ii F o r e s tF o r e s tBiogeosciences and ForestryBiogeosciences and Forestry

Integration between TLS and UAV photogrammetry techniques for forestry applications

Irene Aicardi, Paolo Dabove, Andrea Maria Lingua, Marco Piras

Forests are significant resources from an ecological, economic and social pointof view. Their protection and management could greatly benefit from a com-plete knowledge of the shape and distribution of trees in forest stands. To thispurpose, aerial surveys, especially through Airborne Laser Scanning (ALS),were carried out in the last years to acquire point clouds to be used in 3Dmodels aimed at achieving an accurate description of tree crowns and terrain.However, airborne data acquisition is expensive and may provide poor resultsin case of dense foliage. Further, point cloud resolution is not very high, asmodels with a grid of 2-3 m are usually obtained. In order to implement moreaccurate 3D forest models, a feasible solution is the integration of point cloudsobtained by aerial acquisition (ALS or photogrammetry) for the treetops andthe terrain description, with information from terrestrial surveys. In thispaper, we investigated the possible integration of point clouds obtained byTerrestrial Laser Scanner (TLS) with those collected by photogrammetric 3Dmodels based on images captured by Unmanned Aerial Vehicle (UAV) in a testsite located in northern Italy, with the aim of creating an accurate dataset ofthe forest site with high resolution and precision. The limits of ALS and TLSwere bridged by aerial photogrammetry at low altitude (and vice versa). A 3Dmodel of the study area was obtained with a resolution of 5 cm and a precisionof 3 cm. Such model may be used in a wide range of applications in forestrystudies, e.g., the reconstruction of 3D shapes of trees or the analysis of treegrowth throught time. The implications of the use of such integrate approachas a support tool for decision-making in forest management are discussed.

Keywords: Data Integration, Forestry, Laser Scanner, Photogrammetry,Unmanned Aerial Vehicle, GNSS

IntroductionA key issue in sustainable forest manage-

ment is to improve access to mountain for-est (Sylvain et al. 2015) to increase the effi-ciency of wood harvesting and transport(Spinelli et al. 2007, Savoie et al. 2006), aswell as collect forest inventory data forbiomass analysis and soil model (Fattoriniet al. 2006, Tabacchi et al. 2005). In thisscenario, the EU Gothenburg priorities(http://www.interreg4c.eu/projects/environment-risk-prevention/) focus on the effec-tive management of forest ecosystems andrisk prevention, as well as the protectionand preservation of forest ecosystems. Theaim of various National and European proj-

ects (CROPS 2010, FIRESMART 2010, NEW-FOR 2011) is to contribute to the future sus-tainable development of the environmentand land use. To this end, the developmentof practical tools for managing forest re-sources is needed (Arnold 1993). In particu-lar, comprehensive knowledge of the terri-tory is required, for example by adoptinggeomatics techniques and yielding innova-tive cartographic products.

In the last years, LiDAR (Light DetectionAnd Ranging) techniques have gained sci-entific and operational attention in forestmanagement. The utility of forestry laserscanner has been established over timeboth from terrestrial and aerial perspec-

tives (Pirotti 2011, Maltamo et al. 2014). Themain feature of this method is the acquisi-tion of data both from above and underthe trees, thanks to the signal used and thewaveform analysis. From the aerial per-spective, Airborne Laser Scanning (ALS)provided interesting results in terms ofbiomass prediction (Nyström et al. 2012)and estimation (Gobakken et al. 2012).Moreover, many advantages of the use ofALS have been highlighted in the inventoryprocess aimed at the sustainable forestmanagement, both from aerial (Wulder etal. 2008, Hyyppä et al. 2012) and terrestrialperspectives (Bienert et al. 2006).

Recent progress in LiDAR technology,combined with aerial photogrammetry(Holopainen et al. 2015), enabled forest vol-ume and other stand characteristics to bemapped in the field. However, several pro-blems still exist with the use of this tech-nology. Indeed, the main problem is thatLiDAR data have a low (metrical) resolu-tion, thereby many trees characteristics aresometimes missing due to foliage coverage(Baltsavias 1999). Additionally, ALS acquisi-tion is very expensive, thus it cannot be fre-quently repeated. As for the aerial perspec-tive, photogrammetry can be a solution forvolume and tree height estimation (Ura-moto et al. 2012), especially to generate

© SISEF http://www.sisef.it/iforest/ e1 iForest (early view): e1-e7

Environment, Land, and Infrastructure Department (DIATI), Politecnico di Torino, c.so Duca degli Abruzzi 24, I-10129 Turin (Italy)

@@ Marco Piras (marco.piras@polito.it)

Received: Jul 27, 2015 - Accepted: Mar 04, 2016

Citation: Aicardi I, Dabove P, Lingua AM, Piras M (2016). Integration between TLS and UAV photogrammetry techniques for forestry applications. iForest (early view). – doi:10.3832/ifor1780-009 [online 2016-06-23]

Communicated by: Davide Travaglini

Technical ReportTechnical Reportdoi: doi: 10.3832/ifor1780-00910.3832/ifor1780-009

(Early View)(Early View)

Aicardi I et al. - iForest (early view)

terrain models and orthophotos in moni-toring scenarios (Millera et al. 2000).

During the last years, photogrammetryfrom UAVs (Unmanned Aerial Vehicles) hasreceived increasing attention (Grenzdörf-fer et al. 2008) due to the use of SFM(Structure From Motion) approaches fortree 3D modeling. This approach permits towork on images acquired by UAV with dif-ferent lightness, scale factors and a verylarge overlapping. This kind of data couldbe exploited to reconstruct the surfacemodel of the study area and producehighly detailed orthophotos. However,with aerial photogrammetry there is a lackof ground data which is very important fortree trunk analyses, thus requiring timeconsuming surveys carried out by forestryexperts. For this reason, an integratedapproach between terrestrial laser scannerand aerial photogrammetry from UAVwould be highly desirable, with the aim ofevaluating data quality in terms of acquisi-tion, processing and final results, andtherefore achieving a complete descriptionof the area of interest.

This study investigates the combined useof Terrestrial Laser Scanner (TLS) for treetrunks modeling and UAV for surfacereconstruction. This method allows a com-plete description of the study area withoutthe need to perform specific ground surveyto analyze tree characteristics. In this way,monitoring analysis can be performedstarting from the complete description ofthe forest with a high level of detail andprecision. The final results is a completepoint clouds, the orthophoto of the areaand a Digital Surface Model (DSM). Webelieve that the georeferenced pointclouds should help to support decisionmaking.

The work presented here is part of theNEWFOR research project (NEW technolo-

gies for a better mountain FORest timbermobilization – http://www.newfor.net/, Al-pine Space 2012) bringing together 14 insti-tutions from six Alpine countries. Its aim isto encourage the use of new technologiesto support the planning of forest exploita-tion, focusing on the entire wood chain,through the use of remote sensing andgeographic information systems (GISs).

Material and methods

Case studyThe study was carried out in Beaulard,

Piedmont (northwest Italy – 45° 46′ 22.05″N, 7° 29′ 49.05″ E, elevation: 1745 m a.s.l), ina riverside area characterized by trees bothstanding alone and arranged in rows. Weused both TLS and photogrammetric acqui-sitions with UAV. TLS was used in an areawhere trees form a “natural” tunnel, whileUAV acquisition covered the entire area.

Measurement campaign

Geodetic network and surveyIn order to identify the coordinates of

Ground Control Points (GCPs) which wereused to georeference the orthophoto andDSM, a geodetic network (Fig. 1) was builtand measured using both GNSS and tradi-tional topographic (total station) surveys.

The GNSS campaign was conducted usingdouble frequency and multi-constellationreceivers and considering a post-process-ing approach (Dabove et al. 2014), due tothe lack of internet connection.

A master station was located for a ses-sion length of more than 4 hours on one ofthe vertices, whose coordinates were de-termined in post-processing by consideringa single-base solution (Leica Geo Officev.8.3, Leica Geosystems®) with the perma-nent station of the Regione Piemonte

CORSs (Continuous Operating ReferenceStations) network as reference. A networkadjustment was then carried out to esti-mate the other markers, which were val-ued with a sub-millimeter level of accuracy(σM = 3 mm) and the phase ambiguity wasfixed for all points, which guaranteed ahigh precision for the georeferencing proc-ess. All other markers were measured,wherever possible, by GNSS instrumentsusing Real Time Kinematic (RTK) approach.The coordinates were estimated using acommercial software in post-processing,considering a session length of about 10sec for each point. In some cases, wherevegetation cover was extremely high, tar-get points were measured by traditionaltopographic instruments. The remainingmarkers were measured using a reflector.All measurements (Fig. 1) were subse-quently adjusted (following the minimumconstraint approach) with MicroSurveyStarNet v.7.0, to obtain the final coordi-nates. The root mean square (RMS) of theestimated coordinates was less than 1 cm.

The ellipsoidal heights measured with theGNSS instruments were converted intoorthometric data using a local geoid heightmodel (ITALGEO2005) provided by the Ital-ian Military Geographic Institute.

Marker points were then used to georef-erence the 3D model, which was obtainedby combining the laser scanner and thephotogrammetric data.

Aerial image acquisition for photogrammetry

In order to perform the aerial acquisitionusing UAV, two issues must be considered(Eisenbeiss 2009): (i) choice of the system;(ii) acquisition and data processing strat-egy. As for the first point, two main cate-gories can be chosen: fixed wings andmulti-rotors. These systems have differentperformances in terms of payload, flighttime and stability in data acquisition. Forphotogrammetric purposes, fixed wingsare preferable in wide areas (at least 1.5 kmradius), while multi-rotors are best forsmaller areas (e.g., 400 × 400 m) or wherevertical flight or hovering are required.

The acquisition and data processing strat-egy can be divided into the following steps:• Mission planning: definition of the param-

eters that the UAV must know in order toperform the flight. These concern thestudy area and the geometry of the acqui-sition to define the path to follow and therelative flight altitude. These parametersrely on the expected final Ground SampleDistance (GSD) of the 3D model and thespecific camera used. In the case of theused UAV, the mission planning softwareis unable to automatically estimate theflight parameters as a function of theexpected final results, thus their estima-tion has to be done in advance.

• UAV flight and data acquisition: the flightis the operative step in the data acquisi-tion. The UAV is able to perform anautonomous flight and record digital

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Fig. 1 - Position of the network (red triangle) and detail points (blue circles) used inthe geodetic survey of the study area (Beaulard, Piedmont, northern Italy).

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images following the waypoints previ-ously defined in the flight plan. Duringthe flight, some data, such as positionand attitude of the system, were alsorecorded through the internal navigationsensors. However, this data is not accu-rate enough to perform a correct georef-erencing of the model which is why GCPsare needed;

• Reference network: to use a global coordi-nate system, some vertices can be mea-sured through a topographic or GNSS sur-vey. These points are the local referencesfor the subsequent GCP measurements.

• Ground control points: used to georefer-ence the 3D model from the image block.These points must be located on theground in positions that are clearly visiblefrom the UAV in order to be included inthe images.

• Model generation: consists on the imagealignment to generate the 3D pointclouds (Remondino et al. 2014). Currently,it is possible to perform the triangulationthrough a photogrammetric or a SFM ap-proach. From the 3D dense point clouds itis then possible to reconstruct the tex-tured mesh.

• Final product extraction: typically, DenseDigital Surface Models (DDSMs) andorthophotos can be generated (Rijsdijk2014). They can be very useful for a com-plete geometrically correct description ofthe area in 3D, combined also with theradiometric information.

• Data integration (Mancera-Taboada et al.2010): DDSMs and orthophotos can befinally integrated to generate the SolidOrthophoto, which gives a complete des-cription of the study area.To perform the aerial acquisition steps, a

low-cost Hexakopter® (Mikrokopter, Moor-merland, Germany) was used (Fig. 2), withspecific modifications. This system is highlysuitable for these applications because itenables detailed area and integrate satel-lite mapping to be acquired for forestrystudy and management (Corona 2010). Thesystem performs the flight autonomously,though its take-off and landing require theremote control by an expert pilot.

Several types of sensors can be installedon the UAV, depending on the specificapplication and the maximum payload ofthe system. The Hexakopter used in thistest has a payload of up to 1.2 kg and a 10-15 minutes of flight autonomy. It wasequipped with a Nex5® digital camera(Sony Ltd, Tokyo, Japan), with a CCD sen-sor of 16 Mpixel (4000 x 3000), a focallength of 16 mm and a sensor size of 23.0 ×15.6 mm. The camera automatically ac-quires the images with a fixed number offrames per second using a shutter cable. Inorder to preserve the initial position of thecamera, it is mounted on a special servo-support that controls the rotations in twodirections.

For the flight planning, several compo-nents are critical: (i) the geographic areawhere the flight is performed, the mag-

netic field of the site must be estimatedand a suitable azimuth correction in theflight planning must be set (in our case itwas calculated through the website http://www.ngdc.noaa.gov/geomag-web); (ii) theflight height; (iii) the digital camera sensorspecifications; (iv) the time of flight, whichis a function of the speed and the numberof lines required. Mikrokopter Tools can beused to organize the mission. The follow-ing options can be set: the 2D position ofthe waypoints in geographical coordinates;the flight height; the speed along eachpath; and the compass direction requiredduring the flight. In the specific case of ourstudy area, the flight was performed at arelative altitude of 50 m over seven parallelstrips running in East-West direction andtwo boarding strips (Fig. 3). Flight datawere uploaded onto the UAV using a wire-less connection by a XBee module, whichenables information on the position, orien-tation, height and speed during the flightto be received and sent.

The last step before acquisition involvedthe deployment of 31 wooden markers onthe ground for the point clouds georefer-encing, whose position can be acquiredthrough a GNSS and/or topographic tech-nique. Most points were surveyed througha GNSS receiver using a Real Time Kine-matic (RTK) approach (Dabove & Manzino2014).

Terrestrial Laser ScannerIn forestry applications, the acquisition of

information on tree trunks through aerialphotogrammetry is not always possibledue to foliage coverage. This problem canbe overcome by using a terrestrial laserscanner. In this study, a Faro Focus 3D®

laser scanner (CAM2, Lake Mary, FL, USA)was used, which has an acquisition rangefrom 0.6 m to 130 m, and which is also ableto acquire images through an integratedcamera to color the point clouds.

Eight scans were performed to cover atest array of trees using the following set-tings: resolution 1/5 (i.e., 1 point every 7

mm at 5 m), acquisition speed of 244,000points per second (each scan had about17,500,000 points). Each acquisition re-quired about four minutes for the acquisi-tion of clouds and one minute for the col-lection of images.

The final 3D model can be obtained byintegrating the aerial and terrestrial pointclouds, but only if the clouds are defined ina unique reference system. Thus, 61checkerboard markers were placed on thetrees, which are automatically recognizedby SCENE (software for data processingproduced by FARO). The coordinates ofeach marker were determined using a totalstation.

Data processing

The laser scanner acquisition processThe laser data were processed using

FARO’s Scene version 5.2. The processinvolved the following steps:• scan integration and visualization (Fig. 4),

the scans can be visualized in the projectspace;

• scan georeferencing: the software has aninternal database that automaticallydetects the checkerboards in the cloud,although the targets can be selectedmanually. Given that the markers weretopographically acquired, their coordi-nates were inserted into the software inthe chosen reference system. The wholemodel was then automatically referencedand all the scans were related to eachother;

• data filtering and coloring: sometimesunnecessary data (like remote objects orpeople) can be accidentally recorded;however they can be manually selectedand deleted to reduce the point cloudand to facilitate data processing. Ourpoint cloud was manually filtered to takeonly the points in the chosen area ofinterest. Finally, the images can be ap-plied to the points to get a colored pointcloud.The results obtained by merging the col-

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Fig. 2 - UAV used for aerialphotogrammetry (Hex-akopter®, Mikrokopter, Moormerland, Germany).

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ored point clouds are shown in Fig. 5. Thewhole process (PC: Windows 8, Intel Corei7, 8 GB RAM) took about 2-3 hours, andthe outcome is a colored 3D point cloudcomposed of 77 million points.

Processing the aerial photogrammetric data

Regarding the photogrammetric data

analysis, software programs use dissimilarapproaches, with both commercial andopen source solutions (Remondino et al.2014). As for commercial solutions, userscan only set a limited number of processingparameters, whereas open source soft-wares allow the users to control the wholeprocess, and analyze both the results ob-tained and the algorithms used. In this

study, photogrammetric data were pro-cessed using the software PhotoScan®

(Agisoft LLC, St. Petersburg, Russia) forimage triangulation to obtain a georefer-enced 3D point cloud of the area. Data pro-cessing involved the following steps: • Image alignment: the algorithm searches

for common points on the images andmatches them by finding the position of

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Fig. 3 - Flight planning of the study area, with indica-tion of the flight strips fol-lowed by the UAV.

Fig. 4 - Example of a laser scanner colored point clouds.

Fig. 5 - Result of the regis-tration of the 8-point clouds.

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the camera for each picture and estimat-ing the camera calibration parameters.The software then computes the camerapositions and generates the first sparsepoint cloud.

• Dense cloud: this is based on the esti-mated camera positions and the sparsecloud built by PhotoScan. In the case ofpresence of many trees (as in this study),the best solution was to perform a lowaccuracy alignment and then improve thequality through high resolution densepoint cloud generation.

• Mesh: the software can reconstruct a 3Dpolygonal mesh representing the objectsurface based on the dense point cloud.Generally there are two algorithmic meth-ods that can be applied to 3D mesh gen-eration: height field (for planar surfaces)and arbitrary (for any kind of object). In

this study the height field method wasused.

• Texture: the reconstructed mesh was tex-tured and used to generate the ortho-photo.The coordinates of GCPs were used to

adjust the alignment and to georeferencethe model into the correct reference sys-tem.

The dense point cloud was made up of17.2 million points and the model was madeup of 3.4 million faces and 1.7 million ver-tices. The whole processing took about 5-6hours.

Photogrammetric triangulation can beused to generate an orthophoto and aDSM. These products were generated witha 5 cm resolution, as shown in Fig. 6.

Results and discussionAt the end of the process two dense

point clouds were generated: the first wasobtained by the laser scanner and the sec-ond by using the images. The two datasetswere integrated by creating a unique pointcloud, orthophoto and DDSM (Lisein et al.2013). Data merging was facilitated by thefact that the two clouds were in the samereference system, obtaining fully consis-tent results in terms of accuracy. The inte-gration was performed by importing the3D colored point clouds in .las format intothe software 3DReshaper® (Technodigit,Genay, France), which merges the data andanalyzes the results. The laser point cloudresulted to have a greater density com-pared to the photogrammetric point cloud;however, this did not hamper the integra-tion of the two datasets (Fig. 7, Fig. 8).

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Fig. 6 - Orthophoto andDSM generated by the

photogrammetric tech-nique.

Fig. 7 - Point clouds gener-ated by the photogram-

metric approach (left) andby TLS techniques (right).

Fig. 8 - Results of the pointcloud integration process.

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A single 3D model of the study area wasobtained with a resolution of 5 cm and aprecision equal to 3 cm. The precision wasestimated in comparison with the coordi-nates of the points topographically mea-sured. Such model may be used in a widerange of application in forestry studies,from the reconstruction of 3D shapes oftrees, to the study of tree growth, to otherenvironmental analysis.

A critical aspect in the process of integra-tion of TLS and UAV data could be the dif-ferent accuracy of the points in the clouds,which depends on the methodology used.While photogrammetric analysis allows toestimate the accuracy of points (2 cm inour case), the accuracy of TLS data is moredifficult to be obtained. In this study, theredundancy of the data, the limited dis-tance between TLS and objects, and a net-work of markers with a high accuracyallowed to estimate a final precision of 3cm.

ConclusionsForest resource management can greatly

benefit from the application of geomatictechniques through both terrestrial andaerial surveys and adequate resource map-ping. Such techniques can be considered auseful tool for decision support and moun-tain forest management.

In this study, TLS and UAV photogramme-try techniques were investigated and inte-grated by combining the use of terrestriallaser scanner for tree modeling with UAVdata for surface reconstruction. This inte-grated approach can easily and quickly pro-vide all the necessary information on singletrees and the shape of the forest, with noneeds of time-consuming and expensiveground surveys. Indeed, the accuracy ofthe 3D model obtained in this study allowsto obtain precise measurements of themain tree parameters (e.g., tree diameterand height). Further, the digital model ofthe forest obtained in this study will allowto monitor the investigated stands throughtime in a cost-effective way. In fact, thedense point cloud obtained allowed togenerate also orthophotos and a DDSMfrom the photogrammetric triangulation (5cm of resolution).

However, it is worth to stress that theacquisition step is the most crucial in the3D model generation, since the SFM ap-proach has proven to be reliable understrictly specific constraints. In fact, theapplication of the above methodology toforestry requires to correctly plan the flight(for example, by avoiding evaporation thatcan affect the accuracy of point data) andto adopt specific strategies in data process-ing to find common tie points between dif-ferent images.

Laser scanner and photogrammetry datamay be analyzed using semi-automatedprocedure. However, model georeferenc-ing still requires the interactive selection ofpoints, which the most time-consumingstep and subjected to gross errors by the

operator. To overcome possible sources ofhuman error in data processing, direct geo-referencing of images can be tested in thefuture, by applying external sensors duringthe flight recording the image positionwith high accuracy.

The availability of a digital model of theforest allows the development of automa-tic techniques for the estimation of themain stand parameters such as the stand-ing volume, trees dimensions and charac-teristics, tree growth, etc.

Future developments of the approachpresented here involve the inclusion of thethird spatial information in the models withthe aim of generating solid orthophotos ofthe stands analyzed (Bornaz & Dequal2003, Bornaz et al. 2006). This can beachieved by adding to the DDSM (DenseDigital Surface Model) another matrix withthe same dimension and resolution to animage matrix (RGB) including the elevationdata. Such orthophotos might allow vari-ous measurements (such as, 3D position ofpoints, distances, angles and also volumes)to be obtained through a dedicated soft-ware called STOP (Solid True OrthoPhoto -Forno et al. 2013).

List of abbreviationsThe following abbreviations were used

throughout the paper:• CCD: Charge-Coupled Device• CORS: Continuous Operating Reference

Station• DDSM: Dense Digital Surface Model• DSM: Digital Surface Model• GNSS: Global Navigation Satellite System• IGM: Istituto Geografico Militare• ITALGEO95: Name of an Italian model of

geoid• LiDAR: Light Detection and Ranging• NEWFOR: NEW technologies for a better

mountain FORest timber mobilization• RMS: root mean square• SFM: Structure From Motion• TLS: Terrestrial Laser Scanner• UAV: Unmanned Aerial Vehicle• OSP: orthophoto solid precision

AcknowledgementsThe authors would like to thank Emanue-

le Lingua for the opportunity to participatein the NEWFOR Project, and Paolo Maschiofor his valuable help in piloting the UAV.

All the authors carried out the field mea-surements. IA carried out TLS measure-ments and the post-processing of thesedata and those obtained from instrumentsinstalled on UAV. IA drafted the manuscriptwith PD, who also carried out GNSS mea-surements in the field and their post-pro-cessing. AL conceived the study and helpedto draft the manuscript. MP carried outUAV measurements and coordinated theacquisition procedures and the processingphase.

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