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.     

Automatic building height extraction by volumetric shadow analysis of monoscopic imagery

This article presents a new approach to automatic extraction of building heights from monoscopic urban scenes. A volumetric shadow analysis (VSA) method was proposed previously for extraction of 3D building information (height, shape, and footprint location) and for handling occluded building footprints or shadows. It determined building heights by adjusting building height manually until the projected shadows generated for an assumed height and actual shadows in the image matched. In this article, we propose an intelligent scheme based on the VSA for automatic building height extraction. We achieve this by checking the location change of projected shadow lines with respect to the actual shadow regions while building heights are increased incrementally. In this article, the performance of the proposed automatic height extraction was compared to that of manual extraction. The method was first applied to IKONOS, KOMPSAT-2, QuickBird, and Worldview-1 images with manually extracted building roofs. The root mean square error (RMSE) of building heights was under 3 m by automatic height extraction and 2 m by manual extraction. The RMSE of building footprint location was close to twice that of image ground sample distance (GSD) by automatic height extraction and under twice that of image GSD by manual extraction. These results support the capability of the proposed method in automatic height extraction from a single image efficiently and accurately, and in handling occluded building footprints and shadows. Second, the method was combined with an existing roof extraction method and tested for automated building roof extraction. The results showed that the proposed method can also provide a powerful cue for automatic building roof extraction from a single image.     

Building facade detection,segmentation, and parameter estimation for mobile robot stereo vision

Building facade detection is an important problem in computer vision, with applications in mobile robotics and semantic scene understanding. In particular, mobile platform localization and guidance in urban environments can be enabled with accurate models of the various building facades in a scene. Toward that end, we present a system for detection, segmentation, and parameter estimation of building facades in stereo imagery. The proposed method incorporates multilevel appearance and disparity features in a binary discriminative model, and generates a set of candidate planes by sampling and clustering points from the image with Random Sample Consensus (RANSAC), using local normal estimates derived from Principal Component Analysis (PCA) to inform the planar models. These two models are incorporated into a two-layer Markov Random Field (MRF): an appearance- and disparity-based discriminative classifier at the mid-level, and a geometric model to segment the building pixels into facades at the high-level. By using object-specific stereo features, our discriminative classifier is able to achieve substantially higher accuracy than standard boosting or modeling with only appearance-based features. Furthermore, the results of our MRF classification indicate a strong improvement in accuracy for the binary building detection problem and the labeled planar surface models provide a good approximation to the ground truth planes.     

3D reconstruction of buildings from LiDAR data considering various types of roof structures

In this article, a different approach has been proposed to detect and reconstruct buildings in 3D space. In this regard, some potentially primary features were produced and a genetic algorithm (GA) was employed in order to select the optimum features. The selected features were utilized to detect the building by using k-nearest neighbour (k-NN) algorithm. The detection results were used as inputs of reconstruction procedure. The proposed approach for 3D reconstruction consists of three main steps: roof planes were separated in the first step. Then, the corners of each plane boundary were extracted in order to provide the roof reconstruction possibility. Finally, the walls were reconstructed and merged to roofs and the final 3D model was obtained. Results evaluation indicated that the average value of buildings detection in the test areas was 87.84% in Quality. Moreover, the average value of buildings reconstruction in the test areas was 76.95% in object based Quality, and 95.66% in Quality of building planes that were greater than 25 m², respectively. Also, the average of altimetric and planimetric RMS of the test areas were 0.3 m and 0.75 m, respectively.     

Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach

The increasing availability of open geospatial data, such as building footprint vector data and LiDAR (Light Detection and Ranging) point clouds, has provided opportunities to generate large-scale 3D city models at low cost. However, using unclassified point clouds with building footprints to estimate building heights may yield erroneous results due to potential errors and anomalies in both datasets and their integration. Some of the points within footprints often reflect irrelevant objects other than roofs, leading to biases in height estimation, and few studies have developed systematic methods to filter them out. In this paper, a LiDAR point classification methodology is proposed that extracts only rooftop points for building height estimation. The LiDAR points are characterized by point, footprint, and neighborhood-based features and classified by the Random Forest (RF) algorithm into four classes – rooftop, wall, ground, and high outlier. The percentage of correctly classified points among 15,577 sample points in Columbus, Ohio, amounts to 96.5%. Conducting this classification separately for different building types (commercial, residential, skyscraper, and small constructions) does not significantly change the overall accuracy. The footprint-based features contribute most to predicting the classes correctly. Height validation results based on a sample of 498 buildings show that (1) using average or median heights with classified points provides the best estimates, minimizing the disparities between computed heights and ground truth and (2) the RF method yields outcomes much closer to ground truth than earlier classification approaches. Some outcomes are visualized in 3D format using Google Earth 3D Imagery and ArcScene.     

Effects of Different Spatial Resolution of Remote SensingImages on Estimation Accuracy of Urban Building Height

In this study,the remote sensing images of WorldView-2,GF-2,and GF-1,which cover Xiamen Software Park,were selected for study.A building and shadow extraction process suitable for different images was constructed,which applied object\|oriented approach and morphology ideas combined with spectral,shadow and shape constraints.Subsequently,the building heights of three different spatial resolutions of 0.5 m,1 m and 2 m were estimated by using the shadow length estimation method.Finally,the influence of image spatial resolution on building extraction accuracy and building height estimation accuracy was evaluated quantitatively.The main conclusions are as follows：(1) The improved building and shadow extraction process achieves higher extraction accuracy,but accuracy decreases slightly with the decrease of spatial resolution of images;(2) With the decrease of spatial resolution,the accuracy of building height estimation decreases gradually,but it does not show linear relationship.At the resolution increases from 1m to 0.5 m,the accuracy of building height estimation increases faster than the resolution increases from 2 m to 1 m;(3) GF-1 is more suitable for height estimation of high\|rise buildings and GF-2 is suitable for middle and high rise buildings,while WorldView\|2 has higher estimation accuracy for building height in different height ranges.     

DEM generation and error analysis using the first Chinese airborne dual-antenna interferometric SAR data

Terrain survey with traditional photogrammetry is often difficult in western China, such as Qingzang tableland at an average height of 5000 m above sea level and the southwest China area with cloudy weather. To resolve western terrain mapping, the first Chinese single-pass airborne Interferometric Synthetic Aperture Radar (InSAR) system was successfully developed by the Institute of Electronics, Chinese Academy of Sciences (IECAS) in 2004. The main objective of this article is to examine and evaluate the performance of the airborne SAR system through interferometric processing and error analysis. First, the article describes how high-precision digital elevation models (DEMs) are derived from the airborne dual-antenna (single-pass) InSAR data. In order to improve the precision, the antenna eccentricity correction and parameter calibration with the least square method (LSM) are proposed. Based on the airborne dual-antenna InSAR bore-sight model, this article summarizes the primary factors that influence the accuracy of DEMs in data processing, and analyses the errors induced by these factors. Then, the global positioning system (GPS)/inertial measurement unit (IMU) data, acquired and stored by the position and orientation system (POS), is used for analysing the quantitative relationships among the platform height, baseline length, baseline angle, look angle and DEM error. The experimental data used are airborne dual-antenna X-band InSAR data, and the measured ground control points (GCPs) are used to validate the accuracy of the DEM. The evaluation results in terms of the standard deviation (SD) and the average mean error (AME) are derived by comparing the reconstructed InSAR DEM with the reference GCPs. The AMEs of the X-direction, the Y-direction and the height are up to 2.078, 9.149 and 1.763 m, respectively. The SDs of the X-direction, the Y-direction and the height are up to ±1.379, ±0.764 and ±1.086 m, respectively. These results agree with the previously calculated quantitative errors. The error value of the Y-direction seems too large, a possible result of system errors. In general, the airborne dual-antenna InSAR system initially meets the requirements of 1:50 000 terrain mapping in western China.     

Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data

Canopy height distributions were created from small-footprint airborne laser scanner (ALS) data collected over 40 field sample plots with size 1000 m² located in mature conifer forest. ALS data were collected with two different instruments, i.e., the ALTM 1233 and ALTM 3100 laser scanners (Optech Inc.). The ALTM 1233 data were acquired at a flying altitude of 1200 m and a pulse repetition frequency (PRF) of 33 kHz. Three different acquisitions were carried out with ALTM 3100, i.e., (1) a flying altitude of 1100 m and a PRF of 50 kHz, (2) a flying altitude of 1100 m and a PRF of 100 kHz, and (3) a flying altitude of 2000 m and a PRF of 50 kHz. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were derived from the four different ALS datasets and for single + first and last echoes of the ALS data separately. The ALS-derived height- and density variables were assessed in pair-wise comparisons to evaluate the effects of (a) instrument, (b) flying altitude, and (c) PRF. A systematic shift in height values of up to 0.3 m between sensors when the first echoes were compared was demonstrated. Also the density-related variables differed significantly between the two instruments. Comparisons of flying altitudes and PRFs revealed upwards shifted canopy height distributions for the highest flying altitude (2000 m) and the lowest PRF (50 kHz). The distribution of echoes on different echo categories, i.e., single and multiple (first and last) echoes, differed significantly between acquisitions. The proportion of multiple echoes decreased with increasing flying altitude and PRF. Different echo categories have different properties since it is likely that single echoes tend to occur in the densest parts of the tree crowns, i.e., near the apex where the concentration of biological matter is highest and distance to the ground is largest. To assess the influence of instrument, flying altitude, and PRF on biophysical properties derived from ALS data, regression analysis was carried out to relate ALS-derived metrics to mean tree height (h_L) and timber volume (V). Cross validation revealed only minor differences in precision for the different ALS acquisitions, but systematic differences between acquisitions of up to 2.5% for h_L and 10.7% for V were found when comparing data from different acquisitions.     

基于RSSI测距的WiFi室内定位算法研究

考虑AP(接入点)部署高度对定位精度的影响,提出了一种消除高度影响的加权质心定位算法.首先对采集的RSSI(接收信号强度指示)进行高斯拟合和Kalman滤波,根据室内信号传播模型得出终端与AP的距离;然后用几何方法对垂直距离做了消除,得出AP和终端的平面距离;最后用消除高度影响的加权质心定位算法计算位置.对于多层建筑物,还提出了一种楼层识别方法,结合二维平面定位构成近三维室内定位算法.在实验楼内用以上方法进行楼层识别,识别率可这100％.在大型教室进行定位实验,结果表明:改进后的算法相比传统定位算法在精度和稳定性上都有一定的提高.     

Dynamic integration of height maps into a 3D world representation from range image sequences

Integration of 21/2D sketches obtained at different observation stations into a consistent world (or object) representation is one of the central issues in computer vision and robotics. The resolution and accuracy of 21/2D sketches may be different from one view point to another, and inconsistent data between different observations may occur. This article presents an approach to building a spatiotemporal representation of dynamic scenes including moving objects from a sequence of range images taken by a moving observer. A range image is transformed into a height-map representation, which is segmented into the ground plane and objects on it. In order to capture the resolution and accuracy of the range information and the consistency of the height information between different height maps, we define a reliability measure of the height information for each bucket on the height map. Using this reliability, the system finds the correspondences of both static and moving objects between different observations, and successively refines the height information and its reliability with newly acquired data, dealing with inconsistent data. Final representation of the integrated height map consists of the time stamp of the last observation, region labels of static and moving objects and their spatiotemporal properties such as height information, its reliability, and the velocities of both the observer and independently moving objects. We applied the method to road scenes physically simulated by landscape toy models and show the experimental results.     

GPS accuracy estimation using map matching techniques: Applied to vehicle positioning and odometer calibration

A test-bed application, called Map Matched GPS (MMGPS) processes raw GPS output data, from RINEX files, or GPS derived coordinates. This developed method uses absolute GPS positioning, map matched, to locate the vehicle on a road centre-line, when GPS is known to be sufficiently accurate. MMGPS software has now been adapted to incorporate positioning based on odometer derived distances (OMMGPS), when GPS positions are not available. Relative GPS positions are used to calibrate the odometer. If a GPS position is detected to be inaccurate, it is not used for positioning, or for calibrating the odometer correction factor. In OMMGPS, GPS pseudorange observations are combined with DTM height information and odometer positions to provide a vehicle position at ‘1 s’ epochs. The described experiment used GPS and odometer observations taken on a London bus on a predefined route in central of London. Therefore, map matching techniques are used to test GPS positioning accuracy, and to identify grossly inaccurate GPS positions. In total, over 15,000 vehicle positions were computed and tested using OMMGPS.In general, the position quality provided by GPS alone was extremely poor, due to multipath effects caused by the urban canyons of central London, so that odometer positioning was used much more often to position the vehicle than GPS. Typically, the ratio is 7:3 odometer positions to GPS positions. In the case of one particular trip, OMMGPS provides a mean error of position of 8.8 m compared with 53.7 m for raw GPS alone.     

基于GNSS的建筑物沉降监测系统设计

针对建筑物沉降监测问题,设计了一套基于GNSS的建筑物沉降监测系统。该系统由数据采集终端、监控中心、云平台及报警系统组成。数据采集终端负责监测点GNSS原始数据采集,使用GPRS无线网络将监测数据传输至监控中心。监控中心负责监测点沉降量计算,并将监测结果实时显示及推送至云端。系统采用静态相对定位技术,并对其数据处理进行自动化改进,实现对建筑物的24小时不间断监测。测试结果显示,系统具有较好的监测精度,能够满足建筑物沉降监测的需要。     

Determining malicious executable distinguishing attributes and low-complexity detection

Detection of rapidly evolving malware requires classification techniques that can effectively and efficiently detect zero-day attacks. Such detection is based on a robust model of benign behavior and deviations from that model are used to detect malicious behavior. In this paper we propose a low-complexity host-based technique that uses deviations in static file attributes to detect malicious executables. We first develop simple statistical models of static file attributes derived from the empirical data of thousands of benign executables. Deviations among the attribute models of benign and malware executables are then quantified using information-theoretic (Kullback-Leibler-based) divergence measures. This quantification reveals distinguishing attributes that are considerably divergent between benign and malware executables and therefore can be used for detection. We use the benign models of divergent attributes in cross-correlation and log-likelihood frameworks to classify malicious executables. Our results, using over 4,000 malicious file samples, indicate that the proposed detector provides reasonably high detection accuracy, while having significantly lower complexity than existing detectors.     

An Automation System of Rooftop Detection and 3D Building Modeling from Aerial Images

This paper presents a prototype system of rooftop detection and 3D building modeling from aerial images. In this system, without the knowledge of the position and orientation information of the aerial vehicle a priori, the parameters of the camera pose and ground plane are first estimated by simple human?Ccomputer interaction. Next, after an over-segmentation of the aerial image by the Mean-Shift algorithm, the rooftop regions are coarsely detected by integrating multi-scale SIFT-like feature vectors with SVM-based visual object recognition. 2D cues alone however might not always be sufficient to separate regions such as parking lots from building roofs. Thus in order to further refine the accuracy of the roof-detection result and remove the misclassified non-rooftop regions such as parking lots, we further resort to 3D depth information estimated based on multi-view geometry. More specifically, we determine whether a candidate region is a rooftop or not according to its height information relative to the ground plane, whereas the candidate region??s height information is obtained by a novel, hierarchical, asymmetry correlation-based corner matching scheme. The output of the system will be a water-tight triangle mesh based 3D building model texture mapped with the aerial images. We developed an interactive 3D viewer based on OpenGL and C+?+ to allow the user to virtually navigate the reconstructed 3D scene with mouse and keyboard. Experimental results are shown on real aerial scenes.     

Assessment of forest structure with airborne LiDAR and the effects of platform altitude

Airborne scanning LiDAR is a spatial technology increasingly used for forestry and environmental applications. However, the accuracy and coverage of LiDAR observations is highly dependent on both the extrinsic specifications of the LiDAR survey as well as the intrinsic effects such as the underlying forest structure. Extrinsic parameters which are set as part of the LiDAR survey include platform altitude, scan angle (half max. angle off nadir), and beam cross sectional diameter at the reflecting surface (referred to as footprint size). In this paper we investigate the effect of a number of these extrinsic parameters, including three different platform altitudes (1000, 2000, and 3000 m), two scan angles at 1000 m (10° and 15° half max. angle off nadir), and three footprint sizes (0.2, 0.4, and 0.6 m). The comparison was undertaken in eucalypt forests at three sites, varying in vegetation structure and topography within the Wedding Bells State Forest, Coffs Harbour, Australia. Results at the plot scale (40 × 90 m areas) indicate that tree heights computed from the 1000 m LiDAR data set (10° half max. angle off nadir) are well correlated with maximum plot heights (difference < 3 m) and field measured canopy volume (r² > 0.75, p < 0.001). Using normalised canopy height profiles (CHP) derived for sites, from data recorded at each altitude, we observed no significant difference between the relative distribution of LiDAR returns, indicating that platform altitude and footprint size have not had a major influence on CHP estimation. Interestingly, comparisons of first and last returns for individual pulses at increasing altitudes identified progressively fewer discrete first/last pulse combinations with more than 70% of pulses recorded as a single return at the highest altitude (3000 m). A possible hypothesis is that greater platform altitude and footprint size reduces the intensity of laser beam incident on a given surface area thus decreasing the probability of recording a last return above the noise threshold. Furthermore, tree scale analysis found a positive relationship between platform altitude and the underestimation of crown area and crown volume. The implications of this work for forest management are: (i) platform altitudes as high as 3000 m can be used to quantify the vertical distribution of phyto-elements, (ii) higher platform altitudes record a lower proportion of first/last return combinations that will further reduce the number of points available for forest structural assessment and development of digital elevation models, and (iii) for discrete LiDAR data, increasing platform altitude will record a lower frequency of returns per crown, resulting in larger underestimates of individual tree crown area and volume if standard algorithms are applied.     

一种多传感器融合的室内三维导航系统

提出了一种多传感器融合的室内三维导航系统,利用了建筑物内的地磁信号,结合转向角传感器记录转过的角度、气压传感器检测用户所处环境中楼层高度,采用众包思想收集数据进行地磁地图的构建,使用地磁指纹匹配技术实现室内任意位置的定位及导航.相比基于WiFi和蓝牙的室内导航系统,基于地磁的室内导航系统不需要任何基础网络设施,应用广泛、灵活,实现成本低.通过在智能手机上部署系统进行实验性能评估,结果显示最高精度达到1 m以内,有95%的导航误差在3m之内,平均误差不超过2m,证明其有良好的定位功能并适用于一般大型建筑物.     

Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data

In this study, a combination of low and high density airborne LiDAR and satellite SPOT-5 HRG data were used in conjunction with ground measurements of forest structure to parameterize four models for zero-plane displacement height d(m) and aerodynamic roughness length z_0m(m), over cool-temperate forests in Heihe River basin, an arid region of Northwest China. For the whole study area, forest structural parameters including tree height (Ht) (m), first branch height (FBH) (m), crown width (CW) (m) and stand density (SD)(trees ha^− 1) were derived by stepwise multiple linear regressions of ground-based forest measurements and height quantiles and fractional canopy cover (f_c) derived from the low density LiDAR data. The high density LiDAR data, which covered a much smaller area than the low density LiDAR data, were used to relate SPOT-5's reflectance to the effective plant area index (PAIe) of the forest. This was done by linear spectrum decomposition and Li-Strahler geometric-optical models. The result of the SPOT-5 spectrum decomposition was applied to the whole area to calculate PAIe (and leaf area index LAI). Then, four roughness models were applied to the study area with these vegetation data derived from the LiDAR and SPOT-5 as input. For validation, measurements at an eddy covariance site in the study area were used. Finally, the four models were compared by plotting histograms of the accumulative distribution of modeled d and z_0m in the study area. The results showed that the model using by frontal area index (FAI) produced best d estimate, and the model using both LAI and FAI generated the best z_0m. Furthermore, all models performed much better when the representative tree height was Lorey's mean height instead of using an arithmetic mean.     

A comparative study of two software development cost modeling techniques using multi-organizational and company-specific data

This research examined the use of the International Software Benchmarking Standards Group (ISBSG) repository for estimating effort for software projects in an organization not involved in ISBSG. The study investigates two questions: (1) What are the differences in accuracy between ordinary least-squares (OLS) regression and Analogy-based estimation? (2) Is there a difference in accuracy between estimates derived from the multi-company ISBSG data and estimates derived from company-specific data? Regarding the first question, we found that OLS regression performed as well as Analogy-based estimation when using company-specific data for model building. Using multi-company data the OLS regression model provided significantly more accurate results than Analogy-based predictions. Addressing the second question, we found in general that models based on the company-specific data resulted in significantly more accurate estimates.     

基于形态学建筑物指数的城市建筑物提取及其高度估算

通过引入高分辨率影像的形态学建筑物指数和阴影指数,并结合面向对象的地物信息提取思想,准确地提取出城市建筑物及其阴影,进而实现了城市建筑物的高度估算。首先,利用形态学建筑物指数的多方向多尺度特征,将建筑物与邻近光谱相似的道路目标进行分离;其次,采用双阈值策略提取建筑物与相应的阴影,进一步提高了建筑物的提取精度;最后,根据成像时刻卫星和太阳的高度角、方位角,建立建筑物阴影长度与建筑物高度的估算模型。试验利用厦门市思明区软件园资源三号(ZY\|3)数据进行城市建筑物提取及其高度估算,证实该方法能够较准确地估算出建筑物的高度信息,并且比基于SVM的监督分类方法具有更高的建筑物提取精度,建筑物高度估算的中误差可达±1 m。     

Using airborne laser scanning to monitor tree migration in the boreal-alpine transition zone

The boreal tree line is expected to advance upwards into the mountains and northwards into the tundra due to global warming. The major objective of this study was to find out if it is possible to use high-resolution airborne laser scanner data to detect very small trees — the pioneers that are pushing the tree line up into the mountains and out onto the tundra. The study was conducted in a sub-alpine/alpine environment in southeast Norway. A total of 342 small trees of Norway spruce, Scots pine, and downy birch with tree heights ranging from 0.11 to 5.20 m were precisely georeferenced and measured in field. Laser data were collected with a pulse density of 7.7 m^− 2. Three different terrain models were used to process the airborne laser point cloud in order to assess the effects of different pre-processing parameters on small tree detection. Greater than 91% of all trees > 1 m tall registered positive laser height values regardless of terrain model. For smaller trees (< 1 m), positive height values were found in 5-73% of the cases, depending on the terrain model considered. For this group of trees, the highest rate of trees with positive height values was found for spruce. The more smoothed the terrain model was, the larger the portion of the trees that had positive laser height values. The accuracy of tree height derived from the laser data indicated a systematic underestimation of true tree height by 0.40 to 1.01 m. The standard deviation for the differences between laser-derived and field-measured tree heights was 0.11-0.73 m. Commission errors, i.e., the detection of terrain objects — rocks, hummocks — as trees, increased significantly as terrain smoothing increased. Thus, if no classification of objects into classes like small trees and terrain objects is possible, many non-tree objects with a positive height value cannot be separated from those actually being trees. In a monitoring context, i.e., repeated measurements over time, we argue that most other objects like terrain structures, rocks, and hummocks will remain stable over time while the trees will change as they grow and new trees are established. Thus, this study indicates that, given a high laser pulse density and a certain density of newly established trees, it would be possible to detect a sufficient portion of newly established trees over a 10 years period to claim that tree migration is taking place.