Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data

This study presents and compares new methods to describe the 3D canopy structure with Airborne Laser Scanning (ALS) waveform data as well as ALS point data. The ALS waveform data were analyzed in three different ways; by summing the intensity of the waveforms in height intervals (a); by first normalizing the waveforms with an algorithm based on Beer-Lambert law to compensate for the shielding effect of higher vegetation layers on reflection from lower layers and then summing the intensity (b); and by deriving points from the waveforms (c). As a comparison, conventional, discrete return ALS point data from the laser scanning system were also analyzed (d). The study area was located in hemi-boreal, spruce dominated forest in the southwest of Sweden (Lat. 58° N, Long. 13° E). The vegetation volume profile was defined as the volume of all tree crowns and shrubs in 1 dm height intervals in a field plot and the total vegetation volume as the sum of the vegetation volume profile in the field plot. The total vegetation volume was estimated for 68 field plots with 12 m radius from the proportion between the amount of ALS reflections from the vegetation and the total amount of ALS reflections based on Beer-Lambert law. ALS profiles were derived from the distribution of the ALS data above the ground in 1 dm height intervals. The ALS profiles were rescaled using the estimated total vegetation volume to derive the amount of vegetation at different heights above the ground. The root mean square error (RMSE) for cross validated regression estimates of the total vegetation volume was 31.9% for ALS waveform data (a), 27.6% for normalized waveform data (b), 29.1% for point data derived from the ALS waveforms (c), and 36.5% for ALS point data from the laser scanning system (d). The correspondence between the estimated vegetation volume profiles was also best for the normalized waveform data and the point data derived from the ALS waveforms and worst for ALS point data from the laser scanning system as demonstrated by the Reynolds error index. The results suggest that ALS waveform data describe the volumetric aspects of vertical vegetation structure somewhat more accurately than ALS point data from the laser scanning system and that compensation for the shielding effect of higher vegetation layers is useful. The new methods for estimation of vegetation volume profiles from ALS data could be used in the future to derive 3D models of the vegetation structure in large areas

Low umbilical artery vascular flow resistance and fetal outcome.

Background. An abnormally high [above mean + 2 standard deviations (SD)] umbilical artery (UA) pulsatility index (PI) indicates impaired fetal outcome, whereas the impact of an "abnormally" low (below mean -2 SD) PI is unknown. Methods. Perinatal outcome was compared between cases with a UA PI less than mean -2 SD (group A: high-risk cases selected from a database, n = 330; group B: unselected cases, n = 39) and unselected controls (group C) with a PI within mean ± 2 SD (n = 863) at Doppler velocimetry. Groups B and C were retrieved from a population-based sample. The unpaired t-test, Mann-Whitney U-test, chi2-test and Fisher's exact probability test were used for statistical comparisons with a two-tailed p < 0.05 being significant. Results. No significant differences were found between group A vs. group C and group B vs. group C regarding perinatal mortality, Apgar scores at 1, 5 or 10 min, or arterial or venous cord blood pH. Postterm pregnancy in group A carried no additional risk. For obvious reasons, operative delivery and neonatal intensive care were more common in group A than in group C, but no such differences were found between groups B and C. The mean birthweight was 3.7% higher in group B than in group C (p = 0.049). Conclusions. Deeming a UA PI below the lower reference limit as "abnormally" low is a statistical definition that was not reflected by a biological imperfection. Instead, a low UA PI promoted fetal growth

Design, Implementation and Verification Using UML-RT in GSM Radio Base Station 2000

This work deals with the issue of implementing a UML-RT standard, one of the latest notations for object oriented specification and design, in the developing-process of new real-time software for Radio Base Stations in the 2000 series at Ericsson. UML-RT is the real-time extension of the Unified Modeling Language (UML). The thesis investigates the design-, implementation- and verification-problems that exist when combining the current RT functions, Multi Platform Support, with the UML-RT tool, ObjecTime Developer. We describe UML, look at the advantages and disadvantages of using UML-RT tools and investigate current and future possibilities in ObjecTime Developer

Stem Quality Estimates Using Terrestrial Laser Scanning Voxelized Data and a Voting-Based Branch Detection Algorithm

A new algorithm for detecting branch attachments on stems based on a voxel approach and line object detection by a voting procedure is introduced. This algorithm can be used to evaluate the quality of stems by giving the branch density of each standing tree. The detected branches were evaluated using field-sampled trees. The algorithm detected 63% of the total amount of branch whorls and 90% of the branch whorls attached in the height interval from 0 to 10 m above ground. The suggested method could be used to create maps of forest stand stem quality data

Co-registration of single tree maps and data captured by a moving sensor using stem diameter weighted linking

A new method for the co-registration of single tree data in forest stands and forest plots applicable to static as well as dynamic data capture is presented. This method consists of a stem diameter weighted linking algorithm that improves the linking accuracy when operating on diverse diameter stands with stem position errors in the single tree detectors. A co-registration quality metric threshold, QT, is also introduced which makes it possible to discriminate between correct and incorrect stem map co-registrations with high probability (>99%). These two features are combined to a simultaneous location and mapping-based co-registration method that operates with high linking accuracy and that can handle sensors with drifting errors and signal bias. A test with simulated data shows that the method has an 89.35% detection rate. The statistics of different settings in a simulation study are presented, where the effect of stem density and position errors were investigated. A test case with real sensor data from a forest stand shows that the average nearest neighbor distances decreased from 1.90 m to 0.51 m, which indicates the feasibility of this method

Two-phase forest inventory using very-high-resolution laser scanning

In this study, we compared a two-phase laser-scanning-based forest inventory of stands versus a traditional field inventory using sample plots. The two approaches were used to estimate stem volume (VOL), Lorey's mean height (HL), Lorey's stem diameter (DL), and VOL per tree species in a study area in Sweden. The estimates were compared at the stand level with the harvested reference values obtained using a forest harvester. In the first phase, a helicopter acquired airborne laser scanning (ALS) data with >500 points/m2 along 50-m wide strips across the stands. These strips intersected systematic plots in phase two, where terrestrial laser scanning (TLS) was used to model DL for individual trees. In total, phase two included 99 plots across 10 boreal forest stands in Sweden (lat 62.9 degrees N, long 16.9 degrees E). The single trees were segmented in both the ALS and TLS data and linked to each other. The very-high-resolution ALS data enabled us to directly measure tree heights and also classify tree species using a convolutional neural network. Stem volume was predicted from the predicted DBH and the estimated height, using national models, and aggregated at the stand level. The study demonstrates a workflow to derive forest variables and stand-level statistics that has potential to replace many manual field inventories thanks to its time efficiency and improved accuracy. To evaluate the inventories, we estimated bias, RMSE, and precision, expressed as standard error. The laser-scanning-based inventory provided estimates with an accuracy considerably higher than the field inventory. The RMSE was 17 m3/ha (7.24%), 0.9 m (5.63%), and 16 mm (5.99%) for VOL, HL, and DL respectively. The tree species classification was generally successful and improved the three species-specific VOL estimates by 9% to 74%, compared to field estimates. In conclusion, the demonstrated laser-scanning-based inventory shows potential to replace some future forest inventories, thanks to the increased accuracy demonstrated empirically in the Swedish forest study area

Optimal Tracking and Identification of Paths for Industrial Robots

This paper presents results from time-optimal path tracking for industrial robots. More specifically, three subproblems are studied and experimentally evaluated. The first is a contact-force control approach for determining the geometric robot motion, such that the tool centre point of the robot is moved according to the specification. The second problem is off-line solution of the optimisation problem describing the time-optimal path tracking problem, by using software which allows highlevel implementation and solution of optimisation problems. The third problem is robust control of the robot during real-time path tracking based on the optimisation results determined off-line. An earlier developed control structure for robust control is implemented and tested in a robot system. This paper discusses the theory behind time-optimal path tracking and presents experimental results. Both contact-force controlled path identification and real-time path tracking of the identified path are evaluated on a 6-DOF industrial robot of type IRB140 from ABB

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

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