A transputer Based Laser Scanning System

This paper presents a transputer-based laser scanner. This is to be integrated into an existing transputer-based mariufacturing environment to allow rapid construction of' 3-0 models. The approach allows Z-gradient informaticm to be obtained from a 2-D image by illuminating areas of interest with a form of structured light. An active scanning system is described. Simple algorithms are applied to the raw image data to extract concise information. This foveal analysis greatly reduces the data to be processed, allowing a simple and fast method for analysis. The system primarily consists of a video camera wlhich obliquely views a scene being scanned by a laser. The principle, the procedure, and methods of scanning are described. An overview of the principles of foveal analysis, the prototype experimental system and initial results are presented

Laser-scanning techniques for rapid ballistics identification

Two different laser-scanning methods may be utilized. In each case scanned cylindrical bullet surface is displayed ""unwrapped'' on oscilloscope screen. Bullets are compared by photographing each display and superimposing negatives of two images. With some modifications bullets can be scanned and compared by superimposing images on screen of dual-beam oscilloscope

Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods

Estimation of forest variables using airborne laser scanning

Airborne laser scanning can provide three-dimensional measurements of the forest canopy with high efficiency and precision. There are presently a large number of airborne laser scanning instruments in operation. The aims of the studies reported in this thesis were, to develop and validate methods for estimation of forest variables using laser data, and to investigate the influence of laser system parameters on the estimates. All studies were carried out in hemi-boreal forest at a test area in southwestern Sweden (lat. 58°30’N, long. 13°40’ E). Forest variables were estimated using regression models. On plot level, the Root Mean Square Error (RMSE) for mean tree height estimations ranged between 6% and 11% of the average value for different datasets and methods. The RMSE for stem volume estimations ranged between 19% and 26% of the average value for different datasets and methods. On stand level (area 0.64 ha), the RMSE was 3% and 11% of the average value for mean tree height and stem volume estimations, respectively. A simulation model was used to investigate the effect of different scanning angles on laser measurement of tree height and canopy closure. The effect of different scanning angles was different within different simulated forest types, e.g., different tree species. High resolution laser data were used for detection of individual trees. In total, 71% of the field measurements were detected representing 91% of the total stem volume. Height and crown diameter of the detected trees could be estimated with a RMSE of 0.63 m and 0.61 m, respectively. The magnitude of the height estimation errors was similar to what is usually achieved using field inventory. Using different laser footprint diameters (0.26 to 3.68 m) gave similar estimation accuracies. The tree species Norway spruce (Picea abies L. Karst.) and Scots pine (Pinus sylvestris L.) were discriminated at individual tree level with an accuracy of 95%. The results in this thesis show that airborne laser scanners are useful as forest inventory tools. Forest variables can be estimated on tree level, plot level and stand level with similar accuracies as traditional field inventories

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

Microscopic Observation of Plastic Deformation of Polycrystalline Aluminum by Laser Scanning Microscope

Free surface of polycrystalline metal becomes roughened after plastic deformation. The surface roughening is closely related to the inhomogeneity of polycrystalline metals, that is, to the inhomogeneous plastic deformation of respective grains. In the present study, inhomogeneous deformation on the free surface of polycrystalline aluminum specimen during uniaxial tension is studied. The inhomogeneous deformation of grains in the central area of the free surface of specimen is observed by the laser scanning microscope, while the inhomogeneous deformation perpendicular to the surface is studied by the laser scanning microscope as well as the stylus measuring instrument. It is shown that the surface roughness and the strain of respective grains increase with the applied strain. Discussions are made on the change in the surface roughness, the strain in each grain and the slip-line angles with the applied strain

ScanImage: Flexible software for operating laser scanning microscopes

BACKGROUND: Laser scanning microscopy is a powerful tool for analyzing the structure and function of biological specimens. Although numerous commercial laser scanning microscopes exist, some of the more interesting and challenging applications demand custom design. A major impediment to custom design is the difficulty of building custom data acquisition hardware and writing the complex software required to run the laser scanning microscope. RESULTS: We describe a simple, software-based approach to operating a laser scanning microscope without the need for custom data acquisition hardware. Data acquisition and control of laser scanning are achieved through standard data acquisition boards. The entire burden of signal integration and image processing is placed on the CPU of the computer. We quantitate the effectiveness of our data acquisition and signal conditioning algorithm under a variety of conditions. We implement our approach in an open source software package (ScanImage) and describe its functionality. CONCLUSIONS: We present ScanImage, software to run a flexible laser scanning microscope that allows easy custom design

Assessing the performance of a handheld laser scanning system for individual tree mapping—A mixed forests showcase in Spain

Producción CientíficaThe use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser Scanning (HLS) system to estimate tree attributes within a forest patch in central Spain. We describe the different stages of the HLS approach: field position, ground data collection, scanning path design, point cloud processing, alignment between detected trees and measured reference trees, and finally, the assessment of main tree structural attributes diameter at breast height (DBH) and tree height considering species and tree size as control factors. We surveyed 418 reference trees to account for omission and commission error rates over a 1 ha plot divided into 16 sections and scanned using two different scanning paths. The HLS-based approach reached a high of 88 and 92% tree detection rate for the best combination of scanning path and point cloud processing modes for the HLS system. The root mean squared errors for DBH estimates varied between species: errors for Pinus pinaster were below 2 cm for Scan 02. Quercus pyrenaica, and Alnus glutinosa showed higher error rates. We observed good agreement between ALS and HLS estimates for tree height, highlighting differences to field measurements. Despite the complexity of the mixed forest area surveyed, our results show that HLS is highly efficient at detecting tree locations, estimating DBH, and supporting tree height measurements as confirmed with airborne laser data used for validation. This study is one of the first HLS-based studies conducted in the Mediterranean mixed forest region, where variability in tree allometries and spacing and the presence of natural regeneration pose challenges for the HLS approach. HLS is a feasible, time-efficient, scalable technology for tree mapping in mixed forests with potential to support forest monitoring programmes such as national forest inventories lacking three-dimensional, remote sensing data to support field measurements.European Union’s Horizon 2020 and Innovation Program Marie Skłodowska-Curie - (Grant 956355)Junta de Castilla y León y Fondo Europeo de Desarrollo Regional (FEDER) - (projects “CLU‑2019‑01 and CL‑EI‑2021‑05—iuFOR Institute Unit of Excellence”)Fondo Europeo de Desarrollo Regional (FEDER), project Interreg COMFOR‑SUDOE - (grant SOE4/P1/E1012