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Accurate assessments of forest resources rely on ground truth data that are collected via in-situ measurements, which are fundamental for all other statistical- and/or remote-sensing-based deductions on quantified forest attributes. The major bottleneck of the current in-situ observation system is that the data collection is time consuming, and, thus, limited in extent, which potentially biases any further inferences made. Consequently, conventional field-data-collection approaches can hardly keep pace with the coverage, scale and frequency required for contemporary and future forest inventories. In-situ measurements from mobile platforms seem to be a promising technique to solve this problem and are estimated at least 10 times faster than static techniques (e.g., terrestrial laser scanning, TLS) at the plot level. However, the mobile platforms are still at the very early stages of development, and it is unclear which three-dimensional (3D) forest measurements the mobile systems can provide and at what accuracy. This study presents a quantitative evaluation of the performance of mobile platforms in a variety of forest conditions and through a comparison with state-of-the-art static in-situ observations. Two mobile platforms were used to collect field data, where the same laser-scanning system was both mounted on top of a vehicle and wore by an operator. The static in-situ observation from TLS is used as a baseline for the evaluation. All point clouds involved were processed through the same processing chain and compared to conventional manual measurement. The evaluation results indicate that the mobile platforms can assess homogeneous forests as well as static observations, but they cannot yet assess heterogeneous forest as required by practical applications. The major challenge is twofold: mobile-data coverage and accuracy. Future research should focus on the robust registration techniques between strips, especially in complex forest conditions, since errors of data registration results in significant impacts ontree attributes estimation accuracy. In cases that the spatial inconstancy cannot be eliminated, attributes estimation in single strips, i.e., the multi-single-scan approach, is an alternative. Meanwhile, operator training deserves attention since the data quality from mobile platforms is partly determined by the operators’ selection of trajectory in the field.

One of the biggest challenges in forestry research is the effective and accurate measuring and monitoring of forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. This paper presented a novel computational method of low-cost forest inventory using global navigation satellite system (GNSS) signals in a crowdsourcing approach. Statistical features of GNSS signals were extracted from widely available GNSS devices and were used for predicting forest attributes, including tree height, diameter at breast height, basal area, stem volume, and above-ground biomass, in boreal forest conditions. The basic evidence of the predictions is the physical correlations between forest variables and the responses of GNSS signals penetrating through the forest. The random forest algorithm was applied to the predictions. GNSS-derived prediction accuracies were comparable with those of the most accurate 2-D remote sensing techniques, and the predictions can be improved further by integration with other publicly available data sources without additional cost. This type of crowdsourcing technique enables the collection of up-to-date forest data at low cost, and it significantly contributes to the development of new reference data collection techniques for forest inventory. Currently, field reference can account for half of the total costs of forest inventory.

Power line mapping using remote sensing can automate the traditionally labor-intensive power line corridor inspection. Land-based mobile laser scanning (MLS) can be a good choice for the power line mapping if an aerial inspection is impossible, too costly or slow, unsafe, prohibited by regulations, or if more detailed information on the power line corridor is needed. The mapping of the power lines using MLS was studied in a rural environment outside the road network for the first time. An automatic power line extraction algorithm was developed. The algorithm first found power line candidate points based on the shape and orientation of the local neighborhood of a point using principal component analysis. Power lines were retrieved from the candidates using random sample consensus (Ransac) and a new power line labeling method, which takes into account the three-dimensional shape of the power lines. The new labeling method was able to find the power lines and remove false detections, which were found, for example, from the forest. The algorithm was tested in forested and open field (arable land) areas, outside the road environment using two different platforms of MLS, namely, personal backpack and all-terrain vehicle. The recall and precision of the power line extraction were 93.3% and 93.6%, respectively, using 10 cm as a distance criterion for a successful detection. Drifting of the positioning solution of the scanner was the largest error source, being the (contributory) cause for 60–70% of the errors. The platform did not have a significant effect on the power line extraction accuracy. The accuracy was higher in the open field compared to the forest, because the one-dimensional point density along the power line was inhomogeneous and GNSS (global navigation satellite system) signal was weak in the forest. The results suggest that the power lines can be mapped accurately enough for inspection purposes using MLS in a rural environment outside the road network.

To secure uninterrupted distribution of electricity, effective monitoring and maintenance of power lines are needed. This literature review article aims to give a wide overview of the possibilities provided by modern remote sensing sensors in power line corridor surveys and to discuss the potential and limitations of different approaches. Monitoring of both power line components and vegetation around them is included. Remotely sensed data sources discussed in the review include synthetic aperture radar (SAR) images, optical satellite and aerial images, thermal images, airborne laser scanner (ALS) data, land-based mobile mapping data, and unmanned aerial vehicle (UAV) data. The review shows that most previous studies have concentrated on the mapping and analysis of network components. In particular, automated extraction of power line conductors has achieved much attention, and promising results have been reported. For example, accuracy levels above 90% have been presented for the extraction of conductors from ALS data or aerial images. However, in many studies datasets have been small and numerical quality analyses have been omitted. Mapping of vegetation near power lines has been a less common research topic than mapping of the components, but several studies have also been carried out in this field, especially using optical aerial and satellite images. Based on the review we conclude that in future research more attention should be given to an integrated use of various data sources to benefit from the various techniques in an optimal way. Knowledge in related fields, such as vegetation monitoring from ALS, SAR and optical image data should be better exploited to develop useful monitoring approaches. Special attention should be given to rapidly developing remote sensing techniques such as UAVs and laser scanning from airborne and land-based platforms. To demonstrate and verify the capabilities of automated monitoring approaches, large tests in various environments and practical monitoring conditions are needed. These should include careful quality analyses and comparisons between different data sources, methods and individual algorithms.

Automatic methods are needed to efficiently process the large point clouds collected using a mobile laser scanning (MLS) system for surveying applications. Machine-learning-based object recognition from MLS point clouds in a road and street environment was studied in order to create maps from the road environment infrastructure. The developed automatic processing workflow included the following phases: the removal of the ground and buildings, segmentation, segment classification, and object location estimation. Several novel geometry-based features, which were previously applied in autonomous driving and general point cloud processing, were applied for the segment classification of MLS point clouds. The features were divided into three sets, i.e., local descriptor histograms (LDHs), spin images, and general shape and point distribution features, respectively. These were used in the classification of the following roadside objects: trees, lamp posts, traffic signs, cars, pedestrians, and hoardings. The accuracy of the object recognition workflow was evaluated using a data set that contained more than 400 objects. LDHs and spin images were applied for the first time for machine-learning-based object classification in MLS point clouds in the surveying applications of the road and street environment. The use of these features improved the classification accuracy by 9.6% (resulting in 87.9% accuracy) compared with the accuracy obtained using 17 general shape and point distribution features that represent the current state of the art in the field of MLS; therefore, significant improvement in the classification accuracy was achieved. Connected component segmentation and ground extraction were the cause of most of the errors and should be thus improved in the future.

Terrestrial laser scanning data have become more and more commonly used in cryospheric studies as the commercial instruments are getting cheaper and more user-friendly. We have studied the usability of laser scanning intensity data in remote sensing of snow-covered surfaces by focusing on two topics: the effect of incidence angle on the intensity data and the depth which the backscattered laser beam represents. The measurements were made with a phase-based laser scanner using 650–690 nm wavelength. For some of the snow backscatter vs. depth studies measurements were also made with a pulse-based scanner at 905 nm. The incidence angle effect was studied by rotating a snow surface sample relative to the scanner and measuring the difference in the intensity values. The experiment was repeated for different snow types. The snow pack layer that the backscattered laser signal represents was studied by inserting black metal plates horizontally into the snow pack and measuring the changes in the intensity values with plates at different depths. The results suggest that the snow type has no effect on the incidence angle effect and that for dry snow the backscattering of the laser beam takes place from the very surface, but for wet snow, the majority of the signal is backscattered from 0.5 to 1 cm depth. An empirical correction function for the incidence angle effect is also presented.

The maintenance of riffle-pool sequences and morphological changes in the long-term have received little attention in the literature. The aims of this study are to determine morphological changes and riffle-pool maintenance in relation to flow conditions in a meandering river channel over a 5-yr period. Change detection was focused on riffle and pool maintenance in a river reach covering three successive meander bends. Changes in a meandering river channel were studied utilizing detailed digital terrain models and flow data. The results indicated that riffle-pool sequences are maintained by high discharge events and the development of pools and riffles was linked. During high discharges, the riverbed eroded on the concave sides and the inflexion points aggraded, causing riffle–pool sequences, whereas during low discharges, concave sides aggraded and inflexion points eroded, causing pool filling and riffle erosion. While discharge increased, near-bed flow velocities increased faster on the concave sides of thebends than at the inflexion points, becoming higher at a discharge of 8 m3/s, ~20% of the bankfull discharge. Changes in the three successive meander bends were mainly similar, and the geometry of meandering rivers contributed to the locations of riffles and pools. Pools and riffles were not stable in size and shape, but their longitudinal location remained the same, instead of migrating up and down the channel. Morphological changes occurred in meander bends year-round, but they were non-linear. Annual channel change was not similar from year to year owing to different flow regimes and morphological changes during the previous year. However, seasonal detection revealed similarities between high and low discharge periods between the years. Concave sides of meander bends may act to temporarily store sediment; however, storage is preserved only under the effective hydrological discharge.

This paper describes the possibilities of the Optech Titan multispectral airborne laser scanner in the fields of mapping and forestry. Investigation was targeted to six land cover classes. Multispectral laser scanner data can be used to distinguish land cover classes of the ground surface, including the roads and separate road surface classes. For forest inventory using point cloud metrics and intensity features combined, total accuracy of 93.5% was achieved for classification of three main boreal tree species (pine, spruce and birch).When using intensity features – without point height metrics - a classification accuracy of 91% was achieved for these three tree species. It was also shown that deciduous trees can be further classified into more species. We propose that intensity-related features and waveform-type features are combined with point height metrics for forest attribute derivation in area-based prediction, which is an operatively applied forest inventory process in Scandinavia. It is expected that multispectral airborne laser scanning can provide highly valuable data for city and forest mapping and is a highly relevant data asset for national and local mapping agencies in the near future.

In this paper, we demonstrate the feasibility of using the Microsoft Kinect and Google Tango frame-based depth sensors for individual tree stem measurements and reconstruction for the purpose of forest inventory. Conventionally field reference data in forest inventory are collected at tree and sample plot level by means of manual measurements (e.g., a caliper), which are both labor-intensive and time-consuming. In this study, color (i.e., red, green and blue channels, RGB) and range images acquired by a Kinect and Tango systems were processed and used to extract tree diameter measurements for the individual tree stems. For this, 121 reference stem diameter measurements were made with tape and caliper. Kinect-derived tree diameters agreed with tape measurements to a 1.90 cm root-mean-square error (RMSE). The stem curve from the ground to the diameter at breast height agreed with a bias of 0.7 cm and random error of 0.8 cm with respect to the reference trunk. For Tango measurements, the obtained stem diameters matched those from tape measurement with an RMSE of 0.73 cm, having an average bias of 0.3 cm. As highly portable and inexpensive systems, both Kinect and Tango provide an easy way to collect tree stem diameter and stem curve information vital to forest inventory. These inexpensive instruments may in future compete with both terrestrial and mobile laser scanning or conventional fieldwork using calipers or tape. Accuracy is adequate for practical applications in forestry. Measurements made using Kinect and Tango type systems could also be applied in crowdsourcing context.

Various field-based studies of meander development have been realised, yet fluviomorphological processes and their spatial patterns over bends are not fully understood. In this paper, the processes of three natural meander bends during a flood event are dissected using a unique study approach, which combines traditional field surveying, close-range remote sensing, and computational modelling. The combined study approach improves the spatial and temporal resolutions of the data compared to traditional survey methods, although the bathymetric data collection needs improvement. The analyses indicate that the influence of a flood on the morphological changes of point bars depends on the stage of the bend's development. The point bars may also experience lateral growth during low discharge. However, the point bar is too large a unit to find simplified relationships between the discharge and morphological changes. Instead, one should concentrate on different parts of the bend and the point bar. In general, the longer the inundation period of one part, the more probable that the area will experience net deposition. The magnitude of the sediment transport and the net morphological change are not interconnected: Areas of net erosion and deposition can experience either high or low sediment transport rates during a flood. The results are compared with earlier theories of meander development, which are also assessed.

Stereo images have long been the main practical data source for the high-accuracy retrieval of 3-D information over large areas. However, stereoscopy has been surpassed by laser scanning (LS) techniques in recent years, particularly in forested areas, because the reflection of laser points from object surfaces directly provides 3-D geometric features and because the laser beam has good penetration capacity through forest canopies. In the last few years, image-based point clouds have become a more widely available data source because of advances in matching algorithms and computer hardware. This paper explores the possibility of using consumer cameras for forest field data collection and presents an application of terrestrial image-based point clouds derived from a handheld camera to forest plot inventories. In the experiment, the sample forest plot was photographed in a stop-and-go mode using different routes and camera settings. Five data sets were generated from photographs taken in the field, representing different photographic conditions. The stem detection accuracy ranged between 60% and 84%, and the root-mean-square errors of the estimated diameters at breast height were between 2.98 and 6.79 cm. The performance of image-based point clouds in forest data collection was compared with that of point clouds derived from two LS techniques, i.e., terrestrial LS (the professional level) and personal LS (an emerging technology). The study indicates that the construction of image-based point clouds of forest field data requires only low-cost, low-weight, and easy-to-use equipment and automated data processing. Photographic measurement is easy and relatively fast. The accuracy of tree attribute estimates is close to an acceptable level for forest field inventory but is lower than that achieved with the tested LS techniques.

Mobile laser scanning (MLS) provides kinematic means to collect three dimensional data from surroundings for various mapping and environmental analysis purposes. Vehicle based MLS has been used for road and urban asset surveys for about a decade. The equipment to derive the trajectory information for the point cloud generation from the laser data is almost without exception based on GNSS-IMU (Global Navigation Satellite System – Inertial Measurement Unit) technique. That is because of the GNSS ability to maintain global accuracy, and IMU to produce the attitude information needed to orientate the laser scanning and imaging sensor data. However, there are known challenges in maintaining accurate positioning when GNSS signal is weak or even absent over long periods of time. The duration of the signal loss affects the severity of degradation of the positioning solution depending on the quality/performance level of the IMU in use. The situation could be improved to a certain extent with higher performance IMUs, but increasing system expenses make such approach unsustainable in general. Another way to tackle the problem is to attach additional sensors to the system to overcome the degrading position accuracy: such that observe features from the environment to solve for short term system movements accurately enough to prevent the IMU solution to drift. This results in more complex system integration with need for more calibration and synchronization of multiple sensors into an operational approach. In this paper we study operation of an ATV (All -terrain vehicle) mounted, GNSS-IMU based single scanner MLS system in boreal forest conditions. The data generated by RoamerR2 system is targeted for generating 3D terrain and tree maps for optimizing harvester operations and forest inventory purposes at individual tree level. We investigate a process-flow and propose a graph optimization based method which uses data from a single scanner MLS for correcting the post-processed GNSS-IMU trajectory for positional drift under mature boreal forest canopy conditions. The result shows that we can improve the internal conformity of the data significantly from 0.7 m to 1 cm based on tree stem feature location data. When the optimization result is compared to reference at plot level we reach down to 6 cm mean error in absolute tree stem locations. The approach can be generalized to any MLS point cloud data, and provides as such a remarkable contribution to harness MLS for practical forestry and high precision terrain and structural modeling in GNSS obstructed environments.

Interest in measuring forest biomass and carbon stock has increased as a result of the United Nations Framework Convention on Climate Change, and sustainable planning of forest resources is therefore essential. Biomass and carbon stock estimates are based on the large area estimates of growing stock volume provided by national forest inventories (NFIs). The estimates for growing stock volume based on the NFIs depend on stem volume estimates of individual trees. Data collection for formulating stem volume and biomass models is challenging, because the amount of data required is considerable, and the fact that the detailed destructive measurements required to provide these data are laborious. Due to natural diversity, sample size for developing allometric models should be rather large. Terrestrial laser scanning (TLS) has proved to be an efficient tool for collecting information on tree stems. Therefore, we investigated how TLS data for deriving stem volume information from single trees should be collected. The broader context of the study was to determine the feasibility of replacing destructive and laborious field measurements, which have been needed for development of empirical stem volume models, with TLS. The aim of the study was to investigate the effect of the TLS data captured at various distance (i.e. corresponding 25%, 50%, 75% and 100% of tree height) on the accuracy of the stem volume derived. In addition, we examined how multiple TLS point cloud data acquired at various distances improved the results. Analysis was carried out with two ways when multiple point clouds were used: individual tree attributes were derived from separate point clouds and the volume was estimated based on these separate values (multiple scan A), and point clouds were georeferenced as a combined point cloud from which the stem volume was estimated (multiple-scan B). This permitted us to deal with the practical aspects of TLS data collection and data processing for development of stem volume equations in boreal forests. The results indicated that a scanning distance of approximately 25% of tree height would be optimal for stem volume estimation with TLS if a single scan was utilized in boreal forest conditions studied here and scanning resolution employed. Larger distances increased the uncertainty, especially when the scanning distance was greater than approximately 50% of tree height, because the number of successfully measured diameters from the TLS point cloud was not sufficient for estimating the stem volume. When two TLS point clouds were utilized, the accuracy of stem volume estimates was improved: RMSE decreased from 12.4% to 6.8%. When two point clouds were processed separately (i.e. tree attributes were derived from separate point clouds and then combined) more accurate results were obtained; smaller RMSE and relative error were achieved compared to processing point clouds together (i.e. tree attributes were derived from a combined point cloud). TLS data collection and processing for the optimal setup in this study required only one sixth of time that was necessary to obtain the field reference. These results helped to further our knowledge on TLS in estimating stem volume in boreal forests studied here and brought us one step closer in providing best practices how a phase-shift TLS can be utilized in collecting data when developing stem volume models. (C) 2016 The Authors. Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

In this letter, we demonstrate the potential of a small, robust, and low-cost mobile scanner for snow-depth studies. Snow-surface model and depth data are needed for purposes such as flood forecasting, agriculture, optimal management of water resources, and in formulating global climate-change scenarios. Traditionally, manual snow-depth measurements are laborious, time-consuming, and costly. A mobile mapping system comprised of an Ibeo Lux laser scanner, operating at a wavelength of 905 nm, and a NovAtel SPAN-CPT inertial navigation system was used to produce geo-referenced point clouds. The data were acquired first in the fall when the ground was free of snow and then a second time in winter when there was snow on the ground. Reference values from a total of 94 locations were collected with an RTK GPS pole. The maximum reference snow depth was 80 cm. The obtained snow-depth bias was 0.3 cm, which indicated that the proposed data-processing approach was capable of avoiding errors due to laser penetration into the snow, and the root-mean-squared error was 5.5 cm. Mobile laser scanning appears to be a promising technology for cryospheric studies having potential, e.g., to calibrate gravimetric measurements and earth observation satellite data, especially when reference data are needed for areas that are too large for terrestrial laser scanning.

Terrestrial laser scanning (TLS) has been demonstrated to be an efficient measurement method in plot-level forest inventories. A permanent sample plot in national forest inventories is typically a small area of forest with a radius of approximately 10 m. In practice, whether reference data can be automatically and accurately collected for larger plot sizes is of great interest. It is expensive to collect references in large areas utilizing conventional measurement tools. The application of static TLS is a possible choice but is very challenging due to its lack of mobility. In this letter, a mobile laser scanning (MLS) system was tested and its implications for forest inventories were discussed. The system is composed of a high performance laser scanner, a navigation unit, and a six-wheeled all-terrain vehicle. In this experiment, about 0.4 ha forest area was mapped utilizing the MLS system. The stem mapping accuracy was 87.5%; the root mean square errors of the estimations of the diameter at breast height and the location were 2.36 cm and 0.28 m, respectively. These results indicate that the MLS system has the potential to accurately map large forest plots and further research on mapping accuracy and cost-benefit analyses is needed.

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.

This study proposed the development plan of a novel aerial-to-ground remote sensing (AGRS) system for surveying the land scenes of interest. Specifically, the AGRS system is composed by integrating an unmanned aerial vehicle (UAV) imaging system and a mobile mapping system (MMS), onboard whose platform a control station is also added. The UAV-MMS-collaboration can be classified into two modes - loosely and tightly, respectively related to two efficacy levels of the AGRS - fine-scale mapping in general and target investigating in special cases. The latter scenario can be illustrated by the tasks of fast-responses to the time-critical events, e.g., seeking the accessible roads into disaster areas. These all pose challenging issues. To ensure the premise for AGRS development, a field test was carried out in prior to examine the collaborative effect between its two RS-functional modules. Two typical topics were explored, i.e., self-indicated orthorectification of the UAV images and landcover classification based on information fusion. The final positive results have basically validated the feasibility of the development of the AGRS system.

The topic of positioning in dense forest has attracted researchers' attention in the last few decades. Currently, accurate and robust positioning in complex forest environment is still not available by using standalone GNSS, and it is a major drawback for forestry automation. Laser scanning-based SLAM demonstrated its potential as an alternative positioning solution in dense boreal forest environment. However, it has difficulties in an open forest due to lack of features. In this paper, we propose a map-matching method to cover that gap. We utilize the existing forest map information, such as the center and radius of stems, and match it with the observed point cloud, which helps mitigate position errors. Field test results show that it is feasible to filter out the noisy measurements and reliably detect stem features in a boreal forest.

Backpack laser scanning systems have emerged recently enabling fast data collection and flexibility to make measurements also in areas that cannot be reached with, for example, vehicle-based laser scanners. Backpack laser scanning systems have been developed both for indoor and outdoor use. We have developed a quality analysis process in which the quality of backpack laser scanning data is evaluated in the forest environment. The reference data was collected with an unmanned aerial vehicle (UAV) laser scanning system. The workflow included noise filtering, division of data into smaller patches, ground point extraction, ground data decimation, and ICP registration. As a result, we managed to observe the misalignments of backpack laser scanning data for 97 patches each including data from circa 10 seconds period of time. This evaluation revealed initial average misalignments of 0.227 m, 0.073 and -0.083 in the easting, northing and elevation directions, respectively. Furthermore, backpack data was corrected according to the ICP registration results. Our correction algorithm utilized the time-based linear transformation of backpack laser scanning point clouds. After the correction of data, the ICP registration was run again. This revealed remaining misalignments between the corrected backpack laser scanning data and the original UAV data. We found average misalignments of 0.084, 0.020 and -0.005 meters in the easting, northing and elevation directions, respectively.

Forests have important impacts on the global carbon cycle and climate, and they are also related to a wide range of industrial sectors. Currently, one of the biggest challenges in forestry research is effectively and accurately measuring and monitoring forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. A low-cost crowdsourcing solution is needed for forest inventory to collect forest variables. Here, we propose global navigation satellite system (GNSS) signals as a novel type of observables for predicting forest attributes and show the feasibility of utilizing GNSS signals for estimating important attributes of forest plots, including mean tree height, mean diameter at breast height, basal area, stem volume and tree biomass. The prediction accuracies of the proposed technique were better in boreal forest conditions than those of the conventional techniques of 2D remote sensing. More importantly, this technique provides a novel, cost-effective way of collecting large-scale forest measurements in the crowdsourcing context. This technique can be applied by, for example, harvesters or persons hiking or working in forests because GNSS devices are widely used, and the field operation of this technique is simple and does not require professional forestry skills.

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.

As a state-of-the-art technology for fine-scale forest investigation, mobile terrestrial light detection and ranging (LiDAR), also referred to as mobile laser scanning (MLS), recently has been increasingly exploited to improve its performance on this task. One potential solution is to apply special MLS systems with the capability of collecting multiple echoes per pulse (multi-echoes, for short) from canopies. The methodologies of this rationale turned out to perform better but still insufficiently for canopy properties retrieval, owing to their common defective premise. That is, the knowledge of the characteristics of MLS scan data comprising multi-echoes, so far, is in shortage, especially when regarding the dynamic process of tree foliation. As a pioneering work for this challenge, this study attempted to comprehensively analyze the characteristics of MLS multi-echoes collected from tree crowns during foliation. Specifically, new stable multi-echoes-related features were deduced under the schem...

Accurate tree stem location distribution is the basic information for biomass estimation in forest inventory. Combining Inertial Measurement Unit (IMU) with Global Navigation Satellite System (GNSS) is a commonly used positioning strategy in most Mobile Laser Scanning (MLS) system for accurate forest mapping. Coupled with a tactical grade even a consumer grade IMU, GNSS offers satisfactory solution in open forest environment and a positioning accuracy better than decimeter can be achieved. However, for such MLS system, positioning in a mature and dense forest is still a challenging task, because of the loss of GNSS signals attenuated by thick canopy of the forest. Most often laser scanning sensors in MLS systems are used for mapping and modeling sensor, not for positioning. In this paper, we investigate a Simultaneous Localization and Mapping (SLAM) aided positioning solution with point clouds collected by a small footprint LiDAR. Based on the field test data, we evaluate the potential of how SLAM can be utilized for positioning and mapping in forest inventory. The results show that the positioning accuracy in the selected test field is improved by 38% when compared to a traditional tactical grade IMU+GNSS positioning system in a mature forest environment and as such, we are able to produce a clearer tree distribution map.

This article presents a three-level frame and a novel algorithm, which is based on a searching schematic analogous to a revolving door (RD), for automatic detection of individual trees from the point clouds acquired by mobile laser scanning (MLS). As a state-of-the-art technology, MLS in collecting the structural information of single trees is different from airborne laser scanners and terrestrial laser scanners in terms of sampling density, view angle and flexibility. To improve the efficiency and accuracy during extraction of trees, the three-level frame and the RD-schematic algorithm are proposed to fully employ the morphological characteristics of canopy surface models from coarse to fine resolutions. Methods of radius thresholding and symmetry judgement are also combined to remove disturbing objects, for example buildings and poles. Experiments based on the real-surveyed point clouds measured by the Sensei MLS system basically validate the proposed work, and the attributions introduced in this articl...

Investigating individual crown attributes yields insight into forest canopy properties over large areas; however, effectively mapping single crowns is still an insufficiently exploited topic. Static terrestrial laser scanning (TLS) can render detailed descriptions of crowns and may serve as a substitute for in-field manual measurements. Preliminary work has also accumulated many TLS–crown reference relationships between TLS samplings and specific crown attributes. However, usage of this inventory technique suffers from the need to place TLS systems in multiple locations, which is a laborious process. Mobile terrestrial laser scanning (MLS) is a state-of-the-art survey technology that shows potential for overcoming this limitation because of its strength in efficiently mapping objects. This study attempts to put forward a new methodology with the potential to investigate crown-level properties, particularly during foliation. The rationale is to first explore correlations between MLS- and TLS-derived predic...