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Information about trees in forest is essential for the assessment of the quantity and the quality of forest ecosystem services. Recently, Deep Learning (DL) methods were regarded as a new cornerstone of algorithm development. Semantic annotations of 3D forest scenes are fundamental for DL algorithm developments. Its necessity has become more urgent as DL is data-driven and requires large amount of training and verification data. However, high-quality annotated forest datasets are still rare, as trees comprise of irregular structures and small components and pose significantly greater challenges even for manual recognition in comparison with artificial objects. This paper introduces a new open point cloud dataset ForestSemantic for forest semantic studies at both individual tree- and plot-levels. The dataset is based on TLS data with different forest conditions. Manual annotation was carried out to a level of detail 4, i.e., until all visible branches. Semantic information is provided at both plot- and tree-levels, as well as at both object- and point-level. Thus, the dataset supports both instance and semantic studies, such as objects detection and segmentation and classification at both tree- and plot-levels. In addition, the dataset also provides comprehensive structural tree traits as reference for further methodological development and verification. This dataset is expected to facilitate research in new dimensions and benchmarks of different systems and solutions. A few examples are demonstrated in this paper to unveil the potentials of the dataset for various applications. In future, it is also possible to simulate other types of point clouds by down-sampling and deforming, and to transfer the dataset for training and verification of other close-range sensing systems, as the dataset was generated using TLS point clouds that represent the highest spatial resolution and geometric accuracy in all close-range point clouds.

Monitoring forest growth accurately is important for assessing and controlling forest carbon stocks that impact, for example, the atmospheric CO2 concentration and, consequently, the climate change. In prior studies, forest growth monitoring with laser scanning methods has resulted in relatively high errors. However, the contribution of reference measurement error to uncertainty in growth resolution has rarely been analysed, and the reference measurements are usually considered mostly flawless. In this study, a seven-year-long growth of individual trees was estimated using both airborne and terrestrial laser scanning (ALS, TLS) that have emerged as potential candidates for digital forest reference measurements. The growth values were derived for diameter at breast height (DBH) and stem volume between the years 2014 and 2021 using an indirect approach. The values obtained with laser scanning were paired with manual field measurements and also with each other to study pairwise errors. The pairwise comparison showed that even though all the three measurement methods produced good Pearson correlation coefficients for one-time measurements (all above 0.88), the coefficients for growth measurements were significantly lower (0.19–0.44 for DBH and 0.47–0.66 for stem volume). The best correlation and root mean squared deviation (RMSD) for DBH growth (ρ = 0.44, RMSD = 0.98 cm) and stem volume growth (ρ = 0.66, RMSD = 0.052 m3) was observed between the manual field measurements and the ALS-based growth measurement method, in which the tree stem curve was obtained from the 2021 point cloud, and the stem curve was predicted backwards for the year 2014 according to height growth. The ALS method suffered less from outlying values than the TLS-based growth measurement method, in which the growth was computed based on the difference of stem curves derived separately for the years 2014 and 2021. The study showed that observing the stem curve is a potential method for short-period growth monitoring. Using the pairwise comparison results, we further derived estimates for the mean and standard deviation of measurement error of each individual measurement method. For the manual measurements, the standard deviation of error was found to be approximately 0.4 cm for DBH growth and 0.03 m3 for volume growth, which were the lowest of the three methods but not by a large margin. This highlights the need for more accurate reference data as the accuracy of laser scanning-based growth estimation methods continues to approach the accuracy of manual measurements.

In this paper, we compare the positioning accuracy of commercial, mobile laser scanning systems operating under a forest canopy. The accuracy was evaluated on a 800-m-long positioning track, using tree locations from both a traditional field reference, collected with total station, and a high-density airborne laser scanning (ALS) system as a reference. Tree locations were used since mobile lasers are studied for automation of field reference for forest inventory and location of individual trees with high accuracy is required. We also developed a novel method for evaluating the ground level around the trees, as it not only affects the z-coordinate, but the horizontal position as well if the tree is tilted.In addition to the accuracy that could only be evaluated for systems equipped with a GNSS receiver, we evaluate the consistency of laser scanning systems by registering the tree locations extracted from the mobile systems to both the field reference and ALS. We demonstrated that the high-density ALS has similar accuracy (RMSE of approximately 6 cm) and precision as the total station field reference, while being much faster to collect. Furthermore, the completeness of the high-density ALS was over 80 %, which is more than enough to register the other methods to it in a robust manner, providing a global position for laser scanners without an inherit way of georeferencing themselves, such as a GNSS receiver.The positioning of all the mobile systems were based on the Simultaneous Localization and Mapping (SLAM) algorithm integrated with an inertial measurement unit (IMU), and they showed a similar precision; planar positioning error of less than 15 cm and vertical error of 10–30 cm. However, the accuracy of the only commercial system in this test whose positioning methods included a GNSS receiver, was order of several meters, indicating a demand for better methods for GNSS-based global positioning inside a dense forest canopy.

Automating forest machines to optimize the forest value chain requires the ability to map the surroundings of the machine and to conduct accurate measurements of nearby trees. In the near-to-medium term, integrating a forest harvester with a mobile laser scanner system may have multiple applications, including real-time assistance of the harvester operator using laser-scanner-derived tree measurements and the collection of vast amounts of training data for large-scale airborne laser scanning-based surveys at the individual tree level. In this work, we present a comprehensive processing flow for a mobile laser scanning (MLS) system mounted on a forest harvester starting from the localization of the harvester under the forest canopy followed by accurate and automatic estimation of tree attributes, such as diameter at breast height (DBH) and stem curve. To evaluate our processing flow, we recorded and processed MLS data from a commercial thinning operation on three test strips with a total driven length ranging from 270 to 447 m in a managed Finnish spruce forest stand containing a total of 658 reference trees within a distance of 15 m from the harvester trajectory. Localization reference was obtained by a robotic total station, while reference tree attributes were derived using a high-quality handheld laser scanning system. As some applications of harvester-based MLS require real-time capabilities while others do not, we investigated the positioning accuracy both for real-time localization of the harvester and after the optimization of the full trajectory. In the real-time positioning mode, the absolute localization error was on average 2.44 m, while the corresponding error after the full optimization was 0.21 m. Applying our automatic stem diameter estimation algorithm for the constructed point clouds, we measured DBH and stem curve with a root-mean-square error (RMSE) of 3.2 cm and 3.6 cm, respectively, while detecting approximately 90% of the reference trees with DBH>20 cm that were located within 15 m from the harvester trajectory. To achieve these results, we demonstrated a distance-adjusted bias correction method mitigating diameter estimation errors caused by the high beam divergence of the laser scanner used.

Real-time semantic segmentation of point clouds has increasing importance in applications related to 3D city modelling and mapping, automated inventory of forests, autonomous driving and mobile robotics. Current state-of-the-art point cloud semantic segmentation methods rely heavily on the availability of 3D laser scanning data. This is problematic in regards of low-latency, real-time applications that use data from high-precision mobile laser scanners, as those are typically 2D line scanning devices. In this study, we experiment with real-time semantic segmentation of high-density multispectral point clouds collected from 2D line scanners in urban environments using encoder - decoder convolutional neural network architectures. We introduce a rasterized multi-scan input format that can be constructed exclusively from the raw (non-georeferenced profiles) 2D laser scanner measurement stream without odometry information. In addition, we investigate the impact of multispectral data on the segmentation accuracy. The dataset used for training, validation and testing was collected with multispectral FGI AkhkaR4-DW backpack laser scanning system operating at the wavelengths of 905 nm and 1550 nm, and consists in total of 228 million points (39 583 scans). The data was divided into 13 classes that represent various targets in urban environments. The results show that the increased spatial context of the multi-scan format improves the segmentation performance on the single-wavelength lidar dataset from 45.4 mIoU (a single scan) to 62.1 mIoU (24 consecutive scans). In the multispectral point cloud experiments we achieved a 71 % and 28 % relative increase in the segmentation mIoU (43.5 mIoU) as compared to the purely single-wavelength reference experiments, in which we achieved 25.4 mIoU (905 nm) and 34.1 mIoU (1550 nm). Our findings show that it is possible to semantically segment 2D line scanner data with good results by combining consecutive scans without the need for odometry information. The results also serve as motivation for developing multispectral mobile laser scanning systems that can be used in challenging urban surveys.

Abstract Stream networks in Arctic and high-elevation regions underlain by frozen ground (i.e., permafrost) are expanding and developing in response to accelerating global warming, and intensifying summertime climate variability. The underlying processes governing landscape dissection in these environments are varied, complex and challenging to unravel due to air-temperature-regulated feedbacks and shifts to new erosional regimes as climate change progresses. Here we use multiple sources of environmental information and physical models to reconstruct and understand a 60-year history of landscape-scale channelization and evolution of the Muskox Valley, Axel Heiberg Island. A time series of air photographs indicates that freeze-thaw-related polygon fields can form rapidly, over decadal time scales. Supporting numerical simulations show that the presence of polygons can control how surface runoff is routed through the landscape, exerting a basic control on channelization, which is sensitive to the timing, duration and magnitude of hydrograph events, as well as seasonal air temperature trends. These results collectively highlight that the occurrence and dynamics of polygon fields modulate channel network establishment in permafrost-rich settings undergoing changes related to a warming climate.

The use of mobile laser scanning for mapping forests has scarcely been studied in difficult forest conditions. In this paper, we compare the accuracy of retrieving tree attributes, particularly diameter at breast height (DBH), stem curve, stem volume, and tree height, using six different laser scanning systems in a managed natural boreal forest. These compared systems operated both under the forest canopy on handheld and unmanned aerial vehicle (UAV) platforms and above the canopy from a helicopter. The complexity of the studied forest sites ranged from easy to difficult, and thus, this is the first study to compare the performance of several laser scanning systems for the direct measurement of stem curve in difficult forest conditions. To automatically detect tree stems and to calculate their attributes, we utilized our previously developed algorithm integrated with a novel bias compensation method to reduce the overestimation of stem diameter arising from finite laser beam divergence. The bias compensation method reduced the absolute value of the diameter bias by 55–99%. The most accurate laser scanning systems were equipped with a Velodyne VLP-16 sensor, which has a relatively low beam divergence, on a handheld or UAV platform. In easy plots, these systems found a root-mean-square error (RMSE) of below 10% for DBH and stem curve estimates and approximately 10% for stem volume. With the handheld system in difficult plots, the DBH and stem curve estimates had an RMSE under 10%, and the stem volume RMSE was below 20%. Even though bias compensation reduced the difference in bias and RMSE between laser scanners with high and low beam divergence, the RMSE remained higher for systems with a high beam divergence. The airborne laser scanner operating above the forest canopy provided tree attribute estimates close to the accuracy of the under-canopy laser scanners, but with a significantly lower completeness rate for stem detection, especially in difficult forest conditions.

We showed that a mobile handheld laser scanner (HHLS) provides useful features concerning the wood quality-influencing external structures of trees. When linked with wood properties measured at a sawmill utilizing state-of-the-art X-ray scanners, these data enable the training of various wood quality models for use in targeting and planning future wood procurement. A total of 457 Norway spruce sample trees (Picea abies (L.) H. Karst.) from 13 spruce-dominated stands in southeastern Finland were used in the study. All test sites were recorded with a ZEB Horizon HHLS, and the sample trees were tracked to a sawmill and subjected to X-rays. Two branch extraction techniques were applied to the HHLS point clouds: 1) a method developed in this study that was based on the density-based spatial clustering of applications with noise (DBSCAN) and 2) segmentation-based quantitative structure model (treeQSM). On average, the treeQSM method detected 46% more branches per tree than the DBSCAN did. However, compared with the X-rayed references, some of the branches detected by the treeQSM may either be false positives or so small in size that the X-rays are unable to detect them as knots, as the method overestimated the whorl count by 19% when compared with the X-rays. On the other hand, the DBSCAN method only detected larger branches and showed a −11% bias in whorl count. Overall, the DBSCAN underestimated knot volumes within trees by 6%, while the treeQSM overestimated them by 25%. When we input the HHLS features into a Random Forest model, the knottiness variables measured at the sawmill were predicted with R2s of 0.47–0.64. The results were comparable with previous results derived with the static terrestrial laser scanners. The obtained stem branching data are relevant for predicting wood quality attributes but do not provide data that are directly comparable with the X-ray features. Future work should combine terrestrial point clouds with dense above-canopy point clouds to overcome the limitations related to vertical coverage.

Tree species characterise biodiversity, health, economic potential, and resilience of an ecosystem, for example. Tree species classification based on remote sensing data, however, is known to be a challenging task. In this paper, we study for the first time the feasibility of tree species classification using high-density point clouds collected with an airborne close-range multispectral laser scanning system – a technique that has previously proved to be capable of providing stem curve and volume accurately and rapidly for standing trees. To this end, we carried out laser scanning measurements from a helicopter on 53 forest sample plots, each with a size of 32 m × 32 m. The plots covered approximately 5500 trees in total, including Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H.Karst.), and deciduous trees such as Downy birch (Betula pubescens Ehrh.) and Silverbirch (Betula pendula Roth). The multispectral laser scanning system consisted of integrated Riegl VUX-1HA, miniVUX-3UAV, and VQ-840-G scanners (Riegl GmbH, Austria) operating at wavelengths of 1550 nm, 905 nm, and 532 nm, respectively. A new approach, layer-by-layer segmentation, was developed for individual tree detection and segmentation from the dense point cloud data. After individual tree segmentation, 249 features were computed for tree species classification, which was tested with approximately 3000 trees. The features described the point cloud geometry as well as single-channel and multi-channel reflectance metrics. Both feature selection and the tree species classification were conducted using the random forest method. Using the layer-by-layer segmentation algorithm, trees in the dominant and co-dominant categories were found with detection rates of 89.5% and 77.9%, respectively, whereas suppressed trees were detected with a success rate of 15.2%–42.3%, clearly improving upon the standard watershed segmentation. The overall accuracy of the tree species classification was 73.1% when using geometric features from the 1550 nm scanner data and 86.6% when combining the geometric features with reflectance information of the 1550 nm data. The use of multispectral reflectance and geometric features improved the overall classification accuracy up to 90.8%. Classification accuracies were as high as 92.7% and 93.7% for dominant and co-dominant trees, respectively.

Today, high-quality reference tree measurements, including the position, diameter, height and volume, are cumbersome and slow to carry out, but highly needed for forest inventories based on airborne laser scanning. Mobile laser scanning technologies hold the promise for collecting reference data for forest inventories with an extremely high efficiency. Perhaps, the most efficient approach for reference data collection would be to mount a high-resolution laser scanning system on board an airborne vehicle flying at a low altitude above the forest canopy since this would allow recording reference samples of individual trees with the speed of flight. To demonstrate the potential of this technology, we mounted an in-house developed HeliALS-DW laser scanning system on board a helicopter and collected point cloud data in a boreal forest on three test sites containing a total of 1469 trees. The obtained point clouds incorporated sufficiently many high-quality stem hits for estimating the stem curves and stem volumes of individual trees since the point clouds had a relatively high point density of 2200–3800 echoes/m2, and the scanner had been tilted by 15° from the nadir to increase the possibility of recording stem hits. To automatically estimate the diameters at breast height (DBH) and stem curves of individual trees, we used algorithms designed to tolerate moderate drifts in the trajectory of the laser scanner. Furthermore, the stem volumes of individual trees were computed by using the estimated stem curves and tree heights without any allometric models. Using the proposed methods, we were able to estimate the stem curves with a root-mean-square error (RMSE) of 1.7–2.6 cm (6–9%) while detecting 42–71% of the trees. The RMSE of stem volume estimates was 0.1–0.15 m3 (12–21%). We also showed that the tree detection rate could be improved up to 87–96% for trees with a DBH exceeding 20 cm if slightly larger average errors for the stem attributes were allowed. Our results pave the way for using high-resolution airborne laser scanning for field reference data collection by conducting direct measurements of tree stems with a high efficiency.

The continuous flow of autonomous vehicle-based data could revolutionize current map updating procedures and allow completely new types of mapping applications. Therefore, in this article, we demonstrate the feasibility of using perception data of autonomous vehicles to replace traditionally conducted mobile mapping surveys with a case study focusing on updating a register of roadside city trees. In our experiment, we drove along a 1.3-km-long road in Helsinki to collect laser scanner data using our autonomous car platform ARVO, which is based on a Ford Mondeo hybrid passenger vehicle equipped with a Velodyne VLS-128 Alpha Prime scanner and other high-grade sensors for autonomous perception. For comparison, laser scanner data from the same region were also collected with a specially-planned high-grade mobile mapping laser scanning system. Based on our results, the diameter at breast height, one of the key parameters of city tree registers, could be estimated with a lower root-mean-square error from the perception data of the autonomous car than from the specially-planned mobile laser scanning survey, provided that time-based filtering was included in the post-processing of the autonomous perception data to mitigate distortions in the obtained point cloud. Therefore, appropriately performed post-processing of the autonomous perception data can be regarded as a viable option for keeping maps updated in road environments. However, point cloud-processing algorithms may need to be adapted for the post-processing of autonomous perception data due to the differences in the sensors and their arrangements compared to designated mobile mapping systems. We also emphasize that time-based filtering may be required in the post-processing of autonomous perception data due to point cloud distortions around objects seen at multiple times. This highlights the importance of saving the time stamp for each data point in the autonomous perception data or saving the temporal order of the data points.

Abstract Background Current automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost. Results In the experiment, an approximately 0.5 ha forest was covered in ca. 10 min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2–4 cm RMSE of the diameter at the breast height estimates, and a 4–7 cm RMSE of the stem curve estimates. Conclusions Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.

The emergence of hand-held Personal Laser Scanning (H-PLS) systems in recent years resulted in initial research on the possibility of its application in forest inventory, primarily for the estimation of the main tree attributes (e.g. tree detection, stem position, DBH, tree height, etc.). Research knowledge acquired so far can help to direct further research and eventually include H-PLS into operational forest inventory in the future. The main aims of this review are: Þ to present the current state of the art for H-PLS systems Þ briefly describe the fundamental concept and methods for H-PLS application in forest inventory Þ provide an overview of the results of previous studies Þ emphasize pros and cons for H-PLS application in forest inventory in relation to conventional field measurements and other similar laser scanning systems Þ highlight the main issues that should be covered by further H-PLS-based forest inventory studies.

Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. Most existing registration methods rely on stems to determine the transformation parameters. However, the complexity of the registration problem increases dramatically as the number of stems grows. It is tricky to reduce the stems and determine the valid ones that can provide reliable registration transformation without a knowledge of the two scans. This paper presents an automatic and fast registration of TLS point clouds in forest areas. It reduces stems by selecting from the overlap areas, which are recovered from the mode-based key points that are detected from crowns. The proposed method was tested in a managed forest in Finland, and was compared with the stem-based registration method without reducing stems. The experiments demonstrated that the mean rotation error was 2.09′, and the mean errors in horizontal and vertical translation were 1.13 and 7.21 cm, respectively. Compared with the stem-based method, the proposed method improves the registration efficiency significantly (818 s vs 96 s) and achieves similar results in terms of the mean registration errors (1.94′ for rotation error, 0.83 and 7.38 cm for horizontal and vertical translation error, respectively).

Forest is one of the most challenging environments to be recorded in a three-dimensional (3D) digitized geometrical representation, because of the size and the complexity of the environment and the data-acquisition constraints brought by on-site conditions. Previous studies have indicated that the data-acquisition pattern can have more influence on the registration results than other factors. In practice, the ideal short-baseline observations, i.e., the dense collection mode, is rarely feasible, considering the low accessibility in forest environments and the commonly limited labor and time resources. The wide-baseline observations that cover a forest site using a few folds less observations than short-baseline observations, are therefore more preferable and commonly applied. Nevertheless, the wide-baseline approach is more challenging for data registration since it typically lacks the required sufficient overlaps between datasets. Until now, a robust automated registration solution that is independent of special hardware requirements has still been missing. That is, the registration accuracy is still far from the required level, and the information extractable from the merged point cloud using automated registration could not match that from the merged point cloud using manual registration. This paper proposes a discrete overlap search (DOS) method to find correspondences in the point clouds to solve the low-overlap problem in the wide-baseline point clouds. The proposed automatic method uses potential correspondences from both original data and selected feature points to reconstruct rough observation geometries without external knowledge and to retrieve precise registration parameters at data-level. An extensive experiment was carried out with 24 forest datasets of different conditions categorized in three difficulty levels. The performance of the proposed method was evaluated using various accuracy criteria, as well as based on data acquired from different hardware, platforms, viewing perspectives, and at different points of time. The proposed method achieved a 3D registration accuracy at a 0.50-cm level in all difficulty categories using static terrestrial acquisitions. In the terrestrial-aerial registration, data sets were collected from different sensors and at different points of time with scene changes, and a registration accuracy at the raw data geometric accuracy level was achieved. These results represent the highest automated registration accuracy and the strictest evaluation so far. The proposed method is applicable in multiple scenarios, such as 1) the global positioning of individual under-canopy observations, which is one of the main challenges in applying terrestrial observations lacking a global context, 2) the fusion of point clouds acquired from terrestrial and aerial perspectives, which is required in order to achieve a complete forest observation, 3) mobile mapping using a new stop-and-go approach, which solves the problems of lacking mobility and slow data collection in static terrestrial measurements as well as the data-quality issue in the continuous mobile approach. Furthermore, this work proposes a new error estimate that units all parameter-level errors into a single quantity and compensates for the downsides of the widely used parameter- and object-level error estimates; it also proposes a new deterministic point sets registration method as an alternative to the popular sampling methods.

Deep learning methods based on convolutional neural networks have shown to give excellent results in semantic segmentation of images, but the inherent irregularity of point cloud data complicates their usage in semantically segmenting 3D laser scanning data. To overcome this problem, point cloud networks particularly specialized for the purpose have been implemented since 2017 but finding the most appropriate way to semantically segment point clouds is still an open research question. In this study we attempted semantic segmentation of point cloud data with convolutional neural networks by using only the raw measurements provided by a multiple echo detection capable profiling laser scanner. We formatted the measurements to a series of 2D rasters, where each raster contains the measurements (range, reflectance, echo deviation) of a single scanner mirror rotation to be able to use the rich research done on semantic segmentation of 2D images with convolutional neural networks. Similar approach for profiling laser scanner in forest context has never been proposed before. A boreal forest in Evo region near Hämeenlinna in Finland was used as experimental study area. The data was collected with FGI Akhka-R3 backpack laser scanning system, georeferenced and then manually labelled to ground, understorey, tree trunk and foliage classes for training and evaluation purposes. The labelled points were then transformed back to 2D rasters and used for training three different neural network architectures. Further, the same georeferenced data in point cloud format was used for training the state-of-the-art point cloud semantic segmentation network RandLA-Net and the results were compared with those of our method. Our best semantic segmentation network reached the mean Intersection-over-Union value of 80.1% and it is comparable to the 80.6% reached by the point cloud -based RandLA-Net. The numerical results and visual analysis of the resulting point clouds show that our method is a valid way of doing semantic segmentation of point clouds at least in the forest context. The labelled datasets were also released to the research community.

In this paper, we compared five crack detection algorithms using terrestrial laser scanner (TLS) point clouds. The methods are developed based on common point cloud processing knowledge in along- and across-track profiles, surface fitting or local pointwise features, with or without machine learning. The crack area and volume were calculated from the crack points detected by the algorithms. The completeness, correctness, and F1 score of each algorithm were computed against manually collected references. Ten 1-m-by-3.5-m plots containing 75 distresses of six distress types (depression, disintegration, pothole, longitudinal, transverse, and alligator cracks) were selected to explain variability of distresses from a 3-km-long-road. For crack detection at plot level, the best algorithm achieved a completeness of up to 0.844, a correctness of up to 0.853, and an F1 score of up to 0.849. The best algorithm’s overall (ten plots combined) completeness, correctness, and F1 score were 0.642, 0.735, and 0.685 respectively. For the crack area estimation, the overall mean absolute percentage errors (MAPE) of the two best algorithms were 19.8% and 20.3%. In the crack volume estimation, the two best algorithms resulted in 19.3% and 14.5% MAPE. When the plots were grouped based on crack detection complexity, in the ‘easy’ category, the best algorithm reached a crack area estimation MAPE of 8.9%, while for crack volume estimation, the MAPE obtained from the best algorithm was 0.7%.

Fallen tree mapping provides valuable information regarding the ecological value of boreal forests. Airborne laser scanning (ALS) enables mapping fallen trees on a large scale. We compared the performance of line-detection-based individual fallen tree detection when using moderate point density ALS data (15 points/m2) and high-point-density unmanned aerial vehicle-based laser scanning (ULS) data (285 points/m2). Furthermore, we inspected the dataset and detection methodology-related factors impacting performance in each case. The results of this study showed that increasing the point density of the laser scanning dataset enables the detection of a larger proportion of fallen trees. However, based on our experiment, a line-detection-based fallen tree detection approach is sensitive to noise, thus generating a large number of false detections, especially with high-point-density data. Different types of filters, such as a simple height-based filter and machine-learning-based filters, can be used for reducing noise. However, using such filters is always a compromise, as in addition to reducing noise and thus false detections, they also reduce the number of true detections. Hence, a less noise-sensitive fallen tree detection method utilizing the finer details visible in high-density point clouds could be more suitable for high-point-density laser scanning data.

In this paper, we present a simple, efficient, and robust algorithm for 2D coarse registration of two point clouds. In the proposed algorithm, the locations of some distinct objects are detected from the point cloud data, and a rotation- and translation-invariant feature descriptor vector is computed for each of the detected objects based on the relative locations of the neighboring objects. Subsequently, the feature descriptors obtained for the different point clouds are compared against one another by using the Euclidean distance in the feature space as the similarity criterion. By using the nearest neighbor distance ratio, the most promising matching object pairs are found and further used to fit the optimal Euclidean transformation between the two point clouds. Importantly, the time complexity of the proposed algorithm scales quadratically in the number of objects detected from the point clouds. We demonstrate the proposed algorithm in the context of forest inventory by performing coarse registration between terrestrial and airborne point clouds. To this end, we use trees as the objects and perform the coarse registration by using no other information than the locations of the detected trees. We evaluate the performance of the algorithm using both simulations and three test sites located in a boreal forest. We show that the algorithm is fast and performs well for a large range of stem densities and for test sites with up to 10 ​000 trees. Additionally, we show that the algorithm works reliably even in the case of moderate errors in the tree locations, commission and omission errors in the tree detection, and partial overlap of the data sets. We also demonstrate that additional tree attributes can be incorporated into the proposed feature descriptor to improve the robustness of the registration algorithm provided that reliable information of these additional tree attributes is available. Furthermore, we show that the registration accuracy between the terrestrial and airborne point clouds can be significantly improved if stem positions estimated from the terrestrial data are matched to stem positions obtained from the airborne data instead of matching them to tree top positions estimated from the airborne data. Even though the 2D coarse registration algorithm is demonstrated in the context of forestry, the algorithm is not restricted to forest data and it may potentially be utilized in other applications, in which efficient 2D point set registration is needed.

Abstract Background Lately, terrestrial point clouds have drawn attention as a new data source for in situ forest investigations. So far, terrestrial laser scanning (TLS) has the highest data quality among all terrestrial point cloud data in terms of geometric accuracy and level of detail (IEEE Transact Geosci Remote Sens 53: 5117–5132, 2015). The TLS point clouds processed by automated algorithms can provide certain individual tree parameters at close to required accuracy in practical applications. However, all terrestrial point clouds face a general challenge, which is the occlusions of upper tree crowns. An emerging technology called unmanned-aerial-vehicle (UAV) - borne laser scanning (ULS) potentially combines the strengths of above and under canopy surveys. Results The performance of ULS are evaluated in 22 sample plots of various forest stand conditions in a boreal forest. The forest parameter estimates are benchmarked through a comparison with state-of-the-art terrestrial mechanisms from both static terrestrial and mobile laser scanning. The results show that in easy forest stand conditions, the performance of ULS point cloud is comparable with the terrestrial solutions. Conclusions This study gives the first strict evaluation of ULS in situ observations in varied forest conditions. The study also acts as a benchmarking of available active remote sensing techniques for forest in situ mensuration. The results indicate that the current off-the-shelf ULS has an excellent tree height/tops measurement performance. Although the geometrical accuracy of the ULS data, especially at the stem parts, does not yet reach the level of other terrestrial point clouds, the unbeatable high mobility and fast data acquisition make the ULS a very attractive option in forest investigations.

Autonomous vehicle perception systems typically rely on single-wavelength lidar sensors to obtain three-dimensional information about the road environment. In contrast to cameras, lidars are unaffected by challenging illumination conditions, such as low light during night-time and various bidirectional effects changing the return reflectance. However, as many commercial lidars operate on a monochromatic basis, the ability to distinguish objects based on material spectral properties is limited. In this work, we describe the prototype hardware for a hyperspectral single photon lidar and demonstrate the feasibility of its use in an autonomous-driving-related object classification task. We also introduce a simple statistical model for estimating the reflectance measurement accuracy of single photon sensitive lidar devices. The single photon receiver frame was used to receive 30 12.3 nm spectral channels in the spectral band 1200–1570 nm, with a maximum channel-wise intensity of 32 photons. A varying number of frames were used to accumulate the signal photon count. Multiple objects covering 10 different categories of road environment, such as car, dry asphalt, gravel road, snowy asphalt, wet asphalt, wall, granite, grass, moss, and spruce tree, were included in the experiments. We test the influence of the number of spectral channels and the number of frames on the classification accuracy with random forest classifier and find that the spectral information increases the classification accuracy in the high-photon flux regime from 50% to 94% with 2 channels and 30 channels, respectively. In the low-photon flux regime, the classification accuracy increases from 30% to 38% with 2 channels and 6 channels, respectively. Additionally, we visualize the data with the t-SNE algorithm and show that the photon shot noise in the single photon sensitive hyperspectral data contributes the most to the separability of material specific spectral signatures. The results of this study provide support for the use of hyperspectral single photon lidar data on more advanced object detection and classification methods, and motivates the development of advanced single photon sensitive hyperspectral lidar devices for use in autonomous vehicles and in robotics.

An efficient 3D survey of a complex indoor environment remains a challenging task, especially if the accuracy requirements for the geometric data are high for instance in building information modeling (BIM) or construction. The registration of non-overlapping terrestrial laser scanning (TLS) point clouds is laborious. We propose a novel indoor mapping strategy that uses a simultaneous localization and mapping (SLAM) laser scanner (LS) to support the building-scale registration of non-overlapping TLS point clouds in order to reconstruct comprehensive building floor/3D maps. This strategy improves efficiency since it allows georeferenced TLS data to only be collected from those parts of the building that require such accuracy. The rest of the building is measured with SLAM LS accuracy. Based on the results of the case study, the introduced method can locate non-overlapping TLS point clouds with an accuracy of 18–51 mm using target sphere comparison.

This study aimed at illustrating how direct measurements, mobile laser scanning and hydraulic modelling can be combined to quantify environmental drivers, improve vegetation models and increase our understanding of vegetation patterns in a sub-arctic river valley. Our results indicate that the resultant vegetation models successfully predict riparian vegetation patterns (Rho = 0.8 for total species richness, AUC = 0.97 for distribution) and highlight differences between eight functional species groups (Rho 0.46-0.84; AUC 0.79-0.93; functional group-specific effects). In our study setting, replacing the laser scanning-based and hydraulic modelling-based variables with a proxy variable elevation did not significantly weaken the models. However, using directly measured and modelled variables allows relating species patterns to e.g. stream power or the length of the flood-free period. Substituting these biologically relevant variables with proxies mask important processes and may reduce the transferability of the results into other sites. At the local scale, the amount of litter is a highly important driver of total species richness, distribution and abundance patterns (relative influences 49, 72 and 83%, respectively) and across all functional groups (13-57%; excluding lichen species richness) in the sub-arctic river valley. Moreover, soil organic matter and soil water content shape vegetation patterns (on average 16 and 7%, respectively). Fluvial disturbance is a key limiting factor only for lichen, bryophyte and dwarf shrub species in this environment (on average 37, 6 and 10%, respectively). Fluvial disturbance intensity is the most important component of disturbance for most functional groups while the length of the disturbance-free period is more relevant for lichens. We conclude that striving for as accurate quantifications of environmental drivers as possible may reveal important processes and functional group differences and help anticipate future changes in vegetation. Mobile laser scanning, high-resolution digital elevation models and hydraulic modelling offer useful methodology for improving correlative vegetation models.

This paper studied the applicability of the Roamer-R4DW mobile laser scanning (MLS) system for road rut depth measurement. The MLS system was developed by the Finnish Geospatial Research Institute (FGI), and consists of two mobile laser scanners and a Global Navigation Satellite System (GNSS)-inertial measurement unit (IMU) positioning system. In the study, a fully automatic algorithm was developed to calculate and analyze the rut depths, and verified in 64 reference pavement plots (1.0 m × 3.5 m). We showed that terrestrial laser scanning (TLS) data is an adequate reference for MLS-based rutting studies. The MLS-derived rut depths based on 64 plots resulted in 1.4 mm random error, which can be considered adequate precision for operational rutting depth measurements. Such data, also covering the area outside the pavement, would be ideal for multiple road environment applications since the same data can also be used in applications, from high-definition maps to autonomous car navigation and digitalization of street environments over time and in space.

In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.

Terrestrial laser scanning (TLS) enables the efficient production of high-density colored 3D point clouds of real-world environments. An increasing number of applications from visual and automated interpretation to photorealistic 3D visualizations and experiences rely on accurate and reliable color information. However, insufficient attention has been put into evaluating the colorization quality of the 3D point clouds produced applying TLS. We have developed a method for the evaluation of the point cloud colorization quality of TLS systems with integrated imaging sensors. Our method assesses the capability of several tested systems to reproduce colors and details of a scene by measuring objective image quality metrics from 2D images that were rendered from 3D scanned test charts. The results suggest that the detected problems related to color reproduction (i.e., measured differences in color, white balance, and exposure) could be mitigated in data processing while the issues related to detail reproduction (i.e., measured sharpness and noise) are less in the control of a scanner user. Despite being commendable 3D measuring instruments, improving the colorization tools and workflows, and automated image processing pipelines would potentially increase not only the quality and production efficiency but also the applicability of colored 3D point clouds.

Changing climate is increasing the amount and intensity of forest stress agents, such as drought, pest insects, and pathogens. Leaf water content, measured here in terms of equivalent water thickness (EWT), is an early indicator of tree stress that provides timely information about the health status of forests. Multispectral terrestrial laser scanning (MS-TLS) measures target geometry and reflectance simultaneously, providing spatially explicit reflectance information at several wavelengths. EWT and leaf internal structure affect leaf reflectance in the shortwave infrared region that can be used to predict EWT with MS-TLS. A second wavelength that is sensitive to leaf internal structure but not affected by EWT can be used to normalize leaf internal effects on the shortwave infrared region and improve the prediction of EWT. Here we investigated the relationship between EWT and laser intensity features using multisensor MS-TLS at 690, 905, and 1,550 nm wavelengths with both drought-treated and Endoconidiophora polonica inoculated Norway spruce seedlings to better understand how MS-TLS measurements can explain variation in EWT. In our study, a normalized ratio of two wavelengths at 905 and 1,550 nm and length of seedling explained 91% of the variation (R2) in EWT as the respective prediction accuracy for EWT was 0.003 g/cm2 in greenhouse conditions. The relation between EWT and the normalized ratio of 905 and 1,550 nm wavelengths did not seem sensitive to a decreased point density of the MS-TLS data. Based on our results, different EWTs in Norway spruce seedlings show different spectral responses when measured using MS-TLS. These results can be further used when developing EWT monitoring for improving forest health assessments.

It is anticipated that many of the future forest mapping applications will be based on three-dimensional (3D) point clouds. A comparison study was conducted to verify the explanatory power and information contents of several 3D remote sensing data sources on the retrieval of above ground biomass (AGB), stem volume (VOL), basal area (G), basal-area weighted mean diameter (Dg) and Lorey’s mean height (Hg) at the plot level, utilizing the following data: synthetic aperture radar (SAR) Interferometry, SAR radargrammetry, satellite-imagery having stereo viewing capability, airborne laser scanning (ALS) with various densities (0.8–6 pulses/m2) and aerial stereo imagery. Laser scanning is generally known as the primary source providing a 3D point cloud. However, photogrammetric, radargrammetric and interferometric techniques can be used to produce 3D point clouds from space- and air-borne stereo images. Such an image-based point cloud could be utilized in a similar manner as ALS providing that accurate digital terrain model is available. In this study, the performance of these data sources for providing point cloud data was evaluated with 91 sample plots that were established in Evo, southern Finland within a boreal forest zone and surveyed in 2014 for this comparison. The prediction models were built using random forests technique with features derived from each data sources as independent variables and field measurements of forest attributes as response variable. The relative root mean square errors (RMSEs) varied in the ranges of 4.6% (0.97 m)–13.4% (2.83 m) for Hg, 11.7% (3.0 cm)–20.6% (5.3 cm) for Dg, 14.8% (4.0 m2/ha)–25.8% (6.9 m2/ha) for G, 15.9% (43.0 m3/ha)–31.2% (84.2 m3/ha) for VOL and 14.3% (19.2 Mg/ha)–27.5% (37.0 Mg/ha) for AGB, respectively, depending on the data used. Results indicate that ALS data achieved the most accurate estimates for all forest inventory attributes. For image-based 3D data, high-altitude aerial images and WorldView-2 satellite optical image gave similar results for Hg and Dg, which were only slightly worse than those of ALS data. As expected, spaceborne SAR data produced the worst estimates. WorldView-2 satellite data performed well, achieving accuracy comparable to the one with ALS data for G, VOL and AGB estimation. SAR interferometry data seems to contain more information for forest inventory than SAR radargrammetry and reach a better accuracy (relative RMSE decreased from 13.4% to 9.5% for Hg, 20.6% to 19.2% for Dg, 25.8% to 20.9% for G, 31.2% to 22.0% for VOL and 27.5% to 20.7% for AGB, respectively). However, the availability of interferometry data is limited. The results confirmed the high potential of all 3D remote sensing data sources for forest inventory purposes. However, the assumption of using other than ALS data is that there exist a high quality digital terrain model, in our case it was derived from ALS.

Indoor positioning technology has become more and more important in the last two decades. Utilizing Received Signal Strength Indicator (RSSI) fingerprints of Signals of OPportunity (SOP) is a promising alternative navigation solution. However, as the RSSIs vary during operation due to their physical nature and are easily affected by the environmental change, one challenge of the indoor fingerprinting method is maintaining the RSSI fingerprint database in a timely and effective manner. In this paper, a solution for rapidly updating the fingerprint database is presented, based on a self-developed Unmanned Ground Vehicles (UGV) platform NAVIS. Several SOP sensors were installed on NAVIS for collecting indoor fingerprint information, including a digital compass collecting magnetic field intensity, a light sensor collecting light intensity, and a smartphone which collects the access point number and RSSIs of the pre-installed WiFi network. The NAVIS platform generates a map of the indoor environment and collects the SOPs during processing of the mapping, and then the SOP fingerprint database is interpolated and updated in real time. Field tests were carried out to evaluate the effectiveness and efficiency of the proposed method. The results showed that the fingerprint databases can be quickly created and updated with a higher sampling frequency (5Hz) and denser reference points compared with traditional methods, and the indoor map can be generated without prior information. Moreover, environmental changes could also be detected quickly for fingerprint indoor positioning.

A professional-quality, personal laser scanning (PLS) system for collecting tree attributes was demonstrated in this paper. The applied system, which is wearable by human operators, consists of a multi-constellation navigation system and an ultra-high-speed phase-shift laser scanner mounted on a rigid baseplate and consisting of a single sensor block. A multipass-corridor-mapping method was developed to process PLS data and a 2,000 m2 forest plot was utilized in the test. The tree stem detection accuracy was 82.6%; the root mean square error (RMSE) of the estimates of tree diameter at breast height (DBH) was 5.06 cm; the RMSE of the estimates of tree location was 0.38 m. The relative RMSE of the DBH estimates was 14.63%. The results showed, for the first time, the potential of the PLS system in mapping large forest plots. Further research on mapping accuracy in various forest conditions, data correction methods and multi-sensoral positioning techniques is needed. The utilization of this system in different applications, such as harvester operations, should also be explored. In addition to collecting tree-level and plot-level data for forest inventory, other possible applications of PLS for forest ecosystem services include mapping of canopy gaps, measuring leaf area index of large areas, documenting and visualizing forest routes feasible for recreation, hiking and berry and mushroom picking.

Accurate terrain models are a crucial component of studies of river channel evolution. In this paper we describe a new methodology for creating high-resolution seamless digital terrain models (DTM) of river channels and their floodplains. We combine mobile laser scanning and low-altitude unmanned aerial vehicle (UAV) photography-based methods for creating both a digital bathymetric model of the inundated river channel and a DTM of a point bar of a meandering sub-arctic river. We evaluate mobile laser scanning and UAV-based photogrammetry point clouds against terrestrial laser scanning and combine these data with an optical bathymetric model to create a seamless DTM of two different measurement periods. Using this multi-temporal seamless data, we calculate a DTM of difference that allows a change detection of the meander bend over a one-year period.

Vegetation plays an important role in stabilizing the soil and decreasing fluvial erosion. In certain cases, vegetation increases the accumulation of fine sediments. Efficient and accurate methods are required for mapping and monitoring changes in the fluvial environment. Here, we develop an area-based approach for mapping and monitoring the vegetation structure along a river channel. First, a 2 × 2 m grid was placed over the study area. Metrics describing vegetation density and height were derived from mobile laser-scanning (MLS) data and used to predict the variables in the nearest-neighbor (NN) estimations. The training data were obtained from aerial images. The vegetation cover type was classified into the following four classes: bare ground, field layer, shrub layer, and canopy layer. Multi-temporal MLS data sets were applied to the change detection of riverine vegetation. This approach successfully classified vegetation cover with an overall classification accuracy of 72.6%; classification accuracies for bare ground, field layer, shrub layer, and canopy layer were 79.5%, 35.0%, 45.2% and 100.0%, respectively. Vegetation changes were detected primarily in outer river bends. These results proved that our approach was suitable for mapping riverine vegetation.

High quality sedimentary measurements are required for studying fluvial geomorphology and hydrological processes e.g., flood and river dynamics. Mobile laser scanning (MLS) currently provides the opportunity to achieve high precision measurements of coarse fluvial sediments in a large survey area. Our study aims to investigate the capability of single-track MLS data for individual particle-based sediment modeling. Individual particles are firstly detected and delineated from a digital surface model (DSM) that is generated from the MLS data. 3D MLS points of each detected individual particle are then extracted from the point cloud. The grain size and the sphericity as well as the orientation of each individual particle are estimated based on the extracted MLS points. According to the evaluations conduced in the paper, it is possible to detect and to model sediment particles above 63 mm from a single-track MLS point cloud with a high reliability. The paper further discusses the strength and the challenges of individual particle-based approach for sedimentary measurement.

Mobile mapping systems (MMSs) are used for mapping topographic and urban features which are difficult and time consuming to measure with other instruments. The benefits of MMSs include efficient data collection and versatile usability. This paper investigates the data processing steps and quality of a boat-based mobile mapping system (BoMMS) data for generating terrain and vegetation points in a river environment. Our aim in data processing was to filter noise points, detect shorelines as well as points below water surface and conduct ground point classification. Previous studies of BoMMS have investigated elevation accuracies and usability in detection of fluvial erosion and deposition areas. The new findings concerning BoMMS data are that the improved data processing approach allows for identification of multipath reflections and shoreline delineation. We demonstrate the possibility to measure bathymetry data in shallow (0–1 m) and clear water. Furthermore, we evaluate for the first time the accuracy of the BoMMS ground points classification compared to manually classified data. We also demonstrate the spatial variations of the ground point density and assess elevation and vertical accuracies of the BoMMS data.

This study was dedicated to illustrating the significance of sensor manipulation in the case of terrestrial laser scanning, which is a field now in quick development. In fact, this quickness was mainly rooted in the emergence of new sensors with better performance, while the implications of sensor manipulation have not been fully recognized by the whole community. For this technical gap, the stop-and-go mapping mode can be reckoned as one of the potential solution plans. Stop-and-go was first proposed to handle the low efficiency of traditional static terrestrial laser scanning, and then, it was re-emphasized to improve the stability of sample collections for the state-of-the-art technology of mobile laser scanning. This work reviewed the previous efforts of trying the stop-and-go mode for improving the performance of static and mobile terrestrial laser scanning and generalized their principles respectively. This work also analyzed its advantages compared to the fully-static and fully-kinematic terrestrial laser scanning, and suggested the plans with more automatic measures for raising the efficacy of terrestrial laser scanning. Overall, this literature review indicated that the stop-and-go mapping mode as a case with generic sense can verify the presumption of sensor manipulation as essential as sensor development.

We introduce and test the performance of two sampling methods that utilize distance distributions of laser point clouds in terrestrial and mobile laser scanning geometries. The methods are leveled histogram sampling and inversely weighted distance sampling. The methods aim to reduce a significant portion of the laser point cloud data while retaining most characteristics of the full point cloud. We test the methods in three case studies in which data were collected using a different terrestrial or mobile laser scanning system in each. Two reference methods, uniform sampling and linear point picking, were used for result comparison. The results demonstrate that correctly selected distance-sensitive sampling techniques allow higher point removal than the references in all the tested case studies.

This study explores the feasibility of applying single-scan airborne, static terrestrial and mobile laser scanning for improving the accuracy of tree height growth measurement. Specifically, compared to the traditional works on forest growth inventory with airborne laser scanning, two issues are regarded: “Can the new technique characterize the height growth for each individual tree?” and “Can this technique refine the minimum growth-discernable temporal interval further?” To solve these two puzzles, the sampling principles of the three laser scanning modes were first examined, and their error sources against the task of tree-top capturing were also analyzed. Next, the three-year growths of 58 Nordic maple trees (Crimson King) for test were intermittently surveyed with one type of laser scanning each time and then analyzed by statistics. The evaluations show that the height growth of each individual tree still cannot be reliably characterized even by single-scan terrestrial laser scanning, and statistical analysis is necessary in this scenario. After Gaussian regression, it is found that the minimum temporal interval with distinguishable tree height growths can be refined into one month based on terrestrial laser scanning, far better than the two years deduced in the previous works based on airborne laser scanning. The associated mean growth was detected to be about 0.12 m. Moreover, the parameter of tree height generally under-estimated by airborne and even mobile laser scanning can be relatively revised by means of introducing static terrestrial laser scanning data. Overall, the effectiveness of the proposed technique is primarily validated.

The performance of various mobile laser scanning systems was tested on an established urban test field. The test was connected to the European Spatial Data Research (EuroSDR) project “Mobile Mapping—Road Environment Mapping Using Mobile Laser Scanning”. Several commercial and research systems collected laser point cloud data on the same test field. The system comparisons focused on planimetric and elevation errors using a filtered digital elevation model, poles, and building corners as the reference objects. The results revealed the high quality of the point clouds generated by all of the tested systems under good GNSS conditions. With all professional systems properly calibrated, the elevation accuracy was better than 3.5 cm up to a range of 35 m. The best system achieved a planimetric accuracy of 2.5 cm over a range of 45 m. The planimetric errors increased as a function of range, but moderately so if the system was properly calibrated. The main focus on mobile laser scanning development in the near future should be on the improvement of the trajectory solution, especially under non-ideal conditions, using both improvements in hardware and software. Test fields are relatively easy to implement in built environments and they are feasible for verifying and comparing the performance of different systems and also for improving system calibration to achieve optimum quality.

Mobile laser scanning is an emerging technology capable of capturing three-dimensional data from surrounding objects. With state-of-the-art sensors, the achieved point clouds capture object details with good accuracy and precision. Many of the applications involve civil engineering in urban areas, as well as traffic and other urban planning, all of which serve to make 3D city modeling probably the fastest growing market segment in this field. This article outlines multiplatform mobile laser scanning solutions such as vehicle- and trolley-operated urban area data acquisition, and boat-mounted equipment for fluvial environments. Moreover, we introduce a novel backpack version of mobile laser scanning equipment for surveying applications in the field of natural sciences where the requirements include precision and mobility in variable terrain conditions. In addition to presenting a technical description of the systems, we discuss the performance of the solutions in the light of various applications in the fields of urban mapping and modeling, fluvial geomorphology, snow-cover characterization, precision agriculture, and in monitoring the effects of climate change on permafrost landforms. The data performance of the mobile laser scanning approach is described by the results of an evaluation of the ROAMER on a permanent MLS test field. Furthermore, an in situ accuracy assessment using a field of spherical 3D targets for the newly-introduced Akhka backpack system is conducted and reported on.

We present two improvements for laser-based forest inventory. The first improvement is based on using last pulse data for tree detection. When trees overlap, the surface model between the trees corresponding to the first pulse stays high, whereas the corresponding model from the last pulse results in a drop in elevation, due to its better penetration between the trees. This drop in elevation can be used for separating trees. In a test carried out in Evo, Southern Finland, we used 292 forests plots consisting of more than 5,500 trees and airborne laser scanning (ALS) data comprised of 12.7 emitted laser pulses per m2. With last pulse data, an improvement of 6% for individual tree detection was obtained when compared to using first pulse data. The improvement increased with an increasing number of stems per plot and with decreasing diameter breast height (DBH). The results confirm that there is also substantial information for tree detection in last pulse data. The second improvement is based on the use of individual tree-based features in addition to the statistical point height metrics in area-based prediction of forest variables. The commonly-used ALS point height metrics and individual tree-based features were fused into the non-parametric estimation of forest variables. By using only four individual tree-based features, stem volume estimation improved when compared to the use of statistical point height metrics. For DBH estimation, the point height metrics and individual tree-based features complemented each other. Predictions were validated at plot level.

The objective of the “Tree Extraction” project organized by EuroSDR (European Spatial data Research) and ISPRS (International Society of Photogrammetry and Remote Sensing) was to evaluate the quality, accuracy, and feasibility of automatic tree extraction methods, mainly based on laser scanner data. In the final report of the project, Kaartinen and Hyyppä (2008) reported a high variation in the quality of the published methods under boreal forest conditions and with varying laser point densities. This paper summarizes the findings beyond the final report after analyzing the results obtained in different tree height classes. Omission/Commission statistics as well as neighborhood relations are taken into account. Additionally, four automatic tree detection and extraction techniques were added to the test. Several methods in this experiment were superior to manual processing in the dominant, co-dominant and suppressed tree storeys. In general, as expected, the taller the tree, the better the location accuracy. The accuracy of tree height, after removing gross errors, was better than 0.5 m in all tree height classes with the best methods investigated in this experiment. For forest inventory, minimum curvature-based tree detection accompanied by point cloud-based cluster detection for suppressed trees is a solution that deserves attention in the future.

Precise levelling is known for its accuracy and reliability in height determination, but the process itself is slow, laborious and expensive. We have started a project to study methods for height determination that could decrease the creation time of national height systems without losing the accuracy and reliability that is needed for them. In the pilot project described here, we study some of the alternative techniques with a pilot field test where we compared them with the precise levelling. The purpose of the test is not to evaluate the mutual superiority or suitability of the techniques, but to establish the background for a larger test and to find strong and weak points of each technique. The techniques chosen for this study were precise levelling, Mobile Laser Scanning (MLS) and Global Navigation Satellite System (GNSS) levelling, which included static Global Positioning System (GPS) and Virtual Reference Station (VRS) measurements. This research highlighted the differences of the studied techniques and gave insights about the framework and procedure for the later experiments. The research will continue in a larger scale, where the suitability of the techniques regarding the height systems is to be determined.

Radiometric calibration of airborne laser scanning (ALS) intensity data aims at retrieving a value related to the target scattering properties, which is independent on the instrument or flight parameters. The aim of a calibration procedure is also to be able to compare results from different flights and instruments, but practical applications are sparsely available, and the performance of calibration methods for this purpose needs to be further assessed. We have studied the radiometric calibration with data from three separate flights and two different instruments using external calibration targets. We find that the intensity data from different flights and instruments can be compared to each other only after a radiometric calibration process using separate calibration targets carefully selected for each flight. The calibration is also necessary for target classification purposes, such as separating vegetation from sand using intensity data from different flights. The classification results are meaningful only for calibrated intensity data.

The intensity information from terrestrial laser scanners (TLS) has become an important object of study in recent years, and there are an increasing number of applications that would benefit from the addition of calibrated intensity data to the topographic information. In this paper, we study the range and incidence angle effects on the intensity measurements and search for practical correction methods for different TLS instruments and targets. We find that the range (distance) effect is strongly dominated by instrumental factors, whereas the incidence angle effect is mainly caused by the target surface properties. Correction for both effects is possible, but more studies are needed for physical interpretation and more efficient use of intensity data for target characterization.

Nowadays, advanced real-time visualization for location-based applications, such as vehicle navigation or mobile phone navigation, requires large scale 3D reconstruction of street scenes. This paper presents methods for generating photorealistic 3D city models from raw mobile laser scanning data, which only contain georeferenced XYZ coordinates of points, to enable the use of photorealistic models in a mobile phone for personal navigation. The main focus is on the automated processing algorithms for noise point filtering, ground and building point classification, detection of planar surfaces, and on the key points (e.g., corners) of building derivation. The test site is located in the Tapiola area, Espoo, Finland. It is an area of commercial buildings, including shopping centers, banks, government agencies, bookstores, and high-rise residential buildings, with the tallest building being 45 m in height. Buildings were extracted by comparing the overlaps of X and Y coordinates of the point clouds between the cutoff-boxes at different and transforming the top-view of the point clouds of each overlap into a binary image and applying standard image processing technology to remove the non-building points, and finally transforming this image back into point clouds. The purpose for using points from cutoff-boxes instead of all points for building detection is to reduce the influence of tree points close to the building facades on building extraction. This method can also be extended to transform point clouds in different views into binary images for various other object extractions. In order to ensure the building geometry completeness, manual check and correction are needed after the key points of building derivation by automated algorithms. As our goal is to obtain photorealistic 3D models for walk-through views, terrestrial images were captured and used for texturing building facades. Currently, fully automatic generation of high quality 3D models is still challenging due to occlusions in both the laser and image data and due to significant illumination changes between the images. Especially when the scene contains both trees and vehicles, fully automated methods cannot achieve satisfactory visual appearance. In our approach, we employed the existing software for texture preparation and mapping.

Laser measurements have been used in a fluvial context since 1984, but the change detection possibilities of mobile laser scanning (MLS) for riverine topography have been lacking. This paper demonstrates the capability of MLS in erosion change mapping on a test site located in a 58 km-long tributary of the River Tenojoki (Tana) in the sub-arctic. We used point bars and river banks as example cases, which were measured with the mobile laser scanner ROAMER mounted on a boat and on a cart. Static terrestrial laser scanner data were used as reference and we exploited a difference elevation model technique for describing erosion and deposition areas. The measurements were based on data acquisitions during the late summer in 2008 and 2009. The coefficient of determination (R2) of 0.93 and a standard deviation of error 3.4 cm were obtained as metrics for change mapping based on MLS. The root mean square error (RMSE) of MLS‑based digital elevation models (DEM) for non-vegetated point bars ranged between 2.3 and 7.6 cm after correction of the systematic error. For densely vegetated bank areas, the ground point determination was more difficult resulting in an RMSE between 15.7 and 28.4 cm.

Accurate road environment information is needed in applications such as road maintenance and virtual 3D city modelling. Vehicle-based laser scanning (VLS) can produce dense point clouds from large areas efficiently from which the road and its environment can be modelled in detail. Pole-like objects such as traffic signs, lamp posts and tree trunks are an important part of road environments. An automatic method was developed for the extraction of pole-like objects from VLS data. The method was able to find 77.7% of the poles which were found by a manual investigation of the data. Correctness of the detection was 81.0%.

The intensity data produced by terrestrial laser scanners has become a topic of increasing interest in the remote sensing community. We present a case study of radiometric calibration for two phase-shift continuous wave (CW) terrestrial scanners and discuss some major issues in correcting and applying the intensity data, and a practical calibration scheme based on external reference targets. There are differences in the operation of detectors of different (although similar type) instruments, and the detector effects must be known in order to calibrate the intensity data into values representing the target reflectance. It is, therefore, important that the effects of distance and target reflectance on the recorded intensity are carefully studied before using the intensity data from any terrestrial laser scanner.