scholarly journals Mobile Laser Scanning for Estimating Tree Stem Diameter Using Segmentation and Tree Spine Calibration

2019 ◽  
Vol 11 (23) ◽  
pp. 2781 ◽  
Author(s):  
Johan Holmgren ◽  
Michael Tulldahl ◽  
Jonas Nordlöf ◽  
Erik Willén ◽  
Håkan Olsson
Keyword(s):  
Laser Scanning ◽  
Basal Area ◽  
Ground Level ◽  
Stem Diameter ◽  
Global Navigation Satellite Systems ◽  
Individual Tree ◽  
Satellite Systems ◽  
Link Distance ◽  
Laser Data ◽  
Tree Stem

Mobile laser scanning (MLS) could make forest inventories more efficient, by using algorithms that automatically derive tree stem center positions and stem diameters. In this work we present a novel method for calibration of the position for laser returns based on tree spines derived from laser data. A first calibration of positions was made for sequential laser scans and further calibrations of laser returns were possible after segmentation, in which laser returns were associated to individual tree stems. The segmentation made it possible to model tree stem spines (i.e., center line of tree stems). Assumptions of coherent tree spine positions were used for correcting laser return positions on the tree stems, thereby improving estimation of stem profiles (i.e., stem diameters at different heights from the ground level). The method was validated on six 20-m radius field plots. Stem diameters were estimated with a Root-Mean-Square-Error (RMSE) of 1 cm for safely linked trees (maximum link distance of 0.5 m) and with a restriction of a minimum amount of data from height intervals for supporting circle estimates. The accuracy was high for plot level estimates of basal area-weighted mean stem diameter (relative RMSE 3.4%) and basal area (relative RMSE 8.5%) because of little influence of small trees (i.e., aggregation of individual trees). The spine calibration made it possible to derive 3D stem profiles also from 3D laser data calculated from sensor positions with large errors because of disturbed below canopy signals from global navigation satellite systems.

Influence of ULS data acquisition characteristics on the achievable stem reconstruction accuracies

2020 ◽  
Author(s):  
Moritz Bruggisser ◽  
Johannes Otepka ◽  
Norbert Pfeifer ◽  
Markus Hollaus
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Diameter ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Detection Rates ◽  
Point Count ◽  
Tree Stem

<p>Unmanned aerial vehicles-borne laser scanning (ULS) allows time-efficient acquisition of high-resolution point clouds on regional extents at moderate costs. The quality of ULS-point clouds facilitates the 3D modelling of individual tree stems, what opens new possibilities in the context of forest monitoring and management. In our study, we developed and tested an algorithm which allows for i) the autonomous detection of potential stem locations within the point clouds, ii) the estimation of the diameter at breast height (DBH) and iii) the reconstruction of the tree stem. In our experiments on point clouds from both, a RIEGL miniVUX-1DL and a VUX-1UAV, respectively, we could detect 91.0 % and 77.6 % of the stems within our study area automatically. The DBH could be modelled with biases of 3.1 cm and 1.1 cm, respectively, from the two point cloud sets with respective detection rates of 80.6 % and 61.2 % of the trees present in the field inventory. The lowest 12 m of the tree stem could be reconstructed with absolute stem diameter differences below 5 cm and 2 cm, respectively, compared to stem diameters from a point cloud from terrestrial laser scanning. The accuracy of larger tree stems thereby was higher in general than the accuracy for smaller trees. Furthermore, we recognized a small influence only of the completeness with which a stem is covered with points, as long as half of the stem circumference was captured. Likewise, the absolute point count did not impact the accuracy, but, in contrast, was critical to the completeness with which a scene could be reconstructed. The precision of the laser scanner, on the other hand, was a key factor for the accuracy of the stem diameter estimation. <br>The findings of this study are highly relevant for the flight planning and the sensor selection of future ULS acquisition missions in the context of forest inventories.</p>

scholarly journals ESTIMATION OF TREE POSITION AND STEM DIAMETER USING SIMULTANEOUS LOCALIZATION AND MAPPING WITH DATA FROM A BACKPACK-MOUNTED LASER SCANNER

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 59-63 ◽  
Author(s):  
J. Holmgren ◽  
H. M. Tulldahl ◽  
J. Nordlöf ◽  
M. Nyström ◽  
K. Olofsson ◽  
...  
Keyword(s):  
Laser Scanner ◽  
Stem Diameter ◽  
Measurement Unit ◽  
Positioning System ◽  
Individual Tree ◽  
Matching Problems ◽  
Localization And Mapping ◽  
Combined Use ◽  
Laser Data ◽  
Tree Stem

A system was developed for automatic estimations of tree positions and stem diameters. The sensor trajectory was first estimated using a positioning system that consists of a low precision inertial measurement unit supported by image matching with data from a stereo-camera. The initial estimation of the sensor trajectory was then calibrated by adjustments of the sensor pose using the laser scanner data. Special features suitable for forest environments were used to solve the correspondence and matching problems. Tree stem diameters were estimated for stem sections using laser data from individual scanner rotations and were then used for calibration of the sensor pose. A segmentation algorithm was used to associate stem sections to individual tree stems. The stem diameter estimates of all stem sections associated to the same tree stem were then combined for estimation of stem diameter at breast height (DBH). The system was validated on four 20 m radius circular plots and manual measured trees were automatically linked to trees detected in laser data. The DBH could be estimated with a RMSE of 19 mm (6 %) and a bias of 8 mm (3 %). The calibrated sensor trajectory and the combined use of circle fits from individual scanner rotations made it possible to obtain reliable DBH estimates also with a low precision positioning system.

scholarly journals Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

2021 ◽  
Vol 13 (12) ◽  
pp. 2297
Author(s):  
Jonathon J. Donager ◽  
Andrew J. Sánchez Meador ◽  
Ryan C. Blackburn
Keyword(s):  
Ponderosa Pine ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Basal Area ◽  
Tree Height ◽  
Absolute Error ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Structural Configurations ◽  
Individual Trees

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

scholarly journals Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 905 ◽  
Author(s):  
Guerra-Hernández ◽  
Cosenza ◽  
Cardil ◽  
Silva ◽  
Botequim ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Point Clouds ◽  
Volume Estimation ◽  
P Value ◽  
Test Statistic ◽  
Individual Tree ◽  
Significant Difference

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations.

scholarly journals Very High Density Point Clouds from UAV Laser Scanning for Automatic Tree Stem Detection and Direct Diameter Measurement

2020 ◽  
Vol 12 (8) ◽  
pp. 1236 ◽  
Author(s):  
Karel Kuželka ◽  
Martin Slavík ◽  
Peter Surový
Keyword(s):  
Cross Sections ◽  
Hough Transform ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Bias Error ◽  
Bias Error ◽  
Least Trimmed Squares ◽  
Remotely Sensed Data ◽  
Individual Tree ◽  
Tree Stem

Three-dimensional light detection and ranging (LiDAR) point clouds acquired from unmanned aerial vehicles (UAVs) represent a relatively new type of remotely sensed data. Point cloud density of thousands of points per square meter with survey-grade accuracy makes the UAV laser scanning (ULS) a very suitable tool for detailed mapping of forest environment. We used RIEGL VUX-SYS to scan forest stands of Norway spruce and Scots pine, the two most important economic species of central European forests, and evaluated the suitability of point clouds for individual tree stem detection and stem diameter estimation in a fully automated workflow. We segmented tree stems based on point densities in voxels in subcanopy space and applied three methods of robust circle fitting to fit cross-sections along the stems: (1) Hough transform; (2) random sample consensus (RANSAC); and (3) robust least trimmed squares (RLTS). We detected correctly 99% and 100% of all trees in research plots for spruce and pine, respectively, and were able to estimate diameters for 99% of spruces and 98% of pines with mean bias error of −0.1 cm (−1%) and RMSE of 6.0 cm (19%), using the best performing method, RTLS. Hough transform was not able to fit perimeters in unfiltered and often incomplete point representations of cross-sections. In general, RLTS performed slightly better than RANSAC, having both higher stem detection success rate and lower error in diameter estimation. Better performance of RLTS was more pronounced in complicated situations, such as incomplete and noisy point structures, while for high-quality point representations, RANSAC provided slightly better results.

scholarly journals The Etruscans in 3D: From Space to Underground

2011 ◽  
Vol 6 ◽  
pp. 283-290 ◽  
Author(s):  
Fabio Remondino ◽  
Alessandro Rizzi ◽  
Belen Jimenez ◽  
Giorgio Agugiaro ◽  
Giorgio Baratti ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Information And Communication Technologies ◽  
Laser Scanning ◽  
Information Science ◽  
Geographic Information ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Heritage Sites ◽  
Museum Exhibitions ◽  
Global Navigation Satellite

eomatics and Geoinformatics deal with spatial and geographic information, 3D surveying and modeling as well as information science infrastructures. Geomatics and Geoinformatics are thus involved in cartography, mapping, photogrammetry, remote sensing, laser scanning, Geographic Information Systems (GIS), Global Navigation Satellite Systems (GNSS), geo-visualisation, geospatial data analysis and Cultural Heritage documentation. In particular the Cultural Heritage field can largely benefit from different Information and Communication Technologies (ICT) tools to make digital heritage information more informative for documentation and conservation issues, archaeological analyses or virtual museums. This work presents the 3D surveying and modeling of different Etruscan heritage sites with their underground frescoed tombs dating back to VII-IV century B.C.. The recorded and processed 3D data are used, beside digital conservation, preservation, transmission to future generations and studies purposes, to create digital contents for virtual visits, museum exhibitions, better access and communication of the heritage information, etc.

ESTIMATING SINGLE TREE STEM AND BRANCH BIOMASS USING TERRESTRIAL LASER SCANNING

2015 ◽  
Vol 77 (26) ◽  
Author(s):  
Nurliyana Izzati Ishak ◽  
Md Afif Abu Bakar ◽  
Muhammad Zulkarnain Abdul Rahman ◽  
Abd Wahid Rasib ◽  
Kasturi Devi Kanniah ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Stem Volume ◽  
Biomass Estimation ◽  
Forest Plot ◽  
Individual Tree ◽  
Matching Process ◽  
Branch Biomass ◽  
Individual Trees ◽  
Tree Stem

This paper presents a novel non-destructive approach for individual tree stem and branch biomass estimation using terrestrial laser scanning data. The study area is located at the Royal Belum Reserved Forest area, Gerik, Perak. Each forest plot was designed with a circular shape and contains several scanning locations to ensure good visibility of each tree. Unique tree signage was located on trees with diameter at breast height (DBH) of 10cm and above.  Extractions of individual trees were done manually and the matching process with the field collected tree properties were relied on the tree signage and tree location as collected by total station. Individual tree stems were reconstructed based on cylinder models from which the total stem volume was calculated. Biomass of individual tree stems was calculated by multiplying stem volume with specific wood density. Biomass of individual was estimated using similar concept of tree stem with the volume estimated from alpha-hull shape. The root mean squared errors (RMSE) of estimated biomass are 50.22kg and 27.20kg for stem and branch respectively. 

Accuracy Assessment of Mobile Laser Scanning Elevation Data in Different Vegetation Areas

2017 ◽  
Author(s):  
Kaupo Kokamägi ◽  
Natalja Liba ◽  
Kristo Must ◽  
Martin Sirk
Keyword(s):  
Laser Scanning ◽  
Accuracy Assessment ◽  
Objective Assessment ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Sparse Vegetation ◽  
Aerial Laser Scanning ◽  
Elevation Data ◽  
Global Navigation Satellite ◽  
Ground Profile

Due to the overall development of technology, laser scanning has reached a new level. During the last decade, all the different technologies necessary for mobile laser scanning, have been developed. Due to the fact that mobile laser scanning brings the need to process very large amounts of data, development of computers and software is also very important. The aim of current research was to assess the accuracy of mobile laser scanning elevation data in different vegetation areas and to explore if mobile laser scanning could be used as an alternative to aerial laser scanning. This article only covers the data collecting, processing and accuracy assessment aspects of the research. Data used in current study was collected in summer of 2015, during mobile laser scanning of Põltsamaa-Kärevere section of E263 route (Tallinn-Tartu-Võru-Luhamaa). Three smaller, differently vegetated, sections were picked from the large project to study the accuracy of elevation data. For accuracy assessment, the mobile laser scanning elevation data was compared to the checkpoints measured with GNSS (Global Navigation Satellite Systems) device. Ground profiles were drawn based on mobile laser scanning data. For objective assessment, accuracy of mobile laser scanning elevation data was compared to accuracy of ground profile elevation data and aerial laser scanning elevation data. The study found that the RMSE (Root Mean Square Error) in the I section, which was a field vegetated with 1 metre high crop, was 0,98 metres. RMSE in the II section, which was a pasture with low and sparse vegetation, was 0,23 metres. RMSE in the III section, which contained a bushy ditch and a field behind it, was 0,61 metres. Results show that the accuracy of mobile laser scanning elevation data depends substantially on the density of vegetation in scanned areas and that drawing ground profiles reduced the RMSE of mobile laser scanning elevation data. Results show that the accuracy of mobile laser scanning elevation data depends substantially on the density of vegetation in scanned areas. On this basis it can be concluded, that the most reasonable time to conduct mobile laser scanning would be during a season, when vegetation is the sparsest. It can also be concluded that drawing ground profiles makes mobile laser scanning data more accurate.

Comparing individual tree detection and the area-based statistical approach for the retrieval of forest stand characteristics using airborne laser scanning in Scots pine stands

2011 ◽  
Vol 41 (3) ◽  
pp. 583-598 ◽  
Author(s):  
Jussi Peuhkurinen ◽  
Lauri Mehtätalo ◽  
Matti Maltamo
Keyword(s):  
Laser Scanning ◽  
Statistical Approach ◽  
Basal Area ◽  
Airborne Laser Scanning ◽  
Tree Size ◽  
Size Distributions ◽  
Individual Tree ◽  
Tree Detection ◽  
Airborne Laser ◽  
Number Of Stems

Airborne laser scanning based forest inventories employ two major methods: individual tree detection (ITD) and the area-based statistical approach (ABSA). ITD is based on the assumption that trees are of a certain form and can be delineated using airborne laser scanning techniques, whereas ABSA is an empirical method based on the relations between area-level forest attributes and laser echo height distributions. These two methods are compared here within the same test area in terms of their usefulness for estimating mean forest stand characteristics and tree size distributions. All evaluations were performed using leave-one-out cross validation. The average errors in volume and basal area did not differ significantly between the methods. ABSA resulted in overall better accuracies when estimating the diameter and height of the basal area median tree and the number of stems, whereas ITD produced significantly biased estimates for the number of stems and the mean tree size. Tree size distributions were estimated with slightly better accuracy using ABSA. More comprehensive investigations revealed that both methods were not able to estimate forest structure (tree size distribution and spatial distribution of tree locations), which in turn, affected the estimation accuracies.

Attempts at Automatic Detection of Railway Head Edges from Images and Laser Data

2012 ◽  
Vol 17 (4) ◽  
pp. 151-160 ◽  
Author(s):  
Urszula Marmol ◽  
Sławomir Mikrut
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Research Work ◽  
Ground Level ◽  
Automatic Extraction ◽  
Technical Parameters ◽  
Railway Infrastructure ◽  
Laser Data ◽  
Railway Rail ◽  
Engineering Projects

Abstract The more and more high resolution of aerial and ground images, as well as high density of laser data cause that they are more and more widely applied in many engineering projects. Given the current technical parameters, it is also possible to map railway infrastructure not only from the ground level but also from airborne locations (photogrammetry, laser scanning). Testing the usefulness of those data in obtaining information about railway infrastructure, and in particular, in detecting rail heads has been a subject of research of this paper authors. The paper presents results of experiments, consisting in verification of existing solutions and testing own algorithms for an automatic extraction of railway rail heads. The tested algorithms of object detecting and locating produced preliminary, satisfying results. The authors believe it to be reasonable to continue their research work.

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