Insights into tree morphology and canopy space occupation under the influence of local neighbourhood interactions in mature temperate forests using laser scanning technology

Mounting evidence suggests that tree species richness promotes ecosystem functioning in forests. However, the mechanisms driving positive biodiversity ecosystem functioning relationships remain largely unclear. This also holds for the previously proposed key mechanisms of resource partitioning in canopy space. Until recently, surveying and hence the study of crown space was very time-consuming and the images low resolution. The application of high-resolution laser scanning, however, now enables a fast and precise recording of entire forests. This thesis presents how the abandonment of management strongly alters the individual tree structure from the wood distribution along the trunk to the crown, a tree species-rich neighbourhood can increase the wood volume and crown dimension of individual trees as well as the productivity of large-sized trees, mobile laser scanning in forests is suitable for the acquisition of high-quality point clouds and determination of relevant management parameters, and the direction and strength of the relationship between tree species richness and canopy occupation depends on the definition of both canopy and species richness. These results reinforce the influence of species richness on ecosystem functions in oldgrowth forests and underline the importance of laser scanning for forest ecology research. The findings of the comparative analyses further highlight the importance of underlying definitions for the results obtained

Urban Roadside Tree Inventory Using a Mobile Laser Scanning System

the road environment. Thus, effective methods are needed for the MLS data processing. The main goal of this thesis is to establish a feasible workflow by testing a series of methods to extract geometrical information of roadside trees from the MLS-acquired point clouds. The workflow developed in this study consists of three parts. The first part deals with ground point removal. As such, only off-ground points are used to extract trees. The second part handles tree detection by comparing four segmentation and clustering methods: the Euclidian distance clustering algorithm, the region growing segmentation method, the normalized cut (Ncut) method, and the supervoxel-based tree detection method. The third part focuses on automated extraction of tree geometric parameters such as tree height, DBH, crown spread, and horizontal slices features. Finally, classification of tree species was conducted using the k-Nearest Neighbour (k-NN) and the random forests (RF) algorithm. A total of four MLS datasets (three in Xiamen, China and one in Kingston, Ontario) acquired in iv 2013 and 2015, respectively, were used to test the developed method. The ground truthing data of DBH estimation were obtained through manual measurement of selected roadside trees after the two MLS missions in Xiamen in the fall 2015. The field surveyed DBH values of the 163 roadside trees were used to estimate the accuracy of the proposed tree extraction method. The 200 manually labeled trees with 8 different species were selected to examine accuracy of the proposed classification method. The results show that over 90% of the roadside trees were correctly detected, with an average error of about 5% in DBH estimation when compared to the field survey, and an overall accuracy of 78% for the classification of tree species

GENERATING GIS DATABASE OF STREET TREES USING MOBILE LIDAR DATA

Mobile LiDAR captures complete details of street trees located along roadway and it is most efficient tool for executing large scale projects for road side trees mapping. Information of road-side trees locations and their morphological characteristics are essential for road widening project planning, road safety and autonomous vehicles. We propose a method to generate GIS database of street trees using information of automatically extracted street trees using mobile LiDAR data. Proposed method first organizes mobile LiDAR input data in partially overlapping cylinders and then morphological characteristics of tree crown and trunk, respectively are used to detect trees falling within cylinders. Finally complete tree structure point cloud is recovered from cylinder by vertical cylinders fitting to crown and trunk separately. The proposed method is tested on point cloud data acquired by StreetMapper 360 of 57 m long roadway. Completeness and correctness of 88.24% and 93.75% respectively are obtained in test site

Vehicle localization by lidar point correlation improved by change detection

LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany

Innovative surveying methodologies through Handheld Terrestrial LIDAR Scanner technologies for forest resource assessment

Precision Forestry is an innovative sector that is currently of great importance for forest and spatial planning. It enables complex analyses of forest data to be carried out in a simple and economical way and facilitates collaboration between technicians, industry operators and stakeholders, thus ensuring transparency in forestry interventions (Corona et al., 2017). The principles of "Precision Forestry" are to use modern tools and technologies with the aim to obtain as much real information as possible, to improve decision-making, and to ensure the current objectives of forest management. Thanks to the rapid technological developments in remote sensing during the last few decades, there have been remarkable improvements in measurement accuracy, and consequentially improvements in the quality of technical elaborations supporting planning decisions. During this period, several scientific publications have demonstrated the potential of the LIDAR system for measuring and mapping forests, geology, and topography in large-scale forest areas. The LIDAR scans obtained from the TLS and HLS systems provide detailed information about the internal characteristics of tree canopys, making them an essential tool for studying stem allometry, volume, light environments, photosynthesis, and production models. In light of these considerations, this thesis aims to expand the current knowledge on the terrestrial LIDAR system applications for monitoring forest ecosystems and dynamics by providing insight on the feasibility and effectiveness of these systems for forest planning. In particular, this study fills a gap in the literature regarding practical examples of the use of innovative technologies in forestry. The main themes of this work are: A) The strengths and weaknesses of the mobile LIDAR system for a forest company; B) The applicability and versatility of the LIDAR HLS tool for sustainable forest management applications; C) Single tree analysis from HLS LIDAR data.   To investigate these themes, we analyzed six cases studies: 1) An investigation of the feasibility and efficiency of LIDAR HLS scanning for an accurate estimation of forest structural attributes by comparing scans using the LIDAR HLS survey method (Handheld Mobile Laser Scanner) to traditional instruments; 2) An examination of walking scan path density’s influence on single-tree attribute estimation by HMLS, taking into account the structural biodiversity of two forest ecosystems under examination, and an estimation of the cost-effectiveness of each type of laser survey based on the path scheme considered; 3) A study of how LIDAR HLS surveys can contribute to fire prevention interventions by providing a quantitative classification of fuels and a preliminary description of the structural and spatial development of the forest in question; 4) An application of a method for assessing and rating stem straightness in tree posture using LIDAR HLS surveys to quantify differences between stands of different log qualities; 5) The identification of features of a Mediterranean old-growth forest using LIDAR HLS surveys according to the criteria established in the literature; 6) The extrapolation of dimensional information for Ficus macrophylla subsp. columnaris to identify the monumental character of the tree by comparing the most appropriate LIDAR HLS point cloud processing methodologies and estimating the total volume of individual trees. In conclusion, the results of these cases studies are useful to determine new research aspects within the system in the forest environment by applying recently published analysis methodologies and indications of relevant terrestrial LIDAR methodologies

A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas

International audienceIn this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as " tree points " and " other points ". The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the " tree points ". This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D points among which 17.6% are labeled as " tree points ". The derived results clearly reveal a semantic classification of high accuracy (up to 90.77%) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h)

Building structural characterization using mobile terrestrial point cloud for flood risk anticipation

Compte tenu de la fréquence élevée et de l'impact majeur des inondations, les décideurs, les acteurs des municipalités et le ministère de la sécurité publique ont un besoin urgent de disposer d'outils permettant de prédire ou d'évaluer l'importance des inondations et leur impact sur la population. D'après les statistiques, le premier étage des bâtiments, ainsi que les ouvertures inférieures, sont plus susceptibles de subir des dommages lors d'une inondation. Ainsi, dans le cadre de l'évaluation de l'impact des inondations, il serait nécessaire d'identifier l'emplacement de l'ouverture la plus basse des bâtiments et surtout sa hauteur par rapport au sol. Le système de balayage laser mobile (MLS) monté sur un véhicule s'est avéré être l'une des sources les plus fiables pour caractériser les bâtiments. Il peut produire des millions de points géoréférencés en 3D avec un niveau de détail suffisant, grâce à son point de vue depuis la rue et sa proximité. De plus, l'augmentation du nombre de jeux de données, issues des MLS acquis dans les villes et les environnements ruraux, permet de développer des approches pour caractériser les maisons résidentielles à l'échelle provinciale. Plusieurs défis sont associés à l'extraction d'informations descriptives des façades de bâtiments à l'aide de données MLS. Ainsi, les occlusions devant une façade rendent impossible l'obtention de points 3D sur ces parties de la façade. Aussi, comme les fenêtres sont principalement constituées de verre, qui ne réfléchit pas les signaux laser, les points disponibles pour celles-ci sont généralement limités. De plus, les approches de détection exploitent la répétitivité et les positions symétriques des ouvertures sur la façade. Mais ces caractéristiques sont absentes pour des maisons rurales et résidentielles. Finalement, la variabilité de la densité de points dans les données MLS rend difficile le processus de détection lorsqu'on travaille à l'échelle d'une ville. Par conséquent, l'objectif principal de cette recherche est de concevoir et de développer une approche globale d'extraction efficace des ouvertures présentes sur une façade. La solution proposée se compose de trois phases: l'extraction des façades, la détection des ouvertures et l'identification des occlusions. La première phase utilise une approche de segmentation adaptative par croissance de régions pour extraire la boîte englobante 3D de la façade. La deuxième phase combine la détection de trous avec une technique de maillage pour extraire les boîtes englobantes 2D des ouvertures. La dernière phase, qui vise à discriminer les occlusions des ouvertures, est en cours d'achèvement. Des évaluations qualitatives et quantitatives ont été réalisées à l'aide d'un jeu de données réelles, fourni par Jakarto Cartographie 3D Inc., de la province de Québec, au Canada. Les statistiques ont révélé que l'approche proposée pouvait obtenir de bons taux de performance malgré la complexité du jeu de données, représentatif des données acquises en situation réelle. Les défis concernant l'auto-occlusion de certaines façades et la présence de grandes occlusions environnantes seront à étudier plus en profondeur afin d'obtenir des informations plus précises sur les ouvertures des façades.Given the high frequency and major impact of floods, decision-makers, stakeholders in municipalities and public security ministry are in the urgent need to have tools allowing to predict or assess the significance of flood events and their impact on the population. Based on statistics, the first floor of the buildings, as well as the lower openings, are more likely subject to potential damage during a flood event. Thus, in the context of flood impact assessment, it would be required identifying the location of the buildings' lowest opening and especially its height above the ground. The capacity to characterize building with a relevant level of detail depends on the data sources used for the modeling. Different sources of data have been employed to characterize buildings' façade and openings. Mobile Laser Scanning (MLS) system mounted on a vehicle has proved to be one of the most reliable sources in this domain. It can produce millions of 3D georeferenced points with sufficient level of detail of the building facades and its openings, due to its street-view and close-range distance. Moreover, the increase of MLS providers and acquisitions in towns and rural environments, makes it possible to develop approaches to characterize residential houses at a provincial scale. Although being effective, several challenges are associated with extracting descriptive information of building facades using MLS data. The presence of occlusion in front of a facade makes it impossible to obtain the 3D points of the covered parts of the facade. Given the fact that windows mostly consist of glass and laser signals could not be reflected from the glass, limited points are usually available for windows. While the repetitive pattern and symmetrical positions of the openings on the facade makes it easier for the detection system to extract them, this characteristic is missing on the facade on rural and residential houses. The inconsistency of the point density in MLS data make the detection process even harder when working at city scale. Accordingly, the main objective of this research is to design and develop a comprehensive approach that effectively extracts facade openings. In order to meet the research project objective, the proposed solution consists of three phases including facade extraction, opening detection, and occlusion recognition. The first phase employs an adaptive region growing segmentation approach to extract the 3D bounding box of the facade. The second phase combines a hole-based assumption with an XZ gridding technique to extract 2D bounding boxes of the openings. The last phase which recognizes holes related to the occlusion from the openings is currently being completed. Qualitative and quantitative evaluations were performed using a real-word dataset provided by Jakarto Cartographie 3D inc. of the Quebec Province, Canada. Statistics revealed that the proposed approach could obtain good performance rates despite the complexity of the dataset, representative of the data acquired in real situations. Challenges regarding facade's self-occlusion and the presence of large surrounding occlusions should be further investigated for obtaining more accurate opening information on the facade

Fotogrametría de rango cercano aplicada a la Ingeniería Agroforestal

Tesis por compendio de publicaciones[EN]Since the late twentieth century, Geotechnologies are being applied in different research lines in Agroforestry Engineering aimed at advancing in the modeling of biophysical parameters in order to improve the productivity. In this study, low-cost and close range photogrammetry has been used in different agroforestry scenarios to solve identified gaps in the results and improve procedures and technology hitherto practiced in this field. Photogrammetry offers the advantage of being a non-destructive and non-invasive technique, never changing physical properties of the studied element, providing rigor and completeness to the captured information. In this PhD dissertation, the following contributions are presented divided into three research papers: • A methodological proposal to acquire georeferenced multispectral data of high spatial resolution using a low-cost manned aerial platform, to monitor and sustainably manage extensive áreas of crops. The vicarious calibration is exposed as radiometric calibration method of the multispectral sensor embarked on a paraglider. Low-cost surfaces are performed as control coverages. • The development of a method able to determine crop productivity under field conditions, from the combination of close range photogrammetry and computer vision, providing a constant operational improvement and a proactive management in the crop monitoring. An innovate methodology in the sector is proposed, ensuring flexibility and simplicity in the data collection by non-invasive technologies, automation in processing and quality results with low associated cost. • A low cost, efficient and accurate methodology to obtain Digital Height Models of vegatal cover intended for forestry inventories by integrating public data from LiDAR into photogrammetric point clouds coming from low cost flights. This methodology includes the potentiality of LiDAR to register ground points in areas with high density of vegetation and the better spatial, radiometric and temporal resolution from photogrammetry for the top of vegetal covers.[ES]Desde finales del siglo XX se están aplicando Geotecnologías en diferentes líneas de investigación en Ingeniería Agroforestal orientadas a avanzar en la modelización de parámetros biofísicos con el propósito de mejorar la productividad. En este estudio se ha empleado fotogrametría de bajo coste y rango cercano en distintos escenarios agroforestales para solventar carencias detectadas en los resultados obtenidos y mejorar los procedimientos y la tecnología hasta ahora usados en este campo. La fotogrametría ofrece como ventaja el ser una técnica no invasiva y no destructiva, por lo que no altera en ningún momento las propiedades físicas del elemento estudiado, dotando de rigor y exhaustividad a la información capturada. En esta Tesis Doctoral se presentan las siguientes contribuciones, divididas en tres artículos de investigación: • Una propuesta metodológica de adquisición de datos multiespectrales georreferenciados de alta resolución espacial mediante una plataforma aérea tripulada de bajo coste, para monitorizar y gestionar sosteniblemente amplias extensiones de cultivos. Se expone la calibración vicaria como método de calibración radiométrico del sensor multiespectral embarcado en un paramotor empleando como coberturas de control superficies de bajo coste. • El desarrollo de un método capaz de determinar la productividad del cultivo en condiciones de campo, a partir de la combinación de fotogrametría de rango cercano y visión computacional, facilitando una mejora operativa constante así como una gestión proactiva en la monitorización del cultivo. Se propone una metodología totalmente novedosa en el sector, garantizando flexibilidad y sencillez en la toma de datos mediante tecnologías no invasivas, automatismo en el procesado, calidad en los resultados y un bajo coste asociado. • Una metodología de bajo coste, eficiente y precisa para la obtención de Modelos Digitales de Altura de Cubierta Vegetal destinados al inventario forestal mediante la integración de datos públicos procedentes del LiDAR en las nubes de puntos fotogramétricas obtenidas con un vuelo de bajo coste. Esta metodología engloba la potencialidad del LiDAR para registrar el terreno en zonas con alta densidad de vegetación y una mejor resolución espacial, radiométrica y temporal procedente de la fotogrametría para la parte superior de las cubiertas vegetales

Puiden paikannus ja lajitunnistaminen maalaserkeilausaineistosta

The requirements for more accurate and up-to-date spatial data increases constantly due to changes occurring in the environment. In addition, there is a technical and economical need to map trees, tree ages and sizes, as well in wide forest areas as park areas in cities by modern scanning techniques. The aim of this thesis was to investigate different positioning methods for terrestrial laser scanned trees. The second aim was to examine different techniques to identify the species of the positioned trees. Laser scans from two separate relatively small woodlands were acquired for the thesis. These scans were utilised for tree locating and species identification. Tree positioning was based on the cylinder fitting method performed for tree stems provided by the scans. The results achieved by the positioning were analyzed based on the comparison to the manually measured reference values. To identify the tree species, the tree intensities and structure parameters extracted from the point clouds were used. According to the study results, the classification of some tree species was relatively well succeeded. However, the identification of some other species did not succeed as expected. The best classification correctness of 80 percent was achieved using the combination of tree intensities and the structure parameters, as well as by the structure parameters only. Classification using the intensities only provided considerably more unreliable results. Instead of that, one tree species (spruce) identification succeeded perfectly in each case. However, tree positioning succeeded obviously well, so the tree locations deviated slightly from the reference values. This examination indicated that a reliable evaluation of the tree classification results did not fully succeed with the relatively small tree sample size used in this thesis. To obtain more reliable estimate of success rate for the results provided by terrestrial laser scanning data, a larger sample size may be required. Furthermore, the laser scans for this work were performed in autumn when there were no leaves in the trees. This, of course, affected the intensity-based tree classification. However, modern tree positioning and classification methods appear quite promising. The future use of these techniques require further development and examination work.Yhä tarkemman ja ajantasaisen paikkatiedon tarve kasvaa jatkuvasti ympäristössä tapahtuvien nopeiden muutosten myötä. Tämä näkyy myös teknistaloudellisena tarpeena kartoittaa puulajeja, niiden ikää ja kokoa mm. erilaisilla nykyajan keilausmenetelmillä, niin laajoilla metsäalueilla kuin kaupunkien puistoalueilla. Tämän työn tavoitteena oli tutkia maastolaserkeilattujen puiden erilaisia paikannusmenetelmiä. Toisena tavoitteena oli tarkastella paikannettujen puiden lajitunnistusmenetelmiä. Työn toteuttamiseksi suoritettiin laserkeilauksia kahdella erillisellä pienehköllä metsäalueella. Näitä keilausaineistoja käytettiin puiden paikantamiseen ja lajitunnistukseen. Paikannus perustui keilausten tuloksena saaduille puun rungoille tehtyyn sylinterisovitusmenetelmään. Laskennalla saatuja tuloksia analysoitiin vertaamalla niitä referenssiarvoihin, jotka saatiin pistepilvistä manuaalisesti mittaamalla. Lajitunnistuksessa käytettiin puista saatujen pistepilvien intensiteettejä ja rakenneparametreja. Suoritetun tarkastelun perusteella joidenkin puulajien tunnistaminen onnistui melko hyvin. Kaikkien puiden tunnistaminen ei kuitenkaan onnistunut odotetulla tavalla. Käyttäen pistepilvien intensiteettien ja pistepilvistä saatujen puiden rakenneparametrien yhdistelmää, sekä pelkkiä rakenneparametreja, tulkittiin parhaimmillaankin noin 80 prosenttia tuloksista oikein. Pelkkiä intensiteettejä käyttäen saatiin huomattavasti epäluotettavampi tulos. Sen sijaan yhden puulajin (kuusen) tunnistaminen onnistui kaikissa tapauksissa täydellisesti. Toisaalta, suoritetussa tarkastelussa puiden paikantaminen onnistui kokonaisuudessaan hyvin, sillä laskennalla puille saadut sijainnit poikkesivat referenssiarvoista kauttaaltaan varsin vähän. Tarkastelu osoitti, että maalaserkeilattujen puiden tunnistaminen tässä työssä käytetyllä suhteellisen pienellä otoskoolla ei täysin onnistunut. Tarkempi arvio maalaserkeilausaineistosta saatujen tulosten onnistumisprosentista olisi edellyttänyt suurempaa otoskokoa. Lisäksi puiden laserkeilaukset tehtiin syksyllä, jolloin puissa ei ollut lehtiä. Tämä tietenkin vaikutti puiden tunnistamiseen intensiteettien avulla. Nykyiset puiden tunnistusmenetelmät vaikuttavat kuitenkin kokonaisuudessaan varsin lupaavilta. Menetelmien hyödyntäminen edellyttää yhä tutkimus- ja kehitystyötä

Tree crown segmentation in three dimensions using density models derived from airborne laser scanning

This article describes algorithms to extract tree crowns using two-dimensional (2D) and three-dimensional (3D) segmentation. As a first step, a 2D-search detected the tallest trees but was unable to detect trees located below other trees. However, a 3D-search for local maxima of model fits could be used in a second step to detect trees also in lower canopy layers. We compared tree detection results from ALS carried out at 1450 m above ground level (high altitude) and tree detection results from ALS carried out at 150 m above ground level (low altitude). For validation, we used manual measurements of trees in ten large field plots, each with an 80 m diameter, in a hemiboreal forest in Sweden (lat. 58 degrees 28' N, long. 13 degrees 38' E). In order to measure the effect of using algorithms with different computational costs, we validated the tree detection from the 2D segmentation step and compared the results with the 2D segmentation followed by 3D segmentation of the ALS point cloud. When applying 2D segmentation only, the algorithm detected 87% of the trees measured in the field using high-altitude ALS data; the detection rate increased to 91% using low-altitude ALS data. However, when applying 3D segmentation as well, the algorithm detected 92% of the trees measured in the field using high-altitude ALS data; the detection rate increased to 99% using low-altitude ALS data. For all combinations of algorithms and data resolutions, undetected trees accounted for, on average, 0-5% of the total stem volume in the field plots. The 3D tree crown segmentation, which was using crown density models, made it possible to detect a large percentage of trees in multi-layered forests, compared with using only a 2D segmentation method