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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.

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.

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.

Topographic databases maintained by national mapping agencies are currently the most common nationwide data sets in geo-information. The application of laser scanning as source data for surveying is increasing. Along with this development, several analysis methods that utilize dense point clouds have been introduced. We present the concept of producing a dense nationwide point cloud, produced from multiple sensors and containing multispectral information, as the national core data for geo-information. Geo-information products, such as digital terrain and elevation models and 3D building models, are produced automatically from these data. We outline the data acquisition, processing, and application of the point cloud. As a national data set, a dense multispectral point cloud could produce significant cost savings via improved automation in mapping and a reduction of overlapping surveying efforts.

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.

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.

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...

Light detection and ranging (LIDAR) systems based on unmanned aerial vehicles (UAVs) recently are in rapid advancement, while mini-UAV-borne laser scanning has few reported progress, notwithstanding so extensively required. This study established a pioneered mini-UAV-borne LIDAR system - Sensei, schematically with an Ibeo Lux scanner mounted on a small Align T-Rex 600E helicopter. Furthermore, the associated data processing involved in the coordinate triple, pulse intensity, and multiechoes per pulse was explored to validate its applicability for fine-scale mapping, in terms of, e.g., tree height estimation, pole detection, road extraction, and digital terrain model refinement. The feasibility and advantages of mini-UAV-borne LIDAR have been demonstrated by the promising results based on the real-measured data.

This paper presents a novel low-cost mini-UAV-based laser scanning system, which is also capable of performing car-based mobile mapping. The quality of the system and its feasibility for tree measurements was tested using the system’s laser scanner. The system was constructed as a modular measurement system consisting of a number of measurement instruments: a GPS/IMU positioning system, two laser scanners, a CCD camera, a spectrometer and a thermal camera. An Ibeo Lux and a Sick LMS151 profile laser were integrated into the system to provide dense point clouds; intensities of the reflected echoes can also be obtained with the Sick LMS. In our tests, when using a car as a platform, the pole-type object extraction algorithm which was developed resulted in 90% completeness and 86% correctness. The heights of pole-type objects were obtained with a bias of −1.6 cm and standard deviation of 5.4 cm. Using a mini-UAV as the platform, the standard deviation of individual tree heights was about 30 cm. Also, a digital elevation model extraction was tested with the UAV data, resulting in a height offset of about 3.1 cm and a standard deviation of 9.2 cm. With a multitemporal point cloud, we demonstrated a method to derive the biomass change of a coniferous tree with an R 2 value of 0.92. The proposed system is capable of not only recording point cloud data giving the geometry of the objects, but also simultaneously collecting image data, including overlapping images and the intensity of laser backscatter, as well as hyperspectral and thermal data. Therefore we believe that the system is feasible for new algorithm and concept development and for basic research, especially when data is recorded multitemporally.

We present a new approach for radiometric calibration of light detection and ranging (LIDAR) intensity data and demonstrate an application of this method to natural targets. The method is based on 1) using commercially available sand and gravel as reference targets and 2) the calibration of these reference targets in the laboratory conditions to know their backscatter properties. We have investigated the target properties crucial for accurate and consistent reflectance calibration and present a set of ideal targets easily available for calibration purposes. The first results from LIDAR-based brightness measurement of grass and sand show that the gravel-based calibration approach works in practice, is cost effective, and produces statistically meaningful results: Comparison of results from two separate airborne laser scanning campaigns shows that the relative calibration produces repeatable reflectance values.

Automated processing of the data provided by a laser-based mobile mapping system will be a necessity due to the huge amount of data produced. In the future, vehicle-based laser scanning, here called mobile mapping, should see considerable use for road environment modelling. Since the geometry of the scanning and point density is different from airborne laser scanning, new algorithms are needed for information extraction. In this paper, we propose automatic methods for classifying the road marking and kerbstone points and modelling the road surface as a triangulated irregular network. On the basis of experimental tests, the mean classification accuracies obtained using automatic method for lines, zebra crossings and kerbstones were 80.6%, 92.3% and 79.7%, respectively.

This paper proposed ambient light (ambilight) as a new type of signal sources for positioning. The possibility and methods of ambilight positioning were presented in this paper. It has been shown that two kinds of observables of ambient light can be used for positioning through different principles. Ambilight intensity spectrum measurements have highly location dependency, and they can be used for positioning with the traditional fingerprinting approach. Total ambilight irradiance intensity is used to detect the proximity of a lighting source, and a location solution can be further resolved with the support of knowledge of lighting infrastructure. Ambilight positioning can work in areas where other traditional techniques are not able to function. An ambilight sensor is cost-efficient and miniature in size, and it can be easily integrated with other sensors to form a hybrid positioning system. This paper was concluded with discussions on the possibility, applicability, challenges and outlook of the new ambient light positioning techniques.

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.

Laser scan matching with grid-based maps is a promising tool for real-time indoor positioning of mobile Unmanned Ground Vehicles (UGVs). While there are critical implementation problems, such as the ability to estimate the position by sensing the unknown indoor environment with sufficient accuracy and low enough latency for stable vehicle control, further development work is necessary. Unfortunately, most of the existing methods employ heuristics for quick positioning in which numerous accumulated errors easily lead to loss of positioning accuracy. This severely restricts its applications in large areas and over lengthy periods of time. This paper introduces an efficient real-time mobile UGV indoor positioning system for large-area applications using laser scan matching with an improved probabilistically-motivated Maximum Likelihood Estimation (IMLE) algorithm, which is based on a multi-resolution patch-divided grid likelihood map. Compared with traditional methods, the improvements embodied in IMLE include: (a) Iterative Closed Point (ICP) preprocessing, which adaptively decreases the search scope; (b) a totally brute search matching method on multi-resolution map layers, based on the likelihood value between current laser scan and the grid map within refined search scope, adopted to obtain the global optimum position at each scan matching; and (c) a patch-divided likelihood map supporting a large indoor area. A UGV platform called NAVIS was designed, manufactured, and tested based on a low-cost robot integrating a LiDAR and an odometer sensor to verify the IMLE algorithm. A series of experiments based on simulated data and field tests with NAVIS proved that the proposed IMEL algorithm is a better way to perform local scan matching that can offer a quick and stable positioning solution with high accuracy so it can be part of a large area localization/mapping, application. The NAVIS platform can reach an updating rate of 12 Hz in a feature-rich environment and 2 Hz even in a feature-poor environment, respectively. Therefore, it can be utilized in a real-time application.

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.

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%.

Recently, research towards using 3D city models for personal navigation has been rapidly increasing. In this paper, an approach for 3D city model reconstruction for the application of mobile phone-based navigation is presented, which is based on data collected from vehicle-based mobile mapping system (MMS). Our method is performed based on three objectives: small model size, perfect accuracy control as well as good visual effect. Small model size is achieved by simplified object geometry and reduced texture resolution. Model accuracy is controlled by extracting building outlines from classified point cloud and overlapping with final 3D model. Model completeness is checked by comparing resulting model with original images. Good visual effect is realized by applying photo-realistic texture. Photorealistic texture provides rich information for the reconstructed 3D scene. By applying this approach, in test area, 3D city model is successfully reconstructed.

Mobile mapping is a new way of efficiently collecting three-dimensional data from the road environment. Mobile mapping systems are cost efficient and robust technique to acquire information about even highly dynamic environments like highways and urban streets, where the data collection has previously been laborious and even dangerous for the staff performing the surveying. The dynamic mobile mapping systems could access the site with less risk to the personnel and with less need for road closures. The need for high resolution and details captured in to the data for street and road inventories, or city modelling, are the main reasons for the rapid adoption of the mobile mapping techniques in these fields.

We evaluated an accuracy and efficiency of laser measurement methods that can be utilized in urban tree mapping and monitoring. In this paper we describe our methods and show the first results of the study. In the field, altogether 44 urban trees located in park environment were measured and mapped from our pilot area located in Seurasaari, Helsinki, Finland. Several laser scanning methods were compared and tested beside direct GPS measurements. These methods included airborne, terrestrial and vehicle-based laser scanning. Tree mapping was done straight from the point clouds. The most cost-efficient method to produce tree maps from the scratch would be ALS combined with field measurements. Our first results showed also great potential of vehicle based laser scanning to be used in updating of existing tree maps and monitoring urban forest environment. The mapping accuracy was in line with traditionally used tachymeter measurements. All of these laser-based measurements were more cost-efficient than tachymeter measurements.

The vehicle-based laser scanning (VLS, also known as mobile mapping) is a new technology, which is currently under development for creating 3D models of the surrounding environment. VLS is based on the integration of GPS, IMU, laser scanner and preferably digital cameras mounted on top of a moving platform, i.e. a car in most applications. VLS is a logical development after the first operative Airborne Laser Scanner (ALS) in 1994 and Terrestrial Laser Scanners mounted on top of a tripod. The data/image processing of VLS are mainly based on modifications of the methods created for ALS and TLS taking into account the differences of VLS compared to ALS and TLS. Compared to ALS, the geometry of VLS scanning is different and the pulse density varies as function of range. Two main differences between stationary TLS and constantly moving VLS are the evenness of the data and the perspective. In VLS, the point cloud is evenly distributed along the driving direction, and the viewing direction to the target remains constant. In the stop-and-go mode, the data characteristics of the VLS and conventional TLS are similar. A reasonable amount of research has been done to develop methods for single-time VLS processing, but there have not been any attempts to our knowledge of multitemporal processing of VLS data. In this paper, the high potential of change detection based on multitemporal VLS point clouds was demonstrated. Example cases include the change detection of city models and defoliation of city trees. A method to map biomass and biomass change of (city) trees was developed.

The increasing availability in multiple data sources acquired by different sensor platforms has provided the great advantages for desired result achievement. This paper proposes the use of both mobile laser scanning (MLS) data and Unmanned Aerial Vehicle (UAV) images for 3D model reconstruction. Due to no available exterior orientation parameters for UAV images, the first task is to georeference these images to 3D points. In order to fast and accurate acquire 3D points which are also easy to be found the corresponding locations on UAV images, automated pole extraction from MLS was developed. After georeferencing UAV images, building roofs are acquired from those images and building walls are extracted from MLS data. The roofs and the walls are combined to achieve the complete building models.