From TLS to VLS: Biomass Estimation at Individual Tree Level

Lin, Yi; Jaakkola, Anttoni; Hyyppä, Juha; Kaartinen, Harri

doi:10.3390/rs2081864

Cited by 49 publications

(43 citation statements)

References 37 publications

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“…The navigation sensors typically include GPS receivers and an IMU (Inertial Measurement Unit), while the data acquisition sensors include terrestrial laser scanners and digital cameras; thus, the instruments are similar to ALS surveys. Recent studies on MLS systems and their accuracy as well as environmental modeling done with MLS can be found in [20][21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Mapping Topography Changes and Elevation Accuracies Using a Mobile Laser Scanner

et al. 2011

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Laser measurements have been used in a fluvial context since 1984, but the change detection possibilities of mobile laser scanning (MLS) for riverine topography have been lacking. This paper demonstrates the capability of MLS in erosion change mapping on a test site located in a 58 km-long tributary of the River Tenojoki (Tana) in the sub-arctic. We used point bars and river banks as example cases, which were measured with the mobile laser scanner ROAMER mounted on a boat and on a cart. Static terrestrial laser scanner data were used as reference and we exploited a difference elevation model technique for describing erosion and deposition areas. The measurements were based on data acquisitions during the late summer in 2008 and 2009. The coefficient of determination (R 2 ) of 0.93 and a standard deviation of error 3.4 cm were obtained as metrics for change mapping based on MLS. The root mean square error (RMSE) of MLS-based digital elevation models (DEM) for non-vegetated point bars ranged between 2.3 and 7.6 cm after correction of the systematic error. For densely vegetated bank areas, the ground point determination was more difficult resulting in an RMSE between 15.7 and 28.4 cm. OPEN ACCESSRemote Sens. 2011, 3 588

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Section: Introductionmentioning

confidence: 99%

Mapping Topography Changes and Elevation Accuracies Using a Mobile Laser Scanner

et al. 2011

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“…MLS has been used for modeling road surfaces [31], extracting building facades [29,[32][33][34], and detecting trees, streetlights, and other pole-like objects [28,[35][36][37]. Previous studies have demonstrated the great potential of MLS for street objects identification.…”

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confidence: 99%

A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

et al. 2013

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Abstract:As an important component of urban vegetation, street trees play an important role in maintenance of environmental quality, aesthetic beauty of urban landscape, and social service for inhabitants. Acquiring accurate and up-to-date inventory information for street trees is required for urban horticultural planning, and municipal urban forest management. This paper presents a new Voxel-based Marked Neighborhood Searching (VMNS) method for efficiently identifying street trees and deriving their morphological parameters from Mobile Laser Scanning (MLS) point cloud data. The VMNS method consists of six technical components: voxelization, calculating values of voxels, searching and marking neighborhoods, extracting potential trees, deriving morphological parameters, and eliminating pole-like objects other than trees. The method is validated and evaluated through two case studies. The evaluation results show that the completeness and correctness of our method for street tree detection are over 98%. The derived morphological parameters, including tree height, crown diameter, diameter at breast height (DBH), and crown base height (CBH), are in a good agreement with the field OPEN ACCESS Remote Sens. 2013, 5 585 measurements. Our method provides an effective tool for extracting various morphological parameters for individual street trees from MLS point cloud data.

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“…Recent studies on MLS systems and their accuracy as well as environmental modelling done with MLS can be found in Barber et al (2008), Brenner (2009), Clarke (2004), El-Sheimy (2005), Früh and Zakhor (2004), Graham (2010), Haala et al (2008), Hassan and El-Sheimy (2008), Jaakkola et al (2008), Kukko et al (2007), Kukko and Hyyppä (2009), Lehtomäki et al (2010), Lin et al (2010), Lin and Hyyppä (2011), Manandhar and Shibasaki (2002), Petrie (2010), Shen et al (2008), Steinhauser et al (2008), Tao and Li (2007), Weiss and Dietmayer (2007), Yu et al (2007) and Shibasaki (2003a,b, 2005). System manufacturers have carried out and published their own tests, but few scientific publications exist where system performances are examined and compared using a test field established by and results analyzed by an independent actor.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Mobile Laser Scanning Systems Using a Permanent Test Field

Kaartinen

Kukko

Hyyppä

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Inter-Commission Working Group V/I KEY WORDS: GPS/INS, Reference Data, Systems, Laser scanning, Point Cloud, Accuracy, Mobile, Geometric ABSTRACT:The objective of the study was to benchmark the geometric accuracy of mobile laser scanning (MLS) systems using a permanent test field under good coverage of GNSS. Mobile laser scanning, also called mobile terrestrial laser scanning, is currently a rapidly developing area in laser scanning where laser scanners, GNSS and IMU are mounted onboard a moving vehicle. MLS can be considered to fill the gap between airborne and terrestrial laser scanning. Data provided by MLS systems can be characterized with the following technical parameters: a) point density in the range of 100-1000 points per m 2 at 10 m distance, b) distance measurement accuracy of 2-5 cm, and c) operational scanning range from 1 to 100 m. Several commercial, including e.g. Riegl, Optech and others, and some research mobile laser scanning systems surveyed the test field using predefined driving speed and directions. The acquired georeferenced point clouds were delivered for analyzing. The geometric accuracy of the point clouds was determined using the reference targets that could be identified and measured from the point cloud. Results show that in good GNSS conditions most systems can reach an accuracy of 2 cm both in plane and elevation. The accuracy of a low cost system, the price of which is less than tenth of the other systems, seems to be within a few centimetres at least in ground elevation determination. Inaccuracies in the relative orientation of the instruments lead to systematic errors and when several scanners are used, in multiple reproductions of the objects. Mobile laser scanning systems can collect high density point cloud data with high accuracy. A permanent test field suits well for verifying and comparing the performance of different mobile laser scanning systems. The accuracy of the relative orientation between the mapping instruments needs more attention. For example, if the object is seen double in the point cloud due to imperfect boresight calibration between two scanners, this will make especially the automatic modelling of the object much more challenging.

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From TLS to VLS: Biomass Estimation at Individual Tree Level

Cited by 49 publications

References 37 publications

Mapping Topography Changes and Elevation Accuracies Using a Mobile Laser Scanner

Mapping Topography Changes and Elevation Accuracies Using a Mobile Laser Scanner

A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

Benchmarking Mobile Laser Scanning Systems Using a Permanent Test Field

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