial intelligence-based software (AID-FOREST) for tree detection: A new framework for fast and accurate forest inventorying using LiDAR point clouds
International Journal of Applied Earth Observation and Geoinformation
Forest inventories are essential to accurately estimate different dendrometric and forest stand parameters. However, classical forest inventories are time consuming, slow to conduct, sometimes inaccurate and costly. To address this problem, an efficient alternative approach has been sought and designed that will make this type of field work cheaper, faster, more accurate, and easier to complete. The implementation of this concept has required the development of a specifically designed software called “Artificial Intelligence for Digital Forest (AID-FOREST)”, which is able to process point clouds obtained via mobile terrestrial laser scanning (MTLS) and then, to provide an array of multiple useful and accurate dendrometric and forest stand parameters. Singular characteristics of this approach are: No data pre-processing is required either pre-treatment of forest stand; fully automatic process once launched; no limitations by the size of the point cloud file and fast computations.
Estimation of breast height diameter and trunk curvature with linear and single-photon LiDARs
Annals of Forest Science
Methods We measured 7 Scots pine trees (Pinus sylvestris) with commercial LiDAR (Zeb Horizon by GeoSLAM), prototype SPAD LiDAR, and manual devices. We compared manual measurements to the DBH and curvature values estimated based on LiDAR data. We also scanned a densely branched Picea abies to compare penetrability of the LiDARs and detectability of the obstructed trunk. Results The DBH values deviated 1–3 cm correlating to the specified accuracies of the employed devices, showing close to acceptable results. The curvature values deviated 1–6 cm implying distorted range measurements from the top part of the trunks and inaccurate manual measurement method, leaving space for improvement. The most important finding was that the SPAD LiDAR outperformed conventional LiDAR in detecting tree stem of the densely branched spruce. Conclusion These results represent preliminary but clear evidence that LiDAR technologies are already close to acceptable level in DBH measurements, but not yet satisfactory for curvature measurements. In addition, terrestrial SPAD LiDAR has a great potential to outperform conventional LiDARs in forest measurements of densely branched trees.