scholarly journals Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry

2020 ◽  
Vol 12 (18) ◽  
pp. 2884
Author(s):  
Qingwang Liu ◽  
Liyong Fu ◽  
Qiao Chen ◽  
Guangxing Wang ◽  
Peng Luo ◽  
...  
Keyword(s):  
Point Cloud ◽  
Forest Canopy ◽  
Point Clouds ◽  
Canopy Height ◽  
Central China ◽  
Data Sets ◽  
Interpolation Algorithm ◽  
Data Set ◽  
Spatial Differences ◽  
Mean Square Errors

Forest canopy height is one of the most important spatial characteristics for forest resource inventories and forest ecosystem modeling. Light detection and ranging (LiDAR) can be used to accurately detect canopy surface and terrain information from the backscattering signals of laser pulses, while photogrammetry tends to accurately depict the canopy surface envelope. The spatial differences between the canopy surfaces estimated by LiDAR and photogrammetry have not been investigated in depth. Thus, this study aims to assess LiDAR and photogrammetry point clouds and analyze the spatial differences in canopy heights. The study site is located in the Jigongshan National Nature Reserve of Henan Province, Central China. Six data sets, including one LiDAR data set and five photogrammetry data sets captured from an unmanned aerial vehicle (UAV), were used to estimate the forest canopy heights. Three spatial distribution descriptors, namely, the effective cell ratio (ECR), point cloud homogeneity (PCH) and point cloud redundancy (PCR), were developed to assess the LiDAR and photogrammetry point clouds in the grid. The ordinary neighbor (ON) and constrained neighbor (CN) interpolation algorithms were used to fill void cells in digital surface models (DSMs) and canopy height models (CHMs). The CN algorithm could be used to distinguish small and large holes in the CHMs. The optimal spatial resolution was analyzed according to the ECR changes of DSMs or CHMs resulting from the CN algorithms. Large negative and positive variations were observed between the LiDAR and photogrammetry canopy heights. The stratified mean difference in canopy heights increased gradually from negative to positive when the canopy heights were greater than 3 m, which means that photogrammetry tends to overestimate low canopy heights and underestimate high canopy heights. The CN interpolation algorithm achieved smaller relative root mean square errors than the ON interpolation algorithm. This article provides an operational method for the spatial assessment of point clouds and suggests that the variations between LiDAR and photogrammetry CHMs should be considered when modeling forest parameters.

scholarly journals Evaluation of the Simard et al. 2011 Global Canopy Height Map in Boreal Forests

2020 ◽  
Vol 12 (7) ◽  
pp. 1114
Author(s):  
Wei Yang ◽  
Akihiko Kondoh
Keyword(s):  
Boreal Forests ◽  
Forest Canopy ◽  
Spatial Separation ◽  
Canopy Height ◽  
Tree Cover ◽  
Data Sets ◽  
Data Set ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Light detection and ranging (LiDAR) provides a state-of-the-art technique for measuring forest canopy height. Nevertheless, it may miss some forests due to its spatial separation of individual spots. A number of efforts have been made to overcome the limitation of global LiDAR datasets to generate wall-to-wall canopy height products, among which a global satellite product produced by Simard et al. (2011) (henceforth, the Simard-map) has been the most widely applied. However, the accuracy of the Simard-map is uncertain in boreal forests, which play important roles in the terrestrial carbon cycle and are encountering more extensive climate changes than the global average. In this letter, we evaluated the Simard-map in boreal forests through a literature review of field canopy height. Our comparison shows that the Simard-map yielded a significant correlation with the field canopy height (R2 = 0.68 and p < 0.001). However, remarkable biases were observed with the root mean square error (RMSE), regression slope, and intercept of 6.88 m, 0.448, and 10.429, respectively. Interestingly, we found that the evaluation results showed an identical trend with a validation of moderate-resolution imaging spectroradiometer (MODIS) tree-cover product (MOD44B) in boreal forests, which was used as a crucial input data set for generating the Simard-map. That is, both the Simard-map and MOD44B yielded an overestimation (underestimation) in the lower (upper) tails of the scatterplots between the field and satellite data sets. This indicates that the MOD44B product is the likely source of error for the estimation biases of the Simard-map. Finally, a field calibration was performed to improve the Simard-map in boreal forests by compensating for the estimation biases and discarding non-forest areas, which provided a more reliable canopy height product for future applications.

Ground filtering algorithm for mobile LIDAR using order and neighborhood point information

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Siyuan Huang ◽  
Limin Liu ◽  
Jian Dong ◽  
Xiongjun Fu ◽  
Leilei Jia
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Sorting Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Spherical Coordinate ◽  
Content Type ◽  
Cloud Data ◽  
Filtering Algorithm ◽  
Field Environment

Purpose Most of the existing ground filtering algorithms are based on the Cartesian coordinate system, which is not compatible with the working principle of mobile light detection and ranging and difficult to obtain good filtering accuracy. The purpose of this paper is to improve the accuracy of ground filtering by making full use of the order information between the point and the point in the spherical coordinate. Design/methodology/approach First, the cloth simulation (CS) algorithm is modified into a sorting algorithm for scattered point clouds to obtain the adjacent relationship of the point clouds and to generate a matrix containing the adjacent information of the point cloud. Then, according to the adjacent information of the points, a projection distance comparison and local slope analysis are simultaneously performed. These results are integrated to process the point cloud details further and the algorithm is finally used to filter a point cloud in a scene from the KITTI data set. Findings The results show that the accuracy of KITTI point cloud sorting is 96.3% and the kappa coefficient of the ground filtering result is 0.7978. Compared with other algorithms applied to the same scene, the proposed algorithm has higher processing accuracy. Research limitations/implications Steps of the algorithm are parallel computing, which saves time owing to the small amount of computation. In addition, the generality of the algorithm is improved and it could be used for different data sets from urban streets. However, due to the lack of point clouds from the field environment with labeled ground points, the filtering result of this algorithm in the field environment needs further study. Originality/value In this study, the point cloud neighboring information was obtained by a modified CS algorithm. The ground filtering algorithm distinguish ground points and off-ground points according to the flatness, continuity and minimality of ground points in point cloud data. In addition, it has little effect on the algorithm results if thresholds were changed.

scholarly journals Interactive Correspondence Selection

2011 ◽  
Author(s):  
David Doria
Keyword(s):  
Fundamental Matrix ◽  
Point Cloud ◽  
Point Clouds ◽  
The Other ◽  
Data Sets ◽  
Data Set

This document presents a GUI application to manually select corresponding points in two data sets. The data sets can each be either an image or a point cloud. If both data sets are images, the functionality is equivalent to Matlab’s ‘cpselect’ function. There are many uses of selecting correspondences. If both data sets are images, the correspondences can be used to compute the fundamental matrix, or to perform registration. If both data sets are point clouds, the correspondences can be used to compute a landmark transformation. If one data set is an image and the other is a point cloud, the camera matrix relating the two can be computed.

scholarly journals Architecture of Solution for Panoramic Image Blurring in GIS projects Application

2021 ◽  
Author(s):  
Dejan Vasić ◽  
Marina Davidović ◽  
Ivan Radosavljević ◽  
Đorđe Obradović
Keyword(s):  
Defect Detection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Visual Inspection ◽  
Point Clouds ◽  
Data Sets ◽  
Data Set ◽  
Image Blurring ◽  
Panoramic Images ◽  
Spatial Entities

Abstract. Panoramic images captured using laser scanning technologies, which principally produce point clouds, are readily applicable in colorization of point cloud, detailed visual inspection, road defect detection, spatial entities extraction, diverse maps creation etc. This paper underlines the importance of images in modern surveying technologies and different GIS projects at the same time having regard to their anonymization in accordance with GDPR. Namely, it is a legislative requirement that faces of persons and license plates of vehicles in the collected data are blurred. The objective of this paper is to present a novel architecture of the solution for a particular object blurring. The methodology was tested on four data sets counting 5000, 10 000, 15 000 and 20 000 panoramic images respectively. Percentage of accuracy, i.e. successfully detected and blurred objects of interest, was higher than 97 % for each data set.

scholarly journals ON A KNOWLEDGE-BASED APPROACH TO THE CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-4 ◽  
pp. 343-349 ◽  
Author(s):  
M. Lemmens
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Decision Rules ◽  
Point Clouds ◽  
Bench Mark ◽  
Automated Classification ◽  
Initial Experiment ◽  
Data Set ◽  
Knowledge Based

<p><strong>Abstract.</strong> A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79<span class="thinspace"></span>% for the Paris-rue-Madame point cloud bench mark data set.</p>

scholarly journals 3-D Point Cloud Registration Using Convolutional Neural Networks

2019 ◽  
Vol 9 (16) ◽  
pp. 3273 ◽  
Author(s):  
Wen-Chung Chang ◽  
Van-Toan Pham
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Full Range ◽  
Point Clouds ◽  
Fixed Time ◽  
Training Data ◽  
Data Sets ◽  
Point Cloud Registration ◽  
Cloud Data ◽  
Model Point

This paper develops a registration architecture for the purpose of estimating relative pose including the rotation and the translation of an object in terms of a model in 3-D space based on 3-D point clouds captured by a 3-D camera. Particularly, this paper addresses the time-consuming problem of 3-D point cloud registration which is essential for the closed-loop industrial automated assembly systems that demand fixed time for accurate pose estimation. Firstly, two different descriptors are developed in order to extract coarse and detailed features of these point cloud data sets for the purpose of creating training data sets according to diversified orientations. Secondly, in order to guarantee fast pose estimation in fixed time, a seemingly novel registration architecture by employing two consecutive convolutional neural network (CNN) models is proposed. After training, the proposed CNN architecture can estimate the rotation between the model point cloud and a data point cloud, followed by the translation estimation based on computing average values. By covering a smaller range of uncertainty of the orientation compared with a full range of uncertainty covered by the first CNN model, the second CNN model can precisely estimate the orientation of the 3-D point cloud. Finally, the performance of the algorithm proposed in this paper has been validated by experiments in comparison with baseline methods. Based on these results, the proposed algorithm significantly reduces the estimation time while maintaining high precision.

scholarly journals Integrating UAV and TLS Approaches for Environmental Management: A Case Study of a Waste Stockpile Area

2020 ◽  
Vol 12 (10) ◽  
pp. 1615 ◽  
Author(s):  
Seung Woo Son ◽  
Dong Woo Kim ◽  
Woong Gi Sung ◽  
Jae Jin Yu
Keyword(s):  
Environmental Management ◽  
Point Cloud ◽  
Laser Scanning ◽  
High Efficiency ◽  
Point Clouds ◽  
Fusion Model ◽  
Spatial Features ◽  
Computation Efficiency ◽  
Volume Computation ◽  
Mean Square Errors

A methodology for optimal volume computation for the environmental management of waste stockpiles was derived by integrating the terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) technologies. Among the UAV-based point clouds generated under various flight scenarios, the most accurate point cloud was selected for analysis. The root mean square errors (RMSEs) of the TLS- and UAV-based methods were 0.202 and 0.032 m, respectively, and the volume computation yielded 41,226 and 41,526 m3, respectively. Both techniques showed high accuracy but also exhibited drawbacks in terms of their spatial features and efficiency. The TLS and UAV methods required 800 and 340 min, respectively, demonstrating the high efficiency of the UAV method. The RMSE and volume obtained using the TLS/UAV fusion model were calculated as 0.030 m and 41,232 m3, respectively. The UAV approach generally yielded high point cloud accuracy and volume computation efficiency.

scholarly journals Processing Chain for Estimation of Tree Diameter from GNSS-IMU-Based Mobile Laser Scanning Data

2019 ◽  
Vol 11 (6) ◽  
pp. 615 ◽  
Author(s):  
Juraj Čerňava ◽  
Martin Mokroš ◽  
Ján Tuček ◽  
Michal Antal ◽  
Zuzana Slatkovská
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Point Clouds ◽  
Satellite System ◽  
Forest Road ◽  
Forest Mapping ◽  
Fitting Method ◽  
Progressive Technology ◽  
Global Navigation Satellite

Mobile laser scanning (MLS) is a progressive technology that has already demonstrated its ability to provide highly accurate measurements of road networks. Mobile innovation of the laser scanning has also found its use in forest mapping over the last decade. In most cases, existing methods for forest data acquisition using MLS result in misaligned scenes of the forest, scanned from different views appearing in one point cloud. These difficulties are caused mainly by forest canopy blocking the global navigation satellite system (GNSS) signal and limited access to the forest. In this study, we propose an approach to the processing of MLS data of forest scanned from different views with two mobile laser scanners under heavy canopy. Data from two scanners, as part of the mobile mapping system (MMS) Riegl VMX-250, were acquired by scanning from five parallel skid trails that are connected to the forest road. Misaligned scenes of the forest acquired from different views were successfully extracted from the raw MLS point cloud using GNSS time based clustering. At first, point clouds with correctly aligned sets of ground points were generated using this method. The loss of points after the clustering amounted to 33.48%. Extracted point clouds were then reduced to 1.15 m thick horizontal slices, and tree stems were detected. Point clusters from individual stems were grouped based on the diameter and mean GNSS time of the cluster acquisition. Horizontal overlap was calculated for the clusters from individual stems, and sufficiently overlapping clusters were aligned using the OPALS ICP module. An average misalignment of 7.2 mm was observed for the aligned point clusters. A 5-cm thick horizontal slice of the aligned point cloud was used for estimation of the stem diameter at breast height (DBH). DBH was estimated using a simple circle-fitting method with a root-mean-square error of 3.06 cm. The methods presented in this study have the potential to process MLS data acquired under heavy forest canopy with any commercial MMS.

scholarly journals Influence of Control Points Configuration on the Mobile Laser Scanning Accuracy

2021 ◽  
Vol 906 (1) ◽  
pp. 012091
Author(s):  
Petr Kalvoda ◽  
Jakub Nosek ◽  
Petra Kalvodova
Keyword(s):  
Reference Point ◽  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Data Sets ◽  
Control Points ◽  
Mobile Mapping ◽  
Data Set ◽  
Accuracy Test ◽  
Data Subset

Abstract Mobile mapping systems (MMS) are becoming widely used in standard geodetic tasks more commonly in the last years. The paper is focused on the influence of control points (CPs) number and configuration on mobile laser scanning accuracy. The mobile laser scanning (MLS) data was acquired by MMS RIEGL VMX-450. The resulting point cloud was compared with two different reference data sets. The first reference data set consisted of a high-accuracy test point field (TPF) measured by a Trimble R8s GNSS system and a Trimble S8 HP total station. The second reference data set was a point cloud from terrestrial laser scanning (TLS) using two Faro Focus3D X 130 laser scanners. The coordinates of both reference data sets were determined with significantly higher accuracy than the coordinates of the tested MLS point cloud. The accuracy testing is based on coordinate differences between the reference data set and the tested MLS point cloud. There is a minimum number of 6–7 CPs in our scanned area (based on MLS trajectory length) to achieve the declared relative accuracy of trajectory positioning according to the RIEGL datasheet. We tested two types of ground control point (GCP) configurations for 7 GCPs, using TPF reference data. The first type is a trajectory-based CPs configuration, and the second is a geometry-based CPs configuration. The accuracy differences of the MLS point clouds with trajectory-based CPs configuration and geometry-based CPs configuration are not statistically significant. From a practical perspective, a geometry-based CPs configuration is more advantageous in the nonlinear type of urban area such as our one. The following analyzes are performed on geometry-based CPs configuration variants. We tested the influence of changing the location of two CPs from ground to roof. The effect of the vertical configuration of the CPs on the accuracy of the tested MLS point cloud has not been demonstrated. The effect of the number of control points on the accuracy of the MLS point cloud was also tested. In the overall statistics using TPF, the accuracy increases significantly with increasing the number of GCPs up to 6. This number corresponds to a requirement of the manufacturer. Although further increasing the number of CPs does not significantly increase the global accuracy, local accuracy improves with increasing the number of CPs up to 10 (average spacing 50 m) according to the comparison with the TLS reference point cloud. The accuracy test of the MLS point cloud was divided into the horizontal accuracy test on the façade data subset and the vertical accuracy test on the road data subset using the TLS reference point cloud. The results of this paper can help improve the efficiency and accuracy of the mobile mapping process in geodetic praxis.

scholarly journals RADIOMETRIC CORRECTION OF MULTITEMPORAL HYPERSPECTRAL UAS IMAGE MOSAICS OF SEEDLING STANDS

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 113-118 ◽  
Author(s):  
L. Markelin ◽  
E. Honkavaara ◽  
R. Näsi ◽  
N. Viljanen ◽  
T. Rosnell ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Atmospheric Correction ◽  
Point Clouds ◽  
Hyperspectral Data ◽  
Reflectance Spectra ◽  
Data Sets ◽  
Data Set ◽  
Relative Correction ◽  
Small Areas ◽  
3D Point Clouds

Novel miniaturized multi- and hyperspectral imaging sensors on board of unmanned aerial vehicles have recently shown great potential in various environmental monitoring and measuring tasks such as precision agriculture and forest management. These systems can be used to collect dense 3D point clouds and spectral information over small areas such as single forest stands or sample plots. Accurate radiometric processing and atmospheric correction is required when data sets from different dates and sensors, collected in varying illumination conditions, are combined. Performance of novel radiometric block adjustment method, developed at Finnish Geospatial Research Institute, is evaluated with multitemporal hyperspectral data set of seedling stands collected during spring and summer 2016. Illumination conditions during campaigns varied from bright to overcast. We use two different methods to produce homogenous image mosaics and hyperspectral point clouds: image-wise relative correction and image-wise relative correction with BRDF. Radiometric datasets are converted to reflectance using reference panels and changes in reflectance spectra is analysed. Tested methods improved image mosaic homogeneity by 5&amp;thinsp;% to 25&amp;thinsp;%. Results show that the evaluated method can produce consistent reflectance mosaics and reflectance spectra shape between different areas and dates.

Sign in / Sign up

Export Citation Format

Share Document