1,720,971 research outputs found
An Approach to Conifer Species Classification Based on Crown Structure Modeling in High Density Airborne LiDAR Data.
No full textThe knowledge about the species of trees is essential for precision forest management practices. Modern high density airborne Light Detection and Ranging (LiDAR) systems have the ability to acquire large number of LiDAR points, allowing a very detailed characterization of the forest at the individual tree level. In this context, it is possible to use LiDAR data for accurate classification of the tree species. In this paper, we consider the specific problem of species classification of trees belonging to the conifer class. This is particularly challenging when only the external geometric information is considered. To address the problem we propose a novel approach that model the internal crown structure of the conifers. The internal structure is identified by using 3D region growing and Principal Component Analysis (PCA) and is used for defining a set of novel Internal Crown Geometric features (IGFs). Some state-of-the-art External Crown Geometric Features (EGFs) were also used to improve the classification accuracy. Sparse Support Vector Machines (SSVM) was used for classification and to quantify the feature relevances
An approach to conifer stem localization and modeling in high density airborne LiDAR data
No full textIndividual tree level inventory performed using high density multi-return airborne Light Detection and Ranging (LiDAR) systems provides both internal and external geometric details on individual tree crowns. Among them, the parameters such as, the stem location, and Diameter at Breast Height of the stem (DBH) are very relevant for accurate biomass, and forest growth estimation. However, methods that can accurately estimate these parameters along the vertical canopy are lacking in the state of the art. Thus, we propose a method to locate and model the stem by analyzing the empty volume that appears within the 3D high density LiDAR point cloud of a conifer, due to the stem. In a high LiDAR density data, the points most proximal to the stem location in the upper half of the crown are very likely due to laser reflections from the stem and/or the branch-stem junctions. By locating accurately these points, we can define the lattice of points representing branch-stem junctions and use it to model the empty volume associated to the stem location. We identify these points by using a state-of-the-art internal crown structure modelling technique that models individual conifer branches in a high density LiDAR data. Under the assumption that conifer stem can be closely modelled using a cone shape, we regression fit a geometric shape onto the lattice of branch-stem junction points. The parameters of the geometric shape are used to accurately estimate the diameter at breast height, and height of the tree. The experiments were performed on a set of hundred conifers consisting of trees from six dominant European conifer species, for which the height and the DBH were known. The results prove the method to be accurate
An Effective Approach to 3D Stem Modeling and Branch-Knot Localization in Multiscan TLS Data
No full textAccurate three dimensional (3D) stem modeling is critical to individual tree analysis. Multi-scan Terrestrial Laser Scanning (TLS) systems accurately map fine 3D structural details of tree components including the stem, branches and leaves. State-of-the-art (SoA) methods to model stem are affected by problems such as occlusion and point density variation around the stem. Thus, we propose a voxel based approach to derive accurate 3D model of stem by exploiting the opacity-driven (to lasers) void volume formed within the stem. For each height slice, peaks detected in the external boundary of the point-density-map object generated jointly from points derived from the stem model and the proximal TLS data points correspond to branch-knots. All experiments were conducted on a set of 10 manually delineated trees belonging to pine and spruce. The preliminary results prove the effectiveness of the method to accurately model the 3D stem and localize branch-knots
An Internal Crown Geometric Model for Conifer Species Classification With High-Density LiDAR Data
No full textThe knowledge of the tree species is a crucial information that governs the success of precision forest management practice. High-density small footprint multireturn airborne light detection and ranging (LiDAR) scanning can collect a huge amount of point samples containing structural details of the forest vertical profile, which can reveal important structural information of the forest components. LiDAR data have been successfully used to distinguish between coniferous and deciduous/broadleaved tree species. However, species classification within a class (e.g., the conifer class) using LiDAR data is a challenging problem when considering the tree external crown characteristics only. This paper presents a novel method for conifer species classification based on the use of geometric features describing both the internal and external structures of the crown. The internal crown geometric features (IGFs) are defined based on a novel internal branch structure model, which uses 3-D region growing and principal component analysis to delineate the branch structure of a conifer tree accurately. IGFs are used together with external crown geometric features to perform conifer species classification. Three different support vector machines have been considered for classification performance evaluation. The experimental analysis conducted on high-density LiDAR data acquired over a portion of the Trentino region in Italy proves the effectiveness of the proposed metho
A Simple Approach to Clustering in Excel
No full textData clustering refers to the method of grouping data into different groups depending on their characteristics. This grouping brings an order in the data and hence further processing on this data is made easier. This paper explains the clustering process using the simplest of clustering algorithms - the K-Means. The novelty of the paper comes from the fact that it shows a way to perform clustering in Microsoft Excel 2007 without using macros, through the innovative use of what-if analysis. The paper also shows that, image processing operations can be done in excel and all operations except displaying an image do not require a macro. The paper gives a solution to the problem of reading an image in excel by introducing a user defined add-in. The paper also has explained and implemented image segmentation as an application of clustering. This paper aims at showing that Microsoft Excel is a great tool as far as technical learning is concerned for the fact that, it can implement almost all algorithms and processes, and is very successful in providing the first hand exposure to an novice student
A Novel Approach to Internal Crown Characterization for Coniferous Tree Species Classification
No full textThe knowledge about individual trees in forest is highly beneficial in forest management. High density small foot- print multi-return airborne Light Detection and Ranging (LiDAR) data can provide a very accurate information about the structural properties of individual trees in forests. Every tree species has a unique set of crown structural characteristics that can be used for tree species classification. In this paper, we use both the internal and external crown structural information of a conifer tree crown, derived from a high density small foot-print multi-return LiDAR data acquisition for species classification. Considering the fact that branches are the major building blocks of a conifer tree crown, we obtain the internal crown structural information using a branch level analysis. The structure of each conifer branch is represented using clusters in the LiDAR point cloud. We propose the joint use of the k-means clustering and geometric shape fitting, on the LiDAR data projected onto a novel 3-dimensional space, to identify branch clusters. After mapping the identified clusters back to the original space, six internal geometric features are estimated using a branch-level analysis. The external crown characteristics are modeled by using six least correlated features based on cone fitting and convex hull. Species classification is performed using a sparse Support Vector Machines (sparse SVM) classifier. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only
Spectrum Sensing Implementations for Software Defined Radio in Simulink
No full textAbstractThe lack of spectrum for communication and for research is a bottleneck as far as technology and business development is considered. It is a fact that the availability of useful spectrum is limited by hardware constraints. The studies conducted by the Federal Communications Commission found that that there are many areas of the radio spectrum which are not fully utilized in different geographical areas of the country and FCC recommended locating and utilizing these unused spectrum spaces by other users. This is where spectrum sensing comes into use. From then on different spectrum sensing algorithms were developed. The paper implements four of those major sensing spectrum algorithms in MATLAB-Simulink and also does a performance comparison among the
A Local Projection-Based Approach to Individual Tree Detection and 3-D Crown Delineation in Multistoried Coniferous Forests Using High-Density Airborne LiDAR Data
No full textAccurate crown detection and delineation of dominant and subdominant trees are crucial for accurate inventorying of forests at the individual tree level. The state-of-the-art tree detection and crown delineation methods have good performance mostly with dominant trees, whereas exhibits a reduced accuracy when dealing with subdominant trees. In this paper, we propose a novel approach to accurately detect and delineate both the dominant and subdominant tree crowns in conifer-dominated multistoried forests using small footprint high-density airborne Light Detection and Ranging data. Here, 3-D candidate cloud segments delineated using a canopy height model segmentation technique are projected onto a novel 3-D space where both the dominant and subdominant tree crowns can be accurately detected and delineated. Tree crowns are detected using 2-D features derived from the projected data. The delineation of the crown is performed at the voxel level with the help of both the 2-D features and 3-D texture information derived from the cloud segment. The texture information is modeled by using 3-D Gray Level Co-occurrence Matrix. The performance evaluation was done on a set of six circular plots for which reference data are available. The high detection and delineation accuracies obtained over the state of the art prove the performance of the proposed method
Subdominant Tree Detection in Multi-layered Forests By a Local Projection of Airborne LiDAR Data
No full textAirborne Light Detection and Ranging (LIDAR) remote sensing based forest inventory at the individual tree level is a valuable and effective alternative to manual inventory, due to factors such as higher accuracy, easy repeatability of sampling, and economic benefits. However, individual tree detection in multi-storied forests is challenging due to high tree proximity and forest structure complexity issues. In this work, we aim at detecting subdominant trees in a multi-stored forest from high density small foot-print multi-return airborne LiDAR data. The marker controlled watershed segmentation is used for the three dimensional (3D) delineation of the dominant tree crowns. The data associated with every segment are separately projected onto a novel 3D space, where crown surface information is effectively represented and subdominant trees are highlighted. A set of ten features is employed to separate subdominant from dominant trees. Preliminary results prove the effectiveness of the proposed method
An approach to tree species classification using voxel neighborhood density based subsampling of multiscan terrestrial LiDAR data
No full textThe knowledge on the species of individual trees is ineluctable for accurate forest parameter estimation and related studies. Terrestrial Laser Scanning (TLS) remote sensing systems acquire a huge number of point samples that contain very accurate and detailed three dimensional (3D) information of tree structures. Every tree species has unique internal and external crown structural characteristics that can be modeled
from its TLS data. However, methods in the state of the art show reduced performance due to inaccurate modeling of tree structures such as the crown, and the branch, and poor selection of features. The proposed method leverages on the fine internal and external crown structural information in TLS data to achieve species classification. We remove noise and stem points in TLS data using a novel voxel neighborhood
density-based technique. Internal and external crown geometric features derived from the branch level, and the crown level, respectively, are provided to a non linear Support Vector Machines (SVM) to achieve species classification, and evaluate feature relevance. All experiments were conducted on a set of 75 manually delineated trees belonging to the Spruce, the Pine, and the Birch species
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