Determining forest canopy characteristics using airborne laser data

A pulsed laser system was flown over a forested area in Pennsylvania which exhibited a wide range of canopy closure conditions. The lasing system acts as the ultraviolet light equivalent of radar, sensing not only the distance to the top of the forest canopy, but also the range to the forest floor. The data were analyzed to determine which components of the laser data could explain the variability in crown closure along the flight transect. Results indicated that canopy closure was most strongly related to the penetration capability of the laser pulse. Pulses were attenuated more quickly in a dense canopy. Hence the inability to find a strong ground return in the laser data after initially sensing the top of the canopy connoted dense canopy cover. Photogrammetrically acquired tree heights were compared to laser estimates; average heights differed by less than 1 m. The results indicated that the laser system may be used to remotely sense the vertical forest canopy profile. Elements of this profile are linearly related to crown closure and may be used to assess tree height.     

Use of Large-Footprint Scanning Airborne Lidar To Estimate Forest Stand Characteristics in the Western Cascades of Oregon

A scanning lidar, a relatively new type of sensor which explicitly measures canopy height, was used to measure structure of conifer forests in the Pacific Northwest. SLICER (Scanning Lidar Imager of Canopies by Echo Recovery), an airborne pulsed laser developed by NASA which scans a swath of five 10-m diameter footprints along the aircraft’s flightpath, captures the power of the reflected laser pulse as a function of height from the top of the canopy to the ground. Ground measurements of forest stand structure were collected on 26 plots with coincident SLICER data. Height, basal area, total biomass, and leaf biomass as estimated from field data could be predicted from SLICER-derived metrics with r² values of 0.95, 0.96, 0.96, and 0.84, respectively. These relationships were strong up to a height of 52 m, basal area of 132 m²/ha and total biomass of 1300 Mg/ha. In light of these strong relationships, large-footprint, airborne scanning lidar shows promise for characterizing stand structure for management and research purposes.     

Modeling forest canopy heights: The effects of canopy shape

Three-dimensional models that represent the top-of-canopy forest height structure were developed to simulate airborne laser profiling responses along forested transects. The simulator which produced these 3-D models constructed individual tree crowns according to a tree's total height, height to first branch, crown diameter, and crown shape (cone, parabola, ellipse, sphere, or a random assortment of these shapes), and then inserted these crowns into a fixed-area plot using mapped stand (x,y) coordinates. This two-dimensional array of forest canopy heights was randomly transected to simulate measurements made by an airborne ranging laser. These simulated laser measurements were regressed with ground reference measures to develop predictive linear relationships. The assumed crown shape had a significant impact on 1) simulated laser measurements of height and 2) estimates of basal area, woody volume, and above-ground dry biomass derived via simulation. As canopy shape progressed from a conic form to a more spheric structure, average canopy height, canopy profile area, and canopy volume increased, canopy height variation decreased, and coefficients of variability were stable or decreased. In Costa Rican tropical forests, simulated laser measurements of average height, canopy profile area, and canopy volume increased 8–10% when a parabolic rather than a conic shape was assumed. An elliptic canopy was 16–18% taller, on average, than a conic canopy, and a spheric canopy was 23–25% taller. The effect of these height increases and height variability changes can profoundly affect basal area, volume, and biomass estimates, but the degree to which these estimates are affected is study-area-dependent. Since canopy shape may significantly affect such estimates, canopy shapes should be noted when field data are collected for purposes of height simulation. If canopy shapes are not noted and are unknown, an assumption of an elliptical shape is suggested in order to mitigate potentially large errors which may be incurred using a generic assumption of a cone or sphere.     

Separating the ground and airborne laser sampling phases to estimate tropical forest basal area, volume, and biomass

Airborne laser profiling data were used to estimate the basal area, volume, and biomass of primary tropical forests. A procedure was developed and tested to divorce the laser and ground data collection efforts using three distinct data sets acquired in and over the tropical forests of Costa Rica. Fixed-area ground plot data were used to simulate the height characteristics of the tropical forest canopy and to simulate laser measurements of that canopy. On two of the three study sites, the airborne laser estimates of basal area, volume, and biomass grossly misrepresented ground estimates of same. On the third study site, where the widest ground plots were utilized, airborne and ground estimates agreed within 24%. Basal area, volume, and biomass prediction inaccuracies in the first two study areas were tied directly to disagreements between simulated laser estimates and the corresponding airborne measurements of average canopy height, height variability, and canopy density. A number of sampling issues were investigated; the following results were noted in the analyses of the three study areas. 1) Of the four ground segment lengths considered (25 m, 50 m, 75 m, and 100 m), the 25 m segment length introduced a level of variability which may severely degrade prediction accuracy in these Costa Rican primary tropical forests. This effect was more pronounced as plot width decreased. A minimum segment length was on the order of 50 m. 2) The decision to transform or not to transform the dependent variable (e.g., biomass) was by far the most important factor of those considered in this experiment. The natural log transformation of the dependent variable increased prediction error, and error increased dramatically at the shorter segment lengths. The most accurate models were multiple linear models with forced zero intercept and an untransformed dependent variable. 3) General linear models were developed to predict basal area, volume, and biomass using airborne laser height measurements. Useful laser measurements include average canopy height, all pulses ( _a), average canopy height, canopy hits ( _c) and the coefficients of variation of these terms (c_a and c_c). Coefficients of determination range from 0.4 to 0.6. Based on this research, airborne laser and ground sampling procedures are proposed for use for reconnaissance level surveys of inaccessible forested regions.     

Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index

Remote sensing of forest canopy cover has been widely studied recently, but little attention has been paid to the quality of field validation data. Ecological literature has two different coverage metrics. Vertical canopy cover (VCC) is the vertical projection of tree crowns ignoring within-crown gaps. Angular canopy closure (ACC) is the proportion of covered sky at some angular range around the zenith, and can be measured with a field-of-view instrument, such as a camera. We compared field-measured VCC and ACC at 15° and 75° from the zenith to different LiDAR (Light Detection and Ranging) metrics, using several LiDAR data sets and comprehensive field data. The VCC was estimated to a high precision using a simple proportion of canopy points in first-return data. Confining to a maximum 15° scan zenith angle, the absolute root mean squared error (RMSE) was 3.7-7.0%, with an overestimation of 3.1-4.6%. We showed that grid-based methods are capable of reducing the inherent overestimation of VCC. The low scan angles and low power settings that are typically applied in topographic LiDARs are not suitable for ACC estimation as they measure in wrong geometry and cannot easily detect small within-crown gaps. However, ACC at 0-15° zenith angles could be estimated from LiDAR data with sufficient precision, using also the last returns (RMSE 8.1-11.3%, bias -6.1-+4.6%). The dependency of LiDAR metrics and ACC at 0-75° zenith angles was nonlinear and was modeled from laser pulse proportions with nonlinear regression with a best-case standard error of 4.1%. We also estimated leaf area index from the LiDAR metrics with linear regression with a standard error of 0.38. The results show that correlations between airborne laser metrics and different canopy field characteristics are very high if the field measurements are done with equivalent accuracy.     

Height growth reconstruction of a boreal forest canopy over a period of 58 years using a combination of photogrammetric and lidar models

Field data describing the height growth of trees or stands over several decades are very scarce. Consequently, our capacity of analyzing forest dynamics over large areas and long periods of time is somewhat limited. This study proposes a new method for retrospectively reconstructing plot-wise average dominant tree height based on a time series of high-resolution canopy height maps, termed canopy height models (CHMs). The absolute elevation of the canopy surface, or digital surface model (DSM), was first reconstructed by applying image-matching techniques to stereo-pairs of aerial photographs acquired in 1945, 1965, 1983, and 2003. The historical CHMs were then created by subtracting the bare earth elevation provided from a recent lidar survey from the DSMs. A method for estimating average dominant tree height from these historical CHMs was developed and calibrated for each photographic year. The accuracy of the resulting remote sensing height estimates was compared to age-height data reconstructed based on dendrometric measurements. The height bias of the remote sensing estimates relative to the verification data ranged from 0.52 m to 1.55 m (1.16 m on average). The corresponding root-mean-square errors varied between 1.49 m and 2.88 m (2.03 m average). Despite being slightly less accurate than historical field data, the quality of the remote sensing estimates is sufficient for many types of forest dynamics studies. The procedures for implementing this method, with the exception of the calibration phase, are entirely automated such that forest height growth curves can be reconstructed and mapped over large areas for which recent lidar data and historical photographs exist.     

Technical note: Canopy height models and airborne lasers to estimate forest biomass: Two problems

Three ground datasets were used to simulate the canopy height characteristics of tropical forests in Costa Rica for the purposes of forest biomass estimation. The canopy height models (CHMs) were used in conjunction with airborne laser data. Gross biomass estimation errors on the order of 50-90% arose in two of the three analyses. The characteristics of the datasets and the biomass estimation procedure are reviewed to identify sources of error. In one dataset, the width of the fixed-area ground plots were small enough (5 m) that significant portions of the overstory canopy above the plots were not accounted for in the ground samples. The use of mapped stand data from thin ground plots resulted in inaccurate CHMs, which in turn lead to gross overestimates of forest biomass (about 90% larger than the ground reference value). CHMs generated using mensuration data collected on thin, fixed-area plots may significantly underestimate the average canopy height and crown closure actually found on that plot. This underestimation problem is directly related to plot width. Below a critical threshold, the thinner the plot, the greater the underestimation bias. In the second dataset, it is believed that lower-than-normal rainfalls at the beginning and end of the wet season may have produced a forest canopy with reduced leaf area. The airborne laser pulses penetrated further into the canopy, resulting in airborne laser estimates of forest biomass which grossly underestimated reference values by about 50%. Changing canopy conditions (e.g. leaf loss due to drought, insect defoliation, storm damage) can affect the accuracy of a CHM. Sources of error are reported in order to forewarn those researchers who produce and utilize canopy height models.     

Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data

The Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat) is the first spaceborne lidar instrument for continuous global observation of the Earth. GLAS records a vertical profile of the returned laser energy from its footprint. To help understand the application of the data for forest spatial structure studies in our regional projects, an evaluation of the GLAS data was conducted using NASA's Laser Vegetation Imaging Sensor (LVIS) data in an area near NASA's Goddard Space Flight Center in Greenbelt, Maryland, USA. The tree height indices from airborne large-footprint lidars such as LVIS have been successfully used for estimation of forest structural parameters in many previous studies and served as truth in this study.The location accuracy of the GLAS footprints was evaluated by matching the elevation profile from GLAS with the Shuttle Radar Topography Mission (SRTM) DEM. The results confirmed the location accuracy of the GLAS geolocation, and showed a high correlation between the height of the scattering phase center from SRTM and the top tree height from GLAS data. The comparisons between LVIS and GLAS data showed that the GLAS waveform is similar to the aggregation of the LVIS waveforms within the GLAS footprint, and the tree height indices derived from the GLAS and LVIS waveforms were highly correlated. The best correlations were found between the 75% waveform energy quartiles of LVIS and GLAS (r² = 0.82 for October 2003 GLAS data, and r² = 0.65 for June 2005 GLAS data). The correlations between the 50% waveform energy quartiles of LVIS and GLAS were also high (0.77 and 0.66 respectively). The comparisons of the top tree height and total length of waveform of the GLAS data acquired in fall of 2003 and early summer of 2005 showed a several meter bias. Because the GLAS footprints from these two orbits did not exactly overlap, several other factors may have caused this observed difference, including difference of forest structures, seasonal difference of canopy structures and errors in identifying the ground peak of waveforms.     

Effects of different flying altitudes on biophysical stand properties estimated from canopy height and density measured with a small-footprint airborne scanning laser

Canopy height distributions were created from small-footprint airborne laser scanner data collected over 133 georeferenced field sample plots and 56 forest stands located in young and mature forest. The plot size was 300-400 m² and the average stand size was 1.7 ha. Spruce and pine were the dominant tree species. Canopy height distributions were created from both first and last pulse data. The laser data were acquired from two different flying altitudes, i.e., 530-540 and 840-850 m above ground. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were computed from the laser-derived canopy height distributions. Corresponding metrics derived from the two different flying altitudes were compared. Only 1 of 54 metrics derived from the first pulse data differed significantly between flying altitudes. For the last pulse data, the mean values of the height percentiles were up to 50 cm higher than the corresponding values of the low-altitude data. The high-altitude data yielded significantly higher values for most of the canopy density measures. The standard deviation for the differences between high and low flying altitude for each of the metrics was estimated. The standard deviations for the height percentiles ranged from 0.07 to 0.30 cm in the forest stands, indicating a large degree of stability between repeated flight overpasses.The effect of variable flying altitude on mean tree height (h_L), stand basal area (G), and stand volume (V) estimated from the laser-derived height and density measures using a two-stage inventory procedure was assessed by randomly combining laser data from the two flying altitudes for each individual sample plot and forest stand. The sample plots were used as training data to calibrate the models. The random assignment was repeated 10,000 times. The results of the 10,000 trials indicated that the precision of the estimated values of h_L, G, and V was robust against alterations in flying altitude.     

Simulating the impact of discrete-return lidar system and survey characteristics over young conifer and broadleaf forests

We present a model-based investigation of the effect of discrete-return lidar system and survey characteristics on the signal recorded over young forest environments. A Monte Carlo ray tracing (MCRT) model of canopy scattering was used to examine the sensitivity of model estimates of lidar-derived canopy height, h_lidar to signal triggering method, canopy structure, footprint size, sampling density and scanning angle, for broadleaf and conifer canopies of varying density. Detailed 3D models of Scots pine (Pinus sylvestris) and Downy birch (Betula pubescens) were used to simulate lidar response, with minimal assumptions about canopy structure. Use of such models allowed the impact of lidar parameters on canopy height retrieval to be tested under a range of conditions typically not possible in practice. Retrieved h_lidar was generally found to be an underestimate of ‘true’ canopy height, h_canopy, but with exceptions. Choice of signal triggering method caused h_lidar to underestimate h_canopy by ∼ 4% for birch and ∼ 7% for pine (up to 66% in extreme cases). Variations in canopy structure resulted on average in underestimation of h_canopy by 13% for birch and between 29 and 48% for pine depending on age, but with over-estimates in some cases of up to 10%. Increasing footprint diameter from 0.1 to 1 m increased retrieved h_lidar from significant underestimates of h_canopy to values indistinguishable from h_canopy. Increased sampling density led to slightly increased values of h_lidar to close to h_canopy, but not significantly. Increasing scan angle increased h_lidar by up to 8% for birch, and 19% for pine at a scan angle of 30°. The impact of scan angle was greater for conifers as a result of large variation in crown height. Results showed that interactions between physically modelled (hypothetical) within canopy returns are similar to findings made in other studies using actual lidar systems, and that these modelled returns can depend strongly on the type of canopy and the lidar acquisition characteristics, as well as interactions between these properties. Physical models of laser pulse/canopy interactions may provide additional information on pulse interactions within the canopy, but require validation and testing before they are applied to actual survey planning and logistics.     

Forest canopy recovery from the 1938 hurricane and subsequent salvage damage measured with airborne LiDAR

The structure of a forest canopy often reflects its disturbance history. Such signatures of past disturbances or legacies can influence how the ecosystem functions across broad spatio-temporal scales. The 1938 hurricane and ensuing salvage operations which swept through New England represent the most recent large, infrequent disturbance (LID) in this region. Though devastating (downing ∼ 70% of the timber at Harvard Forest), the disturbance was not indiscriminate; it left behind a heterogeneous landscape comprised of different levels of canopy damage. We analyzed large-footprint LiDAR, from the Prospect Hill tract at Harvard Forest in central Massachusetts, to assess whether damage to the forest structure from the hurricane and subsequent timber extraction could be discerned after ∼ 65 years. Differences in LiDAR-derived measures of canopy height and vertical diversity were a function of the degree of damage from the 1938 hurricane and the predominant tree species which is, in part, a function of land use history. Higher levels of damage corresponded to slightly shorter canopies with a less even vertical distribution of return from the ground to the top. In addition, differences in canopy topography as revealed by spatial autocorrelation of canopy top heights were found among the damage classes. Less disturbed stands were characterized by lower levels of local autocorrelation for canopy height and higher levels of vertical diversity of LiDAR returns. These differences in canopy structure reveal that the forest tract has not completely recovered from the 1938 LID and salvage regime, which may have implications on arboreal and understory habitat and other ecosystem functions.     

基于无人机激光雷达的森林冠层高度分析

快速准确获取森林结构参数对森林资源调查管理及全球碳汇研究具有重要意义。以祁连山东、中部青海云杉林为研究对象,利用16个无人机激光雷达（LiDAR）点云数据、正射影像数据结合实地样方观测数据,提取样方内青海云杉的单木树高并准确验证树木分割精度;结合实测数据和地形数据,依据统计指标验证提取树高精度并分析原因;基于点云数据提取的各样方树高分析祁连山青海云杉冠层高度在空间上的变化。结果表明：在祁连山山地森林,冠层高度平均值估算精度最高,R²为0.93,RMSE为1.39 m（P<0.05）;地形影响基于点云数据的树高提取,坡度较小的青海云杉树高提取效果更好;从东到西,青海云杉平均树高呈下降趋势;随着海拔高度上升,青海云杉的平均树高先上升后下降,这与祁连山东西水热条件差异和不同海拔树木年龄分布有关。     

Optimization of Geoscience Laser Altimeter System waveform metrics to support vegetation measurements

The Geoscience Laser Altimeter System (GLAS) has collected over 250 million measurements of vegetation height over forests globally. Accurate vegetation heights can be determined using waveform metrics that include vertical extent and extent of the waveform's trailing and leading edges. All three indices are highly dependent upon the signal strength, background noise and signal-to-noise ratio of the waveform, as the background noise contribution to the waveforms has to be removed before their calculation. Over the last six years, GLAS has collected data during thirteen observation periods using illumination from three different lasers. The power levels of these lasers have changed over time, resulting in variable signal power and noise characteristics. Atmospheric conditions vary continuously, also influencing signal power and noise.To minimize these effects, we optimized a noise coefficient which could be constant or vary according to observation period or noise metric. This parameter is used with the mean and standard deviation of the background noise to determine a noise level threshold that is removed from each waveform. An optimization analysis was used with a global dataset of waveforms that are near-coincident with waveforms from other observation periods; the goal of the optimization was to minimize the difference in vertical extent between spatially overlapping GLAS observations. Optimizations based on absolute difference in height led to situations in which the total extent was minimized as well; further optimizations reduced a normalized difference in height extent. The simplest optimizations were based on a constant value to be applied to all observations; noise coefficients of 2.7, 3.2, 3.4 and 4.0 were determined for datasets consisting of global forests, global vegetation, forest in the legal Amazon basin and boreal forests respectively. Optimizations based on the power level or the signal-to-noise ratio of waveforms best minimized differences in waveform extent, decreasing the percent root mean squared height difference by 25-54% over the constant value approach. Further development of methods to ensure temporal consistency of waveform indices will be necessary to support long-term satellite lidar missions and will result in more accurate and precise estimates of canopy height.     

Measurement of tropical rainforest three-dimensional light environment and its diurnal change

A key link to understand the relationship between tropical forest canopy physiology and remote-sensing technology is forest light distribution. Accordingly, we conducted three-dimensional light environment measurements to acquire a quantitative and qualitative understanding of tropical rainforests at Lambir Hills National Park, Sarawak, Borneo, Malaysia, as a typical tropical rainforest region. The aim of this study was to elucidate links between canopy physiological studies and remote-sensing technology, and to investigate the up-scaling capability of canopy processes and mechanisms. Measurements were conducted using a ground-based laser profiler system and terrestrial survey instrument. For understanding qualitative forest characteristics, both reflective and transmissive light measurements were conducted, using photon and quantum sensors and a spectroradiometer. Our results suggest there is no great difference in canopy height, when all trees are mature, are interdependent and are employing the same growth strategies. Photosynthetic active radiation attenuation showed no changes over time.     

Development of a simulation model to predict LiDAR interception in forested environments

Airborne scanning LiDAR systems are used to predict a range of forest attributes. However, the accuracy with which this can be achieved is highly dependent on the sensor configuration and the structural characteristics of the forest examined. As a result, there is a need to understand laser light interactions with forest canopies so that LiDAR sensor configurations can be optimised to assess particular forest types. Such optimisation will not only ensure the targeted forest attributes can be accurately and consistently quantified, but may also minimise the cost of data acquisition and indicate when a survey configuration will not deliver information needs.In this paper, we detail the development and application of a model to simulate laser interactions within forested environments. The developed model, known as the LiDAR Interception and Tree Environment (LITE) model, utilises a range of structural configurations to simulate trees with variable heights, crown dimensions and foliage clumping. We developed and validated the LITE model using field data obtained from three forested sites covering a range of structural classes. Model simulations were then compared to coincident airborne LiDAR data collected over the same sites. Results indicate that the LITE model can be used to produce comparable estimates of maximum height of trees within plots (differences < 2.42 m), mean heights of first return data (differences < 2.27 m), and canopy height percentiles (r² = 0.94, p < 0.001) when compared to airborne LiDAR data. In addition, the distribution of airborne LiDAR hits through the canopy profile was closely matched by model predictions across the range of sites. Importantly, this demonstrates that the structural differences between forest stands can be characterised by LITE. Models that are capable of interpreting the response of small-footprint LiDAR waveforms can facilitate algorithm development, the generation of corrections for actual LiDAR data, and the optimisation of sensor configurations for differing forest types, benefiting a range of experimental and commercial LiDAR applications. As a result, we also performed a scenario analysis to demonstrate how differences in forest structure, terrain, and sensor configuration can influence the interception of LiDAR beams.     

Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains,California

Treatments to reduce forest fuels are often performed in forests to enhance forest health, regulate stand density, and reduce the risk of wildfires. Although commonly employed, there are concerns that these forest fuel treatments (FTs) may have negative impacts on certain wildlife species. Often FTs are planned across large landscapes, but the actual treatment extents can differ from the planned extents due to operational constraints and protection of resources (e.g. perennial streams, cultural resources, wildlife habitats). Identifying the actual extent of the treated areas is of primary importance to understand the environmental influence of FTs. Light detection and ranging (lidar) is a powerful remote-sensing tool that can provide accurate measurements of forest structures and has great potential for monitoring forest changes. This study used the canopy height model (CHM) and canopy cover (CC) products derived from multi-temporal airborne laser scanning (ALS) data to monitor forest changes following the implementation of landscape-scale FT projects. Our approach involved the combination of a pixel-wise thresholding method and an object-of-interest (OBI) segmentation method. We also investigated forest change using normalized difference vegetation index (NDVI) and standardized principal component analysis from multi-temporal high-resolution aerial imagery. The same FT detection routine was then applied to compare the capability of ALS data and aerial imagery for FT detection. Our results demonstrate that the FT detection using ALS-derived CC products produced both the highest total accuracy (93.5%) and kappa coefficient (κ) (0.70), and was more robust in identifying areas with light FTs. The accuracy using ALS-derived CHM products (the total accuracy was 91.6%, and the κ was 0.59) was significantly lower than that using ALS-derived CC, but was still higher than using aerial imagery. Moreover, we also developed and tested a method to recognize the intensity of FTs directly from pre- and post-treatment ALS point clouds.     

Direct estimation of noisy sinusoids using abductive networks

Spectral estimation techniques have been used for many years. In many cases, their complexity warrants investigating machine-learning alternatives where intensive computations are required only during training, with actual estimation simplified and speeded up. This allows using simple portable apparatus for fast and automated estimation in real time. We propose using abductive network machine learning for estimating both the amplitude and frequency of a single sine wave in the presence of additive Gaussian noise. Models synthesized by training on 1000 representative simulated sinusoids were evaluated on 500 new cases. With no phase variations and a signal to noise ratio of 7 dB, average absolute percentage errors for the sinusoid amplitude and period are 8.4% and 3.6%, respectively. Effects of the range of frequency variations and the noise level on the complexity and accuracy of the models were investigated. Amplitude and period estimates show signs of bias at a signal to noise ratio of 3 dB. Error variances track the Cramer-Rao bounds at high noise levels, with no thresholding observed down to 0 dB. The method is compared with a neural network model and with conventional discrete Fourier transform (DFT) based techniques and a Prony's based approach. The new approach is particularly useful when only a small portion of the sinusoid cycle is measured.     

An image-based feature tracking algorithm for real-time measurement of clad height

This paper presents a novel algorithm for real-time detection of clad height in laser cladding which is known as a layered manufacturing technique. A real-time measurement of clad geometry is based on the use of a developed trinocular optical detector composed of three CCD cameras and the associated interference filters and lenses. The images grabbed by the trinocular optical detector are fed into an algorithm which combines an image-based tracking protocol and a recurrent neural network to extract the clad height in real-time. The image feature tracking strategy is a synergy between a simple image selecting protocol, a fuzzy thresholding technique, a boundary tracing method, a perspective transformation and an extraction of elliptical features of the projected melt pool’s images. The proposed algorithm and the trained network were utilized in the process resulting in excellent detection of the clad height at various working conditions in which SS303L was deposited on mild steel. It was concluded that the developed system can detect the clad height independent from clad paths with about 12% maximum error.     

Impact of footprint diameter and off-nadir pointing on the precision of canopy height estimates from spaceborne lidar

A spaceborne lidar mission could serve multiple scientific purposes including remote sensing of ecosystem structure, carbon storage, terrestrial topography and ice sheet monitoring. The measurement requirements of these different goals will require compromises in sensor design. Footprint diameters that would be larger than optimal for vegetation studies have been proposed. Some spaceborne lidar mission designs include the possibility that a lidar sensor would share a platform with another sensor, which might require off-nadir pointing at angles of up to 16°. To resolve multiple mission goals and sensor requirements, detailed knowledge of the sensitivity of sensor performance to these aspects of mission design is required.This research used a radiative transfer model to investigate the sensitivity of forest height estimates to footprint diameter, off-nadir pointing and their interaction over a range of forest canopy properties. An individual-based forest model was used to simulate stands of mixed conifer forest in the Tahoe National Forest (Northern California, USA) and stands of deciduous forests in the Bartlett Experimental Forest (New Hampshire, USA). Waveforms were simulated for stands generated by a forest succession model using footprint diameters of 20 m to 70 m. Off-nadir angles of 0 to 16° were considered for a 25 m diameter footprint diameter.Footprint diameters in the range of 25 m to 30 m were optimal for estimates of maximum forest height (R² of 0.95 and RMSE of 3 m). As expected, the contribution of vegetation height to the vertical extent of the waveform decreased with larger footprints, while the contribution of terrain slope increased. Precision of estimates decreased with an increasing off-nadir pointing angle, but off-nadir pointing had less impact on height estimates in deciduous forests than in coniferous forests. When pointing off-nadir, the decrease in precision was dependent on local incidence angle (the angle between the off-nadir beam and a line normal to the terrain surface) which is dependent on the off-nadir pointing angle, terrain slope, and the difference between the laser pointing azimuth and terrain aspect; the effect was larger when the sensor was aligned with the terrain azimuth but when aspect and azimuth are opposed, there was virtually no effect on R² or RMSE. A second effect of off-nadir pointing is that the laser beam will intersect individual crowns and the canopy as a whole from a different angle which had a distinct effect on the precision of lidar estimates of height, decreasing R² and increasing RMSE, although the effect was most pronounced for coniferous crowns.     

Laser-based detection and tracking of multiple people in crowds

Laser-based people tracking systems have been developed for mobile robotic, and intelligent surveillance areas. Existing systems rely on laser point clustering method to extract object locations. However, for dense crowd tracking, laser points of different objects are often interlaced and undistinguishable due to measurement noise and they can not provide reliable features. It causes current systems quite fragile and unreliable. This paper presents a novel and robust laser-based dense crowd tracking method. Firstly, we introduce a stable feature extraction method based on accumulated distribution of successive laser frames. With this method, the noise that generates split and merged measurements is smoothed away and the pattern of rhythmic swing legs is utilized to extract each leg of persons. And then, a region coherency property is introduced to construct an efficient measurement likelihood model. The final tracker is based on the combination of independent Kalman filter and Rao-Blackwellized Monte Carlo data association filter (RBMC-DAF). In real experiments, we obtain raw data from multiple registered laser scanners, which measure two legs for each people on the height of 16 cm from horizontal ground. Evaluation with real data shows that the proposed method is robust and effective. It achieves a significant improvement compared with existing laser-based trackers. In addition, the proposed method is much faster than previous works, and can overcome tracking errors resulted from mixed data of two closely situated persons.