scholarly journals Mapping Individual Tree Species and Vitality along Urban Road Corridors with LiDAR and Imaging Sensors: Point Density versus View Perspective

2018 ◽  
Vol 10 (9) ◽  
pp. 1403 ◽  
Author(s):  
Jianwei Wu ◽  
Wei Yao ◽  
Przemyslaw Polewski
Keyword(s):  
Tree Species ◽  
Urban Areas ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Scanning System ◽  
Urban Road ◽  
Road Corridors ◽  
Laser Scanning System

To meet a growing demand for accurate high-fidelity vegetation cover mapping in urban areas toward biodiversity conservation and assessing the impact of climate change, this paper proposes a complete approach to species and vitality classification at single tree level by synergistic use of multimodality 3D remote sensing data. So far, airborne laser scanning system(ALS or airborne LiDAR) has shown promising results in tree cover mapping for urban areas. This paper analyzes the potential of mobile laser scanning system/mobile mapping system (MLS/MMS)-based methods for recognition of urban plant species and characterization of growth conditions using ultra-dense LiDAR point clouds and provides an objective comparison with the ALS-based methods. Firstly, to solve the extremely intensive computational burden caused by the classification of ultra-dense MLS data, a new method for the semantic labeling of LiDAR data in the urban road environment is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. These priors encode geometric primitives found in the scene through sample consensus segmentation. Then, single trees are segmented from the labelled tree points using the 3D graph cuts algorithm. Multinomial logistic regression classifiers are used to determine the fine deciduous urban tree species of conversation concern and their growth vitality. Finally, the weight-of-evidence (WofE) based decision fusion method is applied to combine the probability outputs of classification results from the MLS and ALS data. The experiment results obtained in city road corridors demonstrated that point cloud data acquired from the airborne platform achieved even slightly better results in terms of tree detection rate, tree species and vitality classification accuracy, although the tree vitality distribution in the test site is less balanced compared to the species distribution. When combined with MLS data, overall accuracies of 78% and 74% for tree species and vitality classification can be achieved, which has improved by 5.7% and 4.64% respectively compared to the usage of airborne data only.

Tree segmentation and classification of deciduous park trees in Sanssouci Park, Potsdam, Germany, using airborne and terrestrial lidar point clouds

2021 ◽  
Author(s):  
Aljoscha Rheinwalt ◽  
Bodo Bookhagen
Keyword(s):  
Tree Species ◽  
Supervised Classification ◽  
Spatial Scales ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Deciduous Tree ◽  
Individual Tree ◽  
Terrestrial Lidar ◽  
Common Tree

<p>While automated, lidar-based tree delineation has proven successful for<br>conifer-dominated forests, deciduous tree stands remain a challenge.  But<br>automatic and reliable segmentation of trees at large spatial scales is a<br>prerequisite for a supervised classification into tree species. We propose an<br>aspect driven tree segmentation that clusters local elevation minima across<br>different aspects. These clusters define tree outlines that respect tree<br>inherent local elevation minima. We validate this approach with more than<br>25.000 mapped trees of the Sanssouci Park, Potsdam, using an airborne lidar<br>point cloud collected in 2018, and various terrestrial lidar scans for a large<br>fraction of the same park. Further, we demonstrate the tree segmentation by<br>supervised tree species classifications for the most common tree species using<br>random forests and Gaussian process classifiers with geometric parameters<br>derived from individual tree crowns.</p>

scholarly journals FEATURE RELEVANCE ASSESSMENT OF MULTISPECTRAL AIRBORNE LIDAR DATA FOR TREE SPECIES CLASSIFICATION

2018 ◽  
Vol XLII-3 ◽  
pp. 31-34 ◽  
Author(s):  
N. Amiri ◽  
M. Heurich ◽  
P. Krzystek ◽  
A. K. Skidmore
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Species Classification ◽  
Tree Species Classification ◽  
3D Point Clouds ◽  
Segmentation Approach ◽  
Species Mapping ◽  
Spectral Channels

The presented experiment investigates the potential of Multispectral Laser Scanning (MLS) point clouds for single tree species classification. The basic idea is to simulate a MLS sensor by combining two different Lidar sensors providing three different wavelngthes. The available data were acquired in the summer 2016 at the same date in a leaf-on condition with an average point density of 37&amp;thinsp;points/m<sup>2</sup>. For the purpose of classification, we segmented the combined 3D point clouds consisiting of three different spectral channels into 3D clusters using Normalized Cut segmentation approach. Then, we extracted four group of features from the 3D point cloud space. Once a varity of features has been extracted, we applied forward stepwise feature selection in order to reduce the number of irrelevant or redundant features. For the classification, we used multinomial logestic regression with <i>L<sub>1</sub></i> regularization. Our study is conducted using 586 ground measured single trees from 20 sample plots in the Bavarian Forest National Park, in Germany. Due to lack of reference data for some rare species, we focused on four classes of species. The results show an improvement between 4&amp;ndash;10&amp;thinsp;pp for the tree species classification by using MLS data in comparison to a single wavelength based approach. A cross validated (15-fold) accuracy of 0.75 can be achieved when all feature sets from three different spectral channels are used. Our results cleary indicates that the use of MLS point clouds has great potential to improve detailed forest species mapping.

scholarly journals HIERARCHICAL HIGHER ORDER CRF FOR THE CLASSIFICATION OF AIRBORNE LIDAR POINT CLOUDS IN URBAN AREAS

2016 ◽  
Vol XLI-B3 ◽  
pp. 655-662 ◽  
Author(s):  
J. Niemeyer ◽  
F. Rottensteiner ◽  
U. Soergel ◽  
C. Heipke
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Conditional Random Field ◽  
Point Clouds ◽  
Higher Order ◽  
Airborne Lidar ◽  
Classification Result ◽  
3D Lidar ◽  
Point Level

We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point level and utilises higher order cliques. Segments are generated from the labelling obtained in this way. They are the entities of the second layer, which incorporates larger scale context. The classification result of the segments is introduced as an energy term for the next iteration of the point-based layer. This framework iterates and mutually propagates context to improve the classification results. Potentially wrong decisions can be revised at later stages. The output is a labelled point cloud as well as segments roughly corresponding to object instances. Moreover, we present two new contextual features for the segment classification: the &lt;i&gt;distance&lt;/i&gt; and the &lt;i&gt;orientation of a segment with respect to the closest road&lt;/i&gt;. It is shown that the classification benefits from these features. In our experiments the hierarchical framework improve the overall accuracies by 2.3% on a point-based level and by 3.0% on a segment-based level, respectively, compared to a purely point-based classification.

scholarly journals SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

2013 ◽  
Vol 5 (8) ◽  
pp. 3749-3775 ◽  
Author(s):  
Jixian Zhang ◽  
Xiangguo Lin ◽  
Xiaogang Ning
Keyword(s):  
Urban Areas ◽  
Point Clouds ◽  
Airborne Lidar

scholarly journals Design and Evaluation of a Permanently Installed Plane-Based Calibration Field for Mobile Laser Scanning Systems

2020 ◽  
Vol 12 (3) ◽  
pp. 555 ◽  
Author(s):  
Erik Heinz ◽  
Christoph Holst ◽  
Heiner Kuhlmann ◽  
Lasse Klingbeil
Keyword(s):  
Reference Point ◽  
Laser Scanning ◽  
Point Clouds ◽  
Measurement Unit ◽  
Scanning System ◽  
Control Points ◽  
Lever Arm ◽  
Laser Scanning System ◽  
The Impact ◽  
Scanning Systems

Mobile laser scanning has become an established measuring technique that is used for many applications in the fields of mapping, inventory, and monitoring. Due to the increasing operationality of such systems, quality control w.r.t. calibration and evaluation of the systems becomes more and more important and is subject to on-going research. This paper contributes to this topic by using tools from geodetic configuration analysis in order to design and evaluate a plane-based calibration field for determining the lever arm and boresight angles of a 2D laser scanner w.r.t. a GNSS/IMU unit (Global Navigation Satellite System, Inertial Measurement Unit). In this regard, the impact of random, systematic, and gross observation errors on the calibration is analyzed leading to a plane setup that provides accurate and controlled calibration parameters. The designed plane setup is realized in the form of a permanently installed calibration field. The applicability of the calibration field is tested with a real mobile laser scanning system by frequently repeating the calibration. Empirical standard deviations of <1 ... 1.5 mm for the lever arm and <0.005 ∘ for the boresight angles are obtained, which was priorly defined to be the goal of the calibration. In order to independently evaluate the mobile laser scanning system after calibration, an evaluation environment is realized consisting of a network of control points as well as TLS (Terrestrial Laser Scanning) reference point clouds. Based on the control points, both the horizontal and vertical accuracy of the system is found to be < 10 mm (root mean square error). This is confirmed by comparisons to the TLS reference point clouds indicating a well calibrated system. Both the calibration field and the evaluation environment are permanently installed and can be used for arbitrary mobile laser scanning systems.

scholarly journals QUALITY ANALYSIS AND CORRECTION OF MOBILE BACKPACK LASER SCANNING DATA

2016 ◽  
Vol III-1 ◽  
pp. 41-47 ◽  
Author(s):  
P. Rönnholm ◽  
X. Liang ◽  
A. Kukko ◽  
A. Jaakkola ◽  
J. Hyyppä
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Quality Analysis ◽  
Scanning System ◽  
Forest Environment ◽  
Analysis Process ◽  
Ground Point ◽  
Aerial Vehicle ◽  
Laser Scanning System ◽  
Scanning Systems

Backpack laser scanning systems have emerged recently enabling fast data collection and flexibility to make measurements also in areas that cannot be reached with, for example, vehicle-based laser scanners. Backpack laser scanning systems have been developed both for indoor and outdoor use. We have developed a quality analysis process in which the quality of backpack laser scanning data is evaluated in the forest environment. The reference data was collected with an unmanned aerial vehicle (UAV) laser scanning system. The workflow included noise filtering, division of data into smaller patches, ground point extraction, ground data decimation, and ICP registration. As a result, we managed to observe the misalignments of backpack laser scanning data for 97 patches each including data from circa 10 seconds period of time. This evaluation revealed initial average misalignments of 0.227 m, 0.073 and -0.083 in the easting, northing and elevation directions, respectively. Furthermore, backpack data was corrected according to the ICP registration results. Our correction algorithm utilized the time-based linear transformation of backpack laser scanning point clouds. After the correction of data, the ICP registration was run again. This revealed remaining misalignments between the corrected backpack laser scanning data and the original UAV data. We found average misalignments of 0.084, 0.020 and -0.005 meters in the easting, northing and elevation directions, respectively.

scholarly journals AUTOMATIC ROAD STRUCTURE DETECTION AND VECTORIZATION USING MLS POINT CLOUDS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1069-1075 ◽  
Author(s):  
X. Mi ◽  
B. Yang ◽  
C. Chen ◽  
M. Yang ◽  
Z. Dong
Keyword(s):  
Free Space ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Scanning System ◽  
The Road ◽  
Structure Detection ◽  
Dimensional Boundary ◽  
On The Road ◽  
Laser Scanning System

<p><strong>Abstract.</strong> Accurate three-dimensional road structures and models are of great significance to intelligent transportation applications, such as vehicle navigation, inventory evaluation, construction quality control, self-driving vehicles and so on. This paper proposes an efficient and robust method to automatically extract structured road curbs from mobile laser scanning (MLS) data. The proposed method mainly consists of three steps: efficient supervoxel generation, road curbs detection and driving free space estimation. First, supervoxels are generated by assigning ground points with similar geometrical characteristics into the same group. Second, supervoxels with higher local projection density and height difference are identified and clustered as initial road curbs, which are continuous vertical curb facets. The continuous facades consisting of lots of scanned points on the road shoulder can be modeled as multi-dimensional boundary models depending on the requirements of the application, such as vector lines with or without height, micro-facades, etc. Finally, driving free space is obtained due to the road limits can be defined by road boundary in most scenarios. The proposed method is tested on two complex datasets acquired by an Alpha3D mobile laser scanning system from the urban area of Shanghai, China. Experimental results show that the road boundaries and driving free space can be accurately and efficiently extracted, which also demonstrates the superiority of the proposed method.</p>

scholarly journals SEGMENT-BASED LIDAR ODOMETRY FOR LESS STRUCTURED OUTDOOR SCENE

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 535-540
Author(s):  
W. Wu ◽  
C. Chen ◽  
J. Li ◽  
Y. Cong ◽  
B. Yang
Keyword(s):  
Laser Scanning ◽  
State Of The Art ◽  
Low Cost ◽  
Sampling Strategy ◽  
Point Clouds ◽  
Scanning System ◽  
System A ◽  
Outdoor Scenes ◽  
Wheeled Robot ◽  
Laser Scanning System

Abstract. Accurate registration of sparse sequential point clouds data frames acquired by a 3D light detection and ranging (LiDAR) sensor like VLP-16 is a prerequisite for the back-end optimization of general LiDAR SLAM algorithms to achieve a globally consistent map. This process is also called LiDAR odometry. Aiming to achieve lower drift and robust LiDAR odometry in less structured outdoor scene using a low-cost wheeled robot-borne laser scanning system, a segment-based sampling strategy for LiDAR odometry is proposed in this paper. Proposed method was tested in two typical less structured outdoor scenes and compared with other two state of the art methods. The results reveal that the proposed method achieves lower drift and significantly outperform the state of the art.

scholarly journals A CALIBRATION METHOD OF TWO MOBILE LASER SCANNING SYSTEM UNITS FOR RAILWAY MEASUREMENT

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 277-283
Author(s):  
K. Yamamoto ◽  
T. Chen ◽  
N. Yabuki
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Calibration Method ◽  
Railway Vehicle ◽  
Scanning System ◽  
Mean Square ◽  
Least Mean Square ◽  
Linear Type ◽  
Laser Scanning System

Abstract. This paper proposes a methodology to calibrate the laser scanner of a Mobile Laser Scanning System (MLS) with the trajectory of the other MLS, both of which are installed directly above the top of both rails. Railway vehicle laser scanners systems of MLS are able to obtain 3D scanning map of the rail environment. In order to adapt the actual site condition of the maintenance works, we propose a calibration method with non-linear Least Mean Square calculation which use point clouds around poles along rails and sleepers of rails as cylindrical and planner constraints. The accuracy of 0.006 m between two laser point clouds can be achieved with this method. With the common planar and cylinder condition Leven-Marquardt method has been applied for this method. This method can execute without a good initial value for the extrinsic parameter and can shorten the processing time compared with the linear type of Least Mean Square method.

scholarly journals Estimates of slope erosion intensity utilizing terrestrial laser scanning

2015 ◽  
Vol 367 ◽  
pp. 59-65 ◽  
Author(s):  
B. Usmanov ◽  
O. Yermolaev ◽  
A. Gafurov
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Scanning System ◽  
Software Inspection ◽  
Rill Erosion ◽  
Erosion Rates ◽  
Laser Scanning System ◽  
Runoff Events ◽  
Erosion Intensity

Abstract. Despite the large variety of methods for estimating slope erosion intensity, it is still difficult to obtain accurate erosion rates. Therefore, our goal was to develop a method to provide accurate estimates of sheet and rill erosion intensities, and evaluate denudation quantities due to abrasion, landslides and talus processes using a high-precision laser scanning system (Trimble® GX). Differential maps between all stages of surveying and TIN-models were built directly on point clouds in "Trimble® RealWorks" software. Inspection and cross-section tools were used for detailed study of ground movements on the slope surface and the development of linear erosion forms. A new method for accurate estimates of the erosion has been developed using terrestrial laser scanning techniques. It makes it possible to assess the denudation–accumulation balance on erosive slopes, determine the dynamics of the volume of material moved on different parts of the slope in various surface runoff events, and identify spatial regularities forming rill washouts.

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