33 research outputs found

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

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    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

    Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest

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    Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this paper, we proposed an approach that includes a machine learning-based refinement step to reduce the number of falsely detected treetops. The approach involves the local maxima filtering and segmentation of the canopy height model to extract different segment-level features used for the classification of treetop candidates. The study was conducted in a mixed temperate forest, predominantly deciduous, with a complex topography and an area size of 0.6 km × 4 km. The classification model’s training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting, Artificial Neural Network, the Support Vector Machine, and Logistic Regression. The final classification model with optimal hyperparameters was adopted based on the best-performing classifier (RF). The overall accuracy (OA) and kappa coefficient (κ) obtained from the ten-fold cross validation for the training data were 90.4% and 0.808, respectively. The prediction of the test data resulted in an OA = 89.0% and a κ = 0.757. This indicates that the proposed method could be an adequate solution for the reduction of falsely detected treetops before tree crown segmentation, especially in deciduous forests

    Uso combinado de técnicas de teledetección y modelización para evaluar la distribución vertical de la vegetación

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    El objetivo de esta tesis es diseñar metodologías que identifiquen la distribución vertical de la vegetación independientemente de los ecosistemas que componen la zona de estudio. Esta tesis, al enmarcarse en un doctorado industrial, incorpora el objetivo de crear desarrollos metodológicos que impulsen la competitividad de la empresa en el mercado laboral. Con este objetivo último, la tesis ha implementado tres metodologías ágiles, precisas y aplicables a gran escala, cuya finalidad ha sido identificar la estructura tridimensional de la vegetación que condiciona las coberturas del suelo presentes y, por tanto, la gestión aplicable en cada caso. Esta tesis incorpora tres capítulos, independientes entre sí, cada uno con un reto metodológico. El Capítulo 3 buscó detectar árboles individuales de plantaciones jóvenes de Pinus pinaster y Pinus radiata en parcelas permanentes destinadas a investigación, delinear sus copas y evaluar su altura. El Capítulo 4 incluye una metodología capaz de identificar las coberturas de suelo presentes en una interfaz urbano-forestal, las cuales están relacionadas directamente con la distribución vertical de la vegetación. El Capítulo 5 incluye el desarrollo de una metodología para identificar un umbral capaz de adaptarse a la estructura real de cada parcela. Este umbral sería una alternativa generalizable a todo un monte y replicable sin necesidad de nuevas mediciones en campo. Paralelamente a estos trabajos se ha participado en dos registros de la propiedad intelectual, easyLaz® y easySat®, cuya finalidad es el procesado de información LiDAR y multiespectral respectivamente

    Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD)

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    Obtaining low vegetation data is important in order to quantify the structural characteristics of a forest. Dense three-dimensional (3D) laser scanning data can provide information on the vertical profile of a forest. However, most studies have focused on the dominant and subdominant layers of the forest, while few studies have tried to delineate the low vegetation. To address this issue, we propose a framework for individual tree crown (ITC) segmentation from laser data that focuses on both overstory and understory trees. The framework includes 1) a new algorithm (SSD) for 3D ITC segmentation of dominant trees, by detecting the symmetrical structure of the trees, and 2) removing points of dominant trees and mean shift clustering of the low vegetation. The framework was tested on a boreal forest in Sweden and the performance was compared 1) between plots with different stem density levels, vertical complexities, and tree species composition, and 2) using airborne laser scanning (ALS) data, terrestrial laser scanning (TLS) data, and merged ALS and TLS data (ALS + TLS data). The proposed framework achieved detection rates of 0.87 (ALS + TLS), 0.86 (TLS), and 0.76 (ALS) when validated with field inventory data (of trees with a diameter at breast height >= 4 cm). When validating the estimated number of understory trees by visual interpretation, the framework achieved 19%, 21%, and 39% root-mean-square error values with ALS + TLS, TLS, and ALS data, respectively. These results show that the SSD algorithm can successfully separate laser points of overstory and understory trees, ensuring the detection and segmentation of low vegetation in forest. The proposed framework can be used with both ALS and TLS data, and achieve ITC segmentation for forests with various structural attributes. The results also illustrate the potential of using ALS data to delineate low vegetation

    A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data

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    Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m^−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management

    Random forest machine learning technique for automatic vegetation detection and modelling in LiDAR data

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    Machine learning techniques have gained a distinguished position in the automatic processing of Light Detection and Ranging (LiDAR) data area. They represent the actual research topic in the remote sensing domain. Indeed, this paper presents one method of supervised machine learning, which is called Random Forest. This algorithm is discussed, and their primary applications in automatic vegetation extraction and modelling in the LiDAR data area are presented here

    Opportunities for the use of drone technology in forest ecosystems

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    Au cours de la dernière décennie, la technologie drone a suscité un grand intérêt et a été largement utilisée pour des applications civiles. Ainsi, les drones ont rapidement prouvé leurs efficiences dans les ressources naturelles, l’environnement, l’agriculture et la foresterie. Étant une plate-forme de télédétection, les drones ont le potentiel d'augmenter l'efficacité d'acquisition des données forestières en ayant des résolutions spatiale et temporelle beaucoup plus importantes que celles des autres techniques de télédétection. Dans cet article, nous présentons une synthèse des travaux de recherche portant sur l’utilisation de la technologie drone dans diverses applications forestières, dont la modélisation de la canopée forestière, l’évaluation des paramètres de l’inventaire forestier, le suivi de la santé des forêts et la discrimination des essences forestières. L’analyse de ces travaux a montré que l’utilisation de la technologie drone a concerné plusieurs aspects diversifiés, tandis que d’autres thématiques de recherche sont encore peu étudiées, notamment l’évaluation de la régénération naturelle, le suivi des projets de réhabilitation des écosystèmes naturels, l’étude des impacts des changements climatiques et des impacts anthropogènes sur un écosystème forestier. Le drone offre des opportunités d’utilisation de la technologie drone dans la gestion du domaine forestier Marocain, afin de remédier aux limites des techniques de télédétection utilisées actuellement en termes de résolution spatiale, de flexibilité du choix du temps d’acquisition des données et en terme du coût. Mots clés: Drone, Écosystème forestier, Inventaire forestier, Modélisation de la canopée forestière, Photogrammétrie, LidarOver the past decade, UAV technology has attracted a great deal of interest and has been widely used for civilian applications. UAVs have rapidly proven their efficiency in natural resources, the environment, agriculture and forestry. As a remote sensing platform, UAVs have the potential to increase the efficiency of forest data acquisition, having much higher spatial and temporal resolutions than other remote sensing techniques. In this paper, we present a synthesis of research on the use of UAV technology in various forestry applications, such as forest canopy modelling, forest inventory parameter assessment, forest health monitoring and forest species discrimination. The analysis of this research has shown that the use of UAV technology has concerned several diversified aspects, while other research themes are less studied, notably the assessment of natural regeneration, the monitoring of natural ecosystem rehabilitation projects, the study of climate and anthropogenic change impacts forest ecosystems. UAV technology is an opportunities for management of the Moroccan forest ecosystem, in order to overcome the limitations of the remote sensing techniques currently used, in terms of spatial resolution, flexibility in the choice of data acquisition time and in terms of cost. Keywords: UAV, Forest ecosystem, Forest inventory, Forest canopy modelling, Photogrammetry, Lida

    Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data

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    Multispectral airborne laser scanning (MS-ALS) provides information about 3D structure as well as the intensity of the reflected light and is a promising technique for acquiring forest information. Data from MS-ALS have been used for tree species classification and tree health evaluation. This paper investigates its potential for individual tree detection (ITD) when using intensity as an additional metric. To this end, rasters of height, point density, vegetation ratio, and intensity at three wavelengths were used for template matching to detect individual trees. Optimal combinations of metrics were identified for ITD in plots with different levels of canopy complexity. The F-scores for detection by template matching ranged from 0.94 to 0.73, depending on the choice of template derivation and raster generalization methods. Using intensity and point density as metrics instead of height increased the F-scores by up to 14% for the plots with the most understorey trees

    Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC)

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    Developing a robust algorithm for automatic individual tree crown (ITC) detection from airborne laser scanning datasets is important for tracking the responses of trees to anthropogenic change. Such approaches allow the size, growth and mortality of individual trees to be measured, enabling forest carbon stocks and dynamics to be tracked and understood. Many algorithms exist for structurally simple forests including coniferous forests and plantations. Finding a robust solution for structurally complex, species-rich tropical forests remains a challenge; existing segmentation algorithms often perform less well than simple area-based approaches when estimating plot-level biomass. Here we describe a Multi-Class Graph Cut (MCGC) approach to tree crown delineation. This uses local three-dimensional geometry and density information, alongside knowledge of crown allometries, to segment individual tree crowns from airborne LiDAR point clouds. Our approach robustly identifies trees in the top and intermediate layers of the canopy, but cannot recognise small trees. From these three-dimensional crowns, we are able to measure individual tree biomass. Comparing these estimates to those from permanent inventory plots, our algorithm is able to produce robust estimates of hectare-scale carbon density, demonstrating the power of ITC approaches in monitoring forests. The flexibility of our method to add additional dimensions of information, such as spectral reflectance, make this approach an obvious avenue for future development and extension to other sources of three-dimensional data, such as structure from motion datasets.Jonathan Williams holds a NERC studentship [NE/N008952/1] which is a CASE partnership with support from Royal Society for the Protection of Birds (RSPB). David Coomes was supported by an International Academic Fellowship from the Leverhulme Trust. Carola-Bibiane Schoenlieb was supported by the RISE projects CHiPS and NoMADS, the Cantab Capital Institute for the Mathematics of Information and the Alan Turing Institute. We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Quadro P6000 GPU used for this research
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