1504 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 11, NO. 9, SEPTEMBER 2014

The Use of a Mobile Laser Scanning Systemfor Mapping Large Forest Plots

Xinlian Liang, Juha Hyyppä, Antero Kukko, Harri Kaartinen, Anttoni Jaakkola, and Xiaowei Yu

Abstract—Terrestrial laser scanning (TLS) has been demon-strated to be an efficient measurement method in plot-level forestinventories. A permanent sample plot in national forest inventoriesis typically a small area of forest with a radius of approximately10 m. In practice, whether reference data can be automatically andaccurately collected for larger plot sizes is of great interest. It isexpensive to collect references in large areas utilizing conventionalmeasurement tools. The application of static TLS is a possiblechoice but is very challenging due to its lack of mobility. In thisletter, a mobile laser scanning (MLS) system was tested and itsimplications for forest inventories were discussed. The system iscomposed of a high performance laser scanner, a navigation unit,and a six-wheeled all-terrain vehicle. In this experiment, about0.4 ha forest area was mapped utilizing the MLS system. The stemmapping accuracy was 87.5%; the root mean square errors ofthe estimations of the diameter at breast height and the locationwere 2.36 cm and 0.28 m, respectively. These results indicate thatthe MLS system has the potential to accurately map large forestplots and further research on mapping accuracy and cost–benefitanalyses is needed.

Index Terms—Forestry, mobile laser scanning (MLS), mobilemapping, point cloud, remote sensing, terrestrial laser scanning(TLS).

I. INTRODUCTION

R EFERENCE data collected from sample plots are fun-damental parameters for forest-related inventories and

ecological studies. Tree attributes from sample plots are neededto measure essential forest data utilized in decision-makingprocesses. A permanent sample plot is a small area of forest,e.g., a circular area with a radius of approximately 10 m in thenational forest inventories. Sample plots are typically small insize because it is very demanding and mostly impractical forforest inventories to collect reference data on circular plots withradii larger than 10 to 12 m using conventional measurementmethods, particularly if the stem count per hectare is high.Large sample plots are desirable, however, because a large sam-ple plot not only provides a more accurate and comprehensiveunderstanding of the forest environment but also makes theregistration of ground references and airborne remote sensingdata easier [1].

New instruments and methods have been continuously de-veloped to improve the accuracy and efficiency of field mea-

Manuscript received September 9, 2013; revised November 7, 2013;accepted December 13, 2013. Date of publication January 22, 2014; date ofcurrent version March 14, 2014. This work was supported by the Academy ofFinland under projects “Science and Technology toward Precision Forestry”and under the Centre of Excellence in Laser Scanning Research (projectdecision number 272195).

The authors are with the Department of Remote Sensing and Photogram-metry, Finnish Geodetic Institute, 02431 Masala, Finland (e-mail: xinlian.liang@fgi.fi; juha.hyyppa@fgi.fi; antero.kukko@fgi.fi; harri.kaartinen@fgi.fi;anttoni.jaakkola@fgi.fi; yu.xiaowei@fgi.fi).

Digital Object Identifier 10.1109/LGRS.2013.2297418

surements. More recently, terrestrial laser scanning (TLS),also known as ground-based LiDAR, has been shown to bea promising technique for forest field inventories at the plotlevel [2], [3]. The main advantage of using TLS in forest fieldinventories lies in its capacity to document the forest in detail.Research has shown that TLS can automatically provide accu-rate tree attributes that are measurable utilizing conventionaltools (e.g., calipers and measuring tapes), such as tree locationsand diameter-at-breast-height (DBH) [4]–[8], and tree attributesthat are not measurable noninvasively utilizing conventionaltools, such as stem volume [9] and biomass [10]. In addition,TLS data also permit automated time series analyses [11].

Three different TLS plot-level measurement techniques havebeen reported, namely single-scan, multi-scan, and multi-single-scan (MSS) approach [1]–[17]. In the single-scan ap-proach, the laser scanner is placed at the center of the plot,creating only one full field-of-view scan (e.g., 360-by-310◦),and all trees are mapped from the single-scan point cloud. In themultiscan approach, several scans are made simultaneously in-side and outside of the plot to collect point clouds representingall trees within the plot. These scans are accurately coregisteredby utilizing artificial reference targets that are manually placedthroughout the plot. In the MSS approach, the plots are scannedfrom several positions inside and outside of the plot. However,artificial reference targets are not used. The sample plots arefirst mapped in individual scans and later mapped by mergingindividual scans at feature and decision levels.

The single-scan method is not suitable for mapping a largeforest area if the information of all trees is supposed to becollected. In the single-scan data, the objects behind the nearestsurfaces in the direction of the laser beams are missed [18].Studies have shown that 10–32% of all trees in the sample plotare not scanned from the plot center [8], [16]–[18]. The multi-scan approach is assumed to be the most accurate technique formapping a sample plot. However, mapping a large forest areautilizing multiscan method has some limitations. The placementof artificial targets and the manual or semiautomated regis-tration of several scans can be time consuming and thereforemake the cost prohibitive in practice. The MSS approach hasthe potential to be utilized as a practical method to map largerforest areas [1]. The difficulty of this approach is the lack ofmobility. New techniques and methods are needed to collecttree attributes in larger forest plots to reduce the time requiredin mapping the study area.

Mobile mapping system (MMS) is a potential solution tomap large forest areas. MMS is a multisensor system integratedon a moving platform. The platform is usually a car in the urbanenvironment. The positioning and navigation system typicallyinclude a Global Navigation Satellite System (GNSS) receiverand an inertial measurement unit (IMU). The data acquisition

1545-598X © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

LIANG et al.: MLS SYSTEM FOR MAPPING LARGE FOREST PLOTS 1505

sensors vary depending on the type of data needed and thetechnique available. Laser scanners started to be incorporatedinto systems in the beginning of the 21st century and evolvedinto mobile laser scanning system (MLS).

Studies on tree-related topics using MLS have mainly beenmade in urban environments [19]–[21] or from the robotics per-spective [22], [23]. Cities typically have good road networks,and the platform is easy to move. In contrast, forest envi-ronments are characterized by rugged terrain and tremendousobstacles, which include living trees, dead wood on the ground,and shrubs and saplings. From the robotics perspective, the per-ception of surrounding environment in forest is an interestingtopic. The research emphasizes the detection of object locationsand the optimization of moving path. The retrieval of treeattributes, such as stem curve and volume, and the measurementaccuracy of those attributes are not the focuses, which are themain research topics in forest inventories utilizing MLS.

This letter reports a first test of the utilization of MLSmeasurements in plot-level forest field inventory. An MLSwas developed using a six-wheeled all-terrain vehicle (ATV)for forest inventories. The data obtained from the MLS wereautomatically processed using a robust mapping method. Theresults were validated by utilizing manual measurements fromthe same point cloud and compared to the mapping resultsreported in previous references utilizing single-scan TLS data.

II. STUDY AREA AND DATA ACQUISITION

A. Study Area and Field Data Measurement

The study area is located in Evo, Hämeenlinna, Finland(61◦13′N, 25◦6′E). The main tree species growing in the testarea is Scots pine (Pinus sylvestris L.). A few Norway spruce(Picea abies L.) and Silver and Downy birch (Betula sp. L.)are also present. The understory is mostly sparse. Many youngconiferous and deciduous trees are growing in the test field,including young spruce trees approximately 10–15 m in height.The terrain is mostly flat. A small road goes through the studyarea. Rocky ground surfaces under the moss were present. Thestudy area is about 0.4 ha in size (74 × 50 m). Eighty treeswere with DBH over 5 cm. The tree density is approximately216 stems/ha, where the road area is included in the study area.

The MLS system was built with the FGI ROAMER laser scan-ning system and an ATV, as shown in Fig. 1. The laser scanningsystem utilized a FARO Photon 120 terrestrial laser scanner.The maximum range is about 153 m. The position informationof the vehicle was given by the NovAtel SPAN system, whichincluded a NovAtel DL-4 plus Global Positioning System(GPS) receiver, a NovAtel GPS-702 antenna, and a HoneywellHG1700 AG58 IMU. The raw laser data were in a scannercoordinate system. These raw observations were synchronizedwith the vehicle positions and transformed into real world three-dimensional (3-D) coordinates. The synchronization was madeby TLS data and signals recorded by the GPS receiver. In eachscanning profile, rotation of the scanner beam deflection mirrorwas logged with a time stamp. For more details, see [24].

In the experiment, the track of MLS measurement was ap-proximately 74 m and the MLS moved at a walking speed.The laser scanner measured 49 profiles per second. The pointresolution in each profile was 0.036◦ (0.6 mrad), which gave

Fig. 1. Mobile laser scanning system.

about 7 mm laser beam size at 25 m from the scanner (beamdivergence is 0.16 mrad and the beam size at exit is 3 mm) and15 mm point separation.

B. Reference Data Measurement

In conventional field reference data collection, locations ofthe trees in circular plots are calculated using plot center,angle, and distance data, which are measured using, e.g., aGPS receiver, a compass, and a rangefinder, respectively. TheDBH is measured using, e.g., calipers. The utilization of thesemeasurement methods in the data collection of large forest plotsis time consuming and labor intensive. In this pilot study, stemlocations and diameters were manually measured from the laserpoint cloud.

The measurement was carried out utilizing the TerraScan soft-ware. The ground surface was triangulated first. A slice at thebreast height, between 1.28 m and 1.32 m above ground, wasselected. In this slice, tree stems typically have an arc shape. Acircle was matched to the stem points by selecting three pointson the trunk edge. The diameter of the circle was used as theDBH estimation, and the circle center was used as the positionestimation. If the exact diameter at 1.3 m was not measurablebecause of occlusion effects, the diameter at the closestpossible height was measured. It is worth to note that there alsoexists uncertainty in manual TLS measurements [20], [25].

III. METHODS

A. Preprocessing

The MLS point clouds were georeferenced utilizing the Way-point Inertial Explorer GPS-IMU postprocessing software andcalibration data. The GPS reference station data were acquiredfrom the Finnish virtual reference station network (GPSNet.fi).

After georeferencing, noise emanating from the phase shiftranging was reduced using TerraScan software. Noise in thepoint cloud includes dark returns that were typically less ac-curate measurements and air points from erroneously solvedphase (error in the carrier phase ambiguity). The intensity rangeof the FARO scanner was 0–2047. The minimum intensity valuewas set to 1200, which was experimentally selected to eliminatethe measurement noise. The isolated points were then detectedin two steps: first, points that had less than 200 points within a1 m radius neighborhood were deleted; second, for the remain-ing points, those with less than 10 points within a 20 cm radiuswere deleted.

1506 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 11, NO. 9, SEPTEMBER 2014

Fig. 2. (a) Laser points captured by the MLS system and (b) the recognizedstem points.

B. Stem Detection and Measurement

The original point cloud captured by the MLS system in-cludes points reflected from various objects in the study area,such as the ground, stem, and crown. To collect detailed treeparameters, tree stem models need to be reconstructed as ac-curately as possible. The recognition and modeling procedureswere automatically made utilizing a robust modeling procedure.

Each laser point was automatically studied in its neighbor-hood to identify possible stem points. A local coordinate systemwas established in a point’s neighborhood. The size of theneighboring space was defined by k-nearest points and k was100 in this study. In this local coordinate system, eigenvectorsgave the axis directions and eigenvalues indicated the variancesof the points along the axes. A point was most likely reflectedfrom a tree stem if it was on a vertical planar structure. Theplanar structure was depicted by the distribution of neighbor-ing points, which were mainly along two axes in the localcoordinate system. The vertical distribution was characterizedby the normal vector to the surface. The vector direction wasapproximately horizontal in the real world coordinate system.Fig. 2(a) shows the laser points captured by the MLS systemand Fig. 2(b) displays the recognized stem points.

Tree stem models were built from selected stem points. Tofaithfully model a stem that may have changing growth direc-tions, a series of 3-D cylinders were utilized to describe stem sec-tions. In each section, the laser points were weighted to reduce theinfluence of non-stem objects, such as branches. For more detailson the robust modeling procedure, reader is referred to [18].

The DBH and location of the stem were then estimated fromthe cylinder element at the breast height, which is 1.3 m aboveground level.

C. Evaluation Criteria

The mapping results were evaluated using measured referen-ces. The mapping accuracy was evaluated on the basis of omis-

TABLE ISTEM MAPPING ACCURACY USING THE MLS SYSTEM

sion errors, commission errors, and overall accuracy. Omissionerrors are objects that are not mapped. Commission errors are ob-jects that are mapped and do not have corresponding referencedata. Overall accuracy is the percentage of correct detections.

The accuracies of the DBH and position estimations wereevaluated using the bias, root mean squared error (RMSE), andrelative bias and RMSE, as defined in (1)–(4)

Bias =1

n

n∑i=1

ei =1

n

n∑i=1

(yi − yri) (1)

RMSE =

√∑(yi − yri)2

n(2)

Bias% =Bias

yr× 100% (3)

RMSE% =RMSE

yr× 100% (4)

where yi is the ith estimation, yri is the ith reference, yr is themean of the reference values, andn is the number of estimations.

IV. RESULTS

The results of the stem mapping using the MLS system are re-ported in Table I. The overall stem-mapping accuracy was 87.5%.

The omission errors were ten reference trees that were notmapped, which included three small trees standing in groupsand seven trees whose stems were barely recorded in the pointcloud. Tree group was difficult to model when the methodis for modeling an individual stem. Among the seven missedreferences, one was a young spruce with many branches. Thetree crown was presented in the MLS data, but the stem washardly recorded. Six other missed references were scannedby only a few scan lines. This is most likely because of theplatform movement effects, such as fast movement and a sharpturn. Their locations were mostly far away from the ATV track,which magnified the platform movement effects.

The commission errors were detected trees without corre-sponding reference data. Two more trees were detected in thetest and they were correctly mapped. One of them was a smallspruce with a DBH of approximately 6 cm, which may beconsidered a small tree with a DBH smaller than 5 cm in the ref-erence measurement. Another commission error was a tree nearthe border of the study area, which may be considered a tree outof the study area in the reference measurement. More discus-sions about omission and commission errors are in Section V.

The evaluation of the DBH and position estimations wasbased on mapped reference trees, which had measurementsfrom reference collection and MLS data. Two mapped treeswere excluded because the visual check showed that the ref-erence DBH values did not correspond to the real values, whichwere too large. This is likely because the circle fitting tends togive a larger estimated circle when observations cover a smallproportion of the circle from one direction. Altogether, 68 treeswere utilized in the accuracy evaluation.

LIANG et al.: MLS SYSTEM FOR MAPPING LARGE FOREST PLOTS 1507

TABLE IIACCURACIES OF THE DBH ESTIMATION

UTILIZING THE MLS SYSTEM

TABLE IIISTATISTICS OF THE DISTANCES BETWEEN STEMS

AND THE PLATFORM MOVEMENT TRACK

The accuracies of the DBH and the position estimationsare reported in Table II. The bias and RMSE of the DBHestimations were −0.52 cm and 2.36 cm, respectively. The biasand RMSE of the position estimations were 0.24 m and 0.28 m,respectively.

V. DISCUSSION

The point cloud captured utilizing the MLS system is similarto static single-scan TLS data in the sense that the point datarecord only the part of the tree that is oriented toward thelaser instrument. The accuracy of measurements is dependenton the amount of ground vegetation, density of trees, anddevelopment class. The MLS data also have some unique datacharacteristics. The mapping results show both similar anddifferent characteristics compared with results from the single-scan TLS data.

The omission errors, or missed references, of the tree map-ping are composed of two typical cases: stems standing ingroups and stems poorly recorded in the point cloud. In thisexperiment, sets of three and seven reference trees were notmapped because of these two effects, respectively. The percent-age of these two omission errors was 3.8% and 8.8% of allthe reference. These are close to those reported utilizing single-scan TLS in [18], where the omission errors caused by complexforest scenes, e.g., tree-group effect, were approximately 5%and the omission errors were approximately 12% caused byreduced laser point data coverage of tree stems.

Table III shows the statistics of the distances between stemsand the platform track for all detected trees and seven omissionerrors that were not mapped because of poor representationsin the point cloud. The mapped trees included trees both nearand far from the mapping system. The mean distance value was9.90 m. The omission errors caused by the poor representationswere mainly those far from the scanner. The mean distancevalue was 17.35 m. These statistics indicate that trees far fromthe platform track are more likely to be influenced by theocclusion effects and platform movement effects.

In plot-level forest inventories utilizing single-scan TLS,omission errors caused by occlusion effects are very common.Some trees are not recorded in point cloud data because they aretotally occluded by other trees closer to the scanning position.In this experiment, this type of omission errors was not present.Omission errors caused by occlusion effects are related to the

reference measurements which were in this experiment madefrom point cloud data and therefore such omission error doesnot exist. However, it can be anticipated that there would belimited omission errors of this type if all reference trees hadbeen measured. In MLS data, one object may be scanned fromseveral or many positions because of the platform movement.This multiview measurement principle greatly improves thepossibility of recording all objects in the study area and reducesthe occlusion effect compared with single-scan static TLS datawhere the observing position is fixed.

The commission errors, or mapped trees without correspond-ing references, in this experiment include a small tree anda border tree. Similar cases were also reported in plot-levelforest inventories utilizing single-scan TLS. For example, in[18], small trees approximately 5 cm in DBH introduced somecommission errors because they were not measured in the fieldmeasurement; in [11], a commission error was on the border ofa plot which was not in the reference data.

The RMSE of the DBH estimation was 2.36 cm, which isclose to those obtained in plot-level forest inventories utilizingthe single-scan TLS measurement. In [15], the RMSE of theDBH estimation was 1.80–3.25 cm in three plots. In [16], theRMSE of the DBH estimation was 3.4–7.0 cm in one plot,where three estimating methods were evaluated. In [1], theRMSE of the DBH estimation was 0.74–2.71 cm in five plots.

The accuracy of the DBH estimation from the MLS datashows that the MLS system has the potential to measure largerforest plots accurately. The accuracy of the DBH and posi-tion estimation was evaluated using measurements from pointcloud data, which include positioning errors introduced bypositioning signals. The estimation accuracy should thereforebe understood as the accuracy of the stem mapping method.The magnitude of positioning errors is currently not yet knownbut should be investigated by using field measured referencesin the future.

In this letter, a recognition method was utilized to detectand model tree from MLS data. Tree stems were identified andmodeled utilizing geometric features. In MLS data processing,the classification method is very popular [19], [23]. This typeof methods first establishes the classifier from hand-labeledtraining data set and then classifies the point data. Both methodshave been shown to be efficient. However, they were developedfor boreal forests. Certain modifications have to be made inorder to apply them to other type of forests.

In MLS, the data collection can be performed in stop-and-gomode or in continuous mode. In the stop-and-go mode, the ve-hicle stops when the forest plot is measured which correspondto conventional static TLS measurement. In the continuousmode, the data collection is carried out when the vehicle ismoving along the driving track. The results in this letter indicatethat basic tree parameters, such as position and DBH, can beestimated rather accurately utilizing automated data processingin the continuous mode. Previous research has shown that bothbasic (e.g., DBH) and advanced tree features (e.g., stem curve,volume, and biomass) can be automatically retrieved fromstatic TLS data [4], [9], [10]. In operational forest plot-levelinventory, the stop-and-go mode could be utilized probablybefore the continue method could.

In the past, TLS has been shown to have the accuracy neededfor plot-level forest inventories. It has been shown to be faster

1508 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 11, NO. 9, SEPTEMBER 2014

than conventional plot-level methods. The lack of processingsoftware and TLS processing knowledge by foresters currentlyhampers the use of TLS in practical forestry. MLS is a tech-nique that could make laser-scanning-based plot-level estimatesmore competitive. For example, MLS can map larger forestsample plots in a short period of time, and ATVs can be usedto more easily move systems to mapping locations without theneed to carry heavy TLS equipment. In this test, MLS collecteddata on a 0.4 ha study plot in about 1.5 min. In principle, MLScould cover a 4 ha corridor area in 15 min. One full-field-of-view static single-scan TLS takes approximately 1.5 minwhen the same scanning configuration is utilized. To cover a4 ha study plot, a lot of scanning positions are, however,required, e.g., 10–20 or more. The scanning campaign may takea whole day or even longer depending on forest conditions andthe amount of reference targets employed. In general, the MLScan collect a larger sample plot area much faster than what staticTLS can.

The results in this study indicated that the MLS is a veryattractive technology for forest inventories. Further researchneeds to test the MLS in even larger areas and in different forestconditions. The point clouds collected by MLS are sparserbut more homogenous in terms of point density comparedwith those obtained with TLS. The platform positioning is,however, one main problem with MLS measurement, which ischallenging under forest canopy. It can be expected that MLS isslightly less accurate than TLS. The accuracy of tree attributesretrieval necessitates further studies. Currently, MLS is at a veryearly stage of use for forest inventories, but it may be a practicalway to collect future plot-level forest inventory data.

VI. CONCLUSION

In this letter, an MLS system was tested for mapping largerforest plots. The mapping results show that the overall mappingaccuracy was 87.5%. The omission errors were trees standingin groups and trees poorly recorded in the point cloud. Thecommission errors were correctly mapped trees that were smallor border trees that were not included in references. The RMSEof the DBH and the location estimations were 2.36 cm and0.28 m, respectively. These results indicate that the MLS systemhas the potential to accurately map forest characteristics forlarger forest plots and areas.

Further studies should evaluate the mapping accuracy uti-lizing field measured references, investigate the magnitude ofpositioning errors, and test the application of MLS in evenlarger areas. Further research should also be carried out indifferent forest conditions, such as varying amount of groundvegetation, different development classes, different species, andunevenly aged forests.

REFERENCES

[1] X. Liang and J. Hyyppä, “Automatic stem mapping by merging severalterrestrial laser scans at the feature and decision levels,” Sensors, vol. 13,no. 2, pp. 1614–1634, Jan. 2013.

[2] M. Erikson and V. Karin, “Finding tree-stems in laser range images ofyoung mixed stands to perform selective cleaning,” in Proc. ScandLaserSci. Workshop Airborne Laser Scanning Forest, 2003, pp. 244–250.

[3] M. Simonse, T. Aschoff, H. Spiecker, and M. Thies, “Automatic determi-nation of forest inventory parameters using terrestrial laserscanning,” inProc. ScandLaser Sci. Workshop Airborne Laser Scanning Forests, 2003,vol. 2003, pp. 252–258.

[4] P. J. Watt and D. N. M. Donoghue, “Measuring forest structure withterrestrial laser scanning,” Int. J. Remote Sens., vol. 26, no. 7, pp. 1437–1446, 2005.

[5] K. Tansey, N. Selmes, A. Anstee, N. J. Tate, and A. Denniss, “Estimatingtree and stand variables in a Corsican Pine woodland from terrestrial laserscanner data,” Int. J. Remote Sens., vol. 30, no. 19, pp. 5195–5209, 2009.

[6] H. Huang, Z. LI, P. Gong, X. Cheng, N. Clinton, C. Cao, W. Ni, andL. Wang, “Automated methods for measuring DBH and tree heights witha commercial scanning lidar,” Photogramm. Eng. Remote Sens., vol. 77,no. 3, pp. 219–227, Mar. 2011.

[7] X. Liang, P. Litkey, J. Hyyppä, H. Kaartinen, A. Kukko, andM. Holopainen, “Automatic plot-wise tree location mapping using single-scan terrestrial laser scanning,” Photogramm. J. Finland, vol. 22, no. 19,pp. 37–48, 2011.

[8] J. L. Lovell, D. L. B. Jupp, G. J. Newnham, and D. S. Culvenor, “Mea-suring tree stem diameters using intensity profiles from ground-basedscanning lidar from a fixed viewpoint,” ISPRS J. Photogramm. RemoteSens., vol. 66, no. 1, pp. 46–55, Jan. 2011.

[9] X. Liang, V. Kankare, X. Yu, J. Hyyppa, and M. Holopainen, “Automatedstem curve measurement using terrestrial laser scanning,” IEEE Trans.Geosci. Remote Sens., vol. 52, no. 3, pp. 1739–1748, Mar. 2014.

[10] X. Yu, X. Liang, J. Hyyppä, V. Kankare, M. Vastaranta, andM. Holopainen, “Stem biomass estimation based on stem reconstructionfrom terrestrial laser scanning point clouds,” Remote Sens. Lett., vol. 4,no. 4, pp. 344–353, 2013.

[11] X. Liang, J. Hyyppä, H. Kaartinen, M. Holopainen, and T. Melkas, “De-tecting changes in forest structure over time with bi-temporal terrestriallaser scanning data,” ISPRS Int. J. Geo-Inf., vol. 1, no. 3, pp. 242–255,Oct. 2012.

[12] T. Aschoff and H. Spiecker, “Algorithms for the automatic detection oftrees in laser scanner data,” Int. Arch. Photogramm., Remote Sens. SpatialInf. Sci., vol. 36, pp. 71–74, 2004.

[13] N. Haala, R. Reulke, M. Thies, and T. Aschoff, “Combination of terrestriallaser scanning with high resolution panoramic images for investigations inforest applications and tree species recognition,” Int. Arch. Photogramm.,Remote Sens. Spatial Inf. Sci., vol. 34, no. 5/W16, pp. 1–4, 2004.

[14] C. Hopkinson, L. Chasmer, C. Young-Pow, and P. Treitz, “Assessing forestmetrics with a ground-based scanning lidar,” Can. J. Forest Res., vol. 34,no. 3, pp. 573–583, Mar. 2004.

[15] H. G. Maas, A. Bienert, S. Scheller, and E. Keane, “Automatic forestinventory parameter determination from terrestrial laser scanner data,” Int.J. Remote Sens., vol. 29, no. 5, pp. 1579–1593, Mar. 2008.

[16] G. Brolly and G. Kiraly, “Algorithms for stem mapping by means ofterrestrial laser scanning,” Acta Silvatica et Lignaria Hungarica, vol. 5,pp. 119–130, 2009.

[17] G. E. Murphy, M. A. Acuna, and I. Dumbrell, “Tree value and log productyield determination in radiata pine (Pinus radiata) plantations in Australia:Comparisons of terrestrial laser scanning with a forest inventory systemand manual measurements,” Can. J. Forest Res., vol. 40, no. 11, pp. 2223–2233, Nov. 2010.

[18] X. Liang, P. Litkey, J. Hyyppa, H. Kaartinen, M. Vastaranta, andM. Holopainen, “Automatic stem mapping using single-scan terrestriallaser scanning,” IEEE Trans. Geosci. Remote Sens., vol. 50, no. 2,pp. 661–670, Feb. 2012.

[19] M. Lehtomäki, A. Jaakkola, J. Hyyppä, A. Kukko, and H. Kaartinen, “De-tection of vertical pole-like objects in a road environment using vehicle-based laser scanning data,” Remote Sens., vol. 2, no. 3, pp. 641–664,Feb. 2010.

[20] M. Holopainen, M. Vastaranta, V. Kankare, T. Kantola, H. Kaartinen,A. Kukko, M. Vaaja, J. Hyyppä, and H. Hyyppä, “Mobile terrestrial laserscanning in urban tree inventory,” in Proc. SilviLaser, 2011, pp. 1–7.

[21] B. Wu, B. Yu, W. Yue, S. Shu, W. Tan, C. Hu, Y. Huang, J. Wu, andH. Liu, “A voxel-based method for automated identification and morpho-logical parameters estimation of individual street trees from mobile laserscanning data,” Remote Sens., vol. 5, no. 2, pp. 584–611, Jan. 2013.

[22] P. Forsman and A. Halme, “3-D mapping of natural environments withtrees by means of mobile perception,” IEEE Trans. Robot., vol. 21, no. 3,pp. 482–490, Jun. 2005.

[23] J.-F. Lalonde, N. Vandapel, and M. Hebert, “Automatic three-dimensionalpoint cloud processing for forest inventory,” Robot. Inst., Pittsburgh, PA,USA, 2006.

[24] A. Kukko, “Road environment mapper-3D data capturing with mobilemapping,” Licentiate’s thesis, Helsinki Univ. Technol., Espoo, Finland,2009, vol. 17.

[25] V. Kankare, M. Holopainen, M. Vastaranta, E. Puttonen, X. Yu, J. Hyyppä,M. Vaaja, H. Hyyppä, and P. Alho, “Individual tree biomass estimationusing terrestrial laser scanning,” ISPRS J. Photogramm. Remote Sens.,vol. 75, pp. 64–75, Jan. 2013.