Map updating and change detection using vehicle-based laser scanning

Hyyppä, Juha; Jaakkola, Anttoni; Hyyppä, Hannu; Kaartinen, Harri; Kukko, Antero; Holopainen, Markus; Zhu, Long; Vastaranta, Mikko; Kaasalainen, Sanna; Krooks, Anssi; Litkey, Paula; Lyytikäinen‐Saarenmaa, Päivi; Matikainen, Leena; Rönnholm, Petri; Chen, Ruizhi; Chen, Yuwei; Kivilahti, Arhi; Kosonen, Iisakki

doi:10.1109/urs.2009.5137682

Cited by 32 publications

(30 citation statements)

References 7 publications

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“…However, often the laser scanner data are only used together with statistical models to give statistical estimates of tree properties. For example, standing tree biomass and its changes can be measured with TLS because they are highly correlated with the number of hits in the TLS point cloud [5,6].…”

Section: Introductionmentioning

confidence: 99%

Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data

et al. 2013

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This paper presents a new method for constructing quickly and automatically precision tree models from point clouds of the trunk and branches obtained by terrestrial laser scanning. The input of the method is a point cloud of a single tree scanned from multiple positions. The surface of the visible parts of the tree is robustly reconstructed by making a flexible cylinder model of the tree. The thorough quantitative model records also the topological branching structure. In this paper, every major step of the whole model reconstruction process, from the input to the finished model, is presented in detail. The model is constructed by a local approach in which the point cloud is covered with small sets corresponding to connected surface patches in the tree surface. The neighbor-relations and geometrical properties of these cover sets are used to reconstruct the details of the tree and, step by step, the whole tree. The point cloud and the sets are segmented into branches, after which the branches are modeled as collections of cylinders. From the model, the branching structure and size properties, such as volume and branch size distributions, for the whole tree or some of its parts, can be approximated. The approach is validated using both measured and modeled terrestrial laser scanner data from real trees and detailed 3D models. The results Remote Sens. 2013, 5 492 show that the method allows an easy extraction of various tree attributes from terrestrial or mobile laser scanning point clouds.

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Section: Introductionmentioning

confidence: 99%

Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data

et al. 2013

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“…These methods are capable of providing tree maps within one-meter accuracy [23,25,26,28]. It is suggested by many researchers [3,5,20,24,26,[28][29][30][31] that these ground-based laser sensors could be mounted to the harvesters to measure standing trees during the logging. The forest industry is also eager to develop harvester measurements and mount these 2D or 3D laser scanners to the harvester.…”

Section: Introductionmentioning

confidence: 99%

Multisource Single-Tree Inventory in the Prediction of Tree Quality Variables and Logging Recoveries

et al. 2014

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Abstract:The stem diameter distribution, stem form and quality information must be measured as accurately as possible to optimize cutting. For a detailed measurement of the stands, we developed and demonstrated the use of a multisource single-tree inventory (MS-STI). The two major bottlenecks in the current airborne laser scanning (ALS)-based single-tree-level inventory, tree detection and tree species recognition, are avoided in MS-STI. In addition to airborne 3D data, such as ALS, MS-STI requires an existing tree map with tree species information as the input information. In operational forest management, tree mapping would be carried out after or during the first thinning. It should be highlighted that the tree map is a challenging prerequisite, but that the recent development in mobile 2D and 3D laser scanning indicates that the solution is within reach. In our study, the tested input tree map was produced by terrestrial laser scanning (TLS) and by using a Global Navigation Satellite System. Predictors for tree quality OPEN ACCESSRemote Sens. 2014, 6 3476 attributes were extracted from ALS data or digital stereo imagery (DSI) and used in the nearest-neighbor estimation approach. Stem distribution was compiled by summing the predicted single-tree measures. The accuracy of the MS-STI was validated using harvester data (timber assortments) and field measures (stem diameter, tree height). RMSEs for tree height, diameter, saw log volume and pulpwood volume varied from 4.2% to 5.3%, from 10.9% to 19.9%, from 28.7% to 43.5% and from 125.1% to 134.3%, respectively. Stand-level saw log recoveries differed from −2.2% to 1.3% from the harvester measurements, as the respective differences in pulpwood recovery were between −3.0% and 10.6%. We conclude that MS-STI improves the predictions of stem-diameter distributions and provides accurate estimates for tree quality variables if an accurate tree map is available.

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“…These results are similar to those previously published (e.g., [26,29]). Liang et al [28] showed that even more accurate dbh estimation can be achieved using automatic stem curve measurements. The estimation accuracy of tree h was also similar to that obtained in previous studies (e.g., [26,29]).…”

Section: Discussionmentioning

confidence: 99%

“…The same level of detail cannot be achieved by any conventional field measurements [21][22][23][24][25][26][27][28][29]. The challenge in the use of TLS is the data processing, which should be fully automatized.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Timber Quality of Scots Pine with Terrestrial Laser Scanning

Kankare

Joensuu

Holopainen

et al. 2014

Forests

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Preharvest information on the quality of Scots pine (Pinus sylvestris) timber is required by the forest industry in Nordic countries, due to the strong association between the technical quality and product recovery of this species in particular. The objective of this study was to assess the accuracy of estimating external quality attributes and classifying the quality of mature Scots pine trees by terrestrial laser scanning (TLS). The tree quality was estimated using a random forest approach, based on both field and TLS measurements of stem diameters, tree height and branch heights. The relative root mean squared errors of the TLS measurements for tree height, diameter, diameter at 6 m and the lowest living and OPEN ACCESSForests 2014, 5 1880 dead branch height were 7.1%, 5.9%, 8.9%, 9.6% and 42.9%, respectively. The highest errors of the branch heights were caused by the shadowing effect in the point cloud data. The quality classes were estimated accurately, based on both (field and TLS measured) tree attributes. Trees were classified with 95.0% and 83.6% accuracy into three operationally-important quality classes and with 87.1% and 76.4% accuracy into five classes using, field or TLS measurements, respectively. The obtained quality classification results were promising. The enhanced tree quality information could have a significant effect on planning forest management procedures, wood supply chains and optimizing the flow of raw materials. To fully integrate tree quality measurements in operational forestry, the methods used should be fully automated.

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Map updating and change detection using vehicle-based laser scanning

Cited by 32 publications

References 7 publications

Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data

Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data

Multisource Single-Tree Inventory in the Prediction of Tree Quality Variables and Logging Recoveries

Estimation of the Timber Quality of Scots Pine with Terrestrial Laser Scanning

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