Estimation of Tree Stem Attributes Using Terrestrial Photogrammetry

Forsman, Mona; Börlin, Niclas; Holmgren, Johan

doi:10.5194/isprsarchives-xxxix-b5-261-2012

Cited by 20 publications

(14 citation statements)

References 7 publications

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“…Any calibrated rig removes the need for a scale bar, since the known length of the rig baseline establishes the scale of the scene. To satisfy the other requirements, the rig was designed with five cameras in positions such that no three-camera subset was collinear ( [18] and Figure 1). The five-camera design would be tolerant towards occlusions, since matching between any combination of three cameras would be sufficient to produce a point.…”

Section: Rig and Image Protocol Designmentioning

confidence: 99%

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Estimation of Tree Stem Attributes Using Terrestrial Photogrammetry with a Camera Rig

Forsman

Börlin

Holmgren

2016

Forests

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Abstract:We propose a novel photogrammetric method for field plot inventory, designed for simplicity and time efficiency on-site. A prototype multi-camera rig was used to acquire images from field plot centers in multiple directions. The acquisition time on-site was less than two minutes. From each view, a point cloud was generated using a novel, rig-based matching of detected SIFT keypoints. Stems were detected in the merged point cloud, and their positions and diameters were estimated. The method was evaluated on 25 hemi-boreal forest plots of a 10-m radius. Due to difficult lighting conditions and faulty hardware, imagery from only six field plots was processed. The method performed best on three plots with clearly visible stems with a 76% detection rate and 0% commission. Diameters could be estimated for 40% of the stems with an RMSE of 2.8-9.5 cm. The results are comparable to other camera-based methods evaluated in a similar manner. The results are inferior to TLS-based methods. However, our method is easily extended to multiple station image schemas, something that could significantly improve the results while retaining low commission errors and time on-site. Furthermore, with smaller hardware, we believe this could be a useful technique for measuring stem attributes in the forest.

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Section: Rig and Image Protocol Designmentioning

confidence: 99%

“…The algorithm for automatic point cloud computation was a development of the algorithm by Forsman et al [18]. Each image was processed using the VLFeat (VisionLab Features Library) implementation of the Scale Invariant Feature Transform (SIFT) algorithm [22].…”

Section: Point Cloud Constructionmentioning

confidence: 99%

Estimation of Tree Stem Attributes Using Terrestrial Photogrammetry with a Camera Rig

Forsman

Börlin

Holmgren

2016

Forests

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“…The result shows, 85% of the measured trees were within 0.5 m of the field-measured tree locations. A multicamera system was developed in [16], which had five calibrated digital cameras installed on a rig. Five images were simultaneously taken from a single viewpoint, and a point cloud was generated.…”

Section: Introductionmentioning

confidence: 99%

Forest Data Collection Using Terrestrial Image-Based Point Clouds From a Handheld Camera Compared to Terrestrial and Personal Laser Scanning

Liang

Wang

Jaakkola

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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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 Manuscript 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.Index Terms-Forest inventory, handheld camera, image-based point cloud, laser scanning (LS), Light Detection And Ranging (LiDAR), point cloud, structure from motion, terrestrial.

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“…As for terrestrial cases, a study to map individual trees in urban environment was reported recently [16]. A multi-camera system was presented in [17], which had five calibrated digital cameras installed on a rig. A point cloud was generated based on the five images simultaneously taken from a single viewpoint, and the DBHs of individual trees in the view were estimated with a root mean squared error (RMSE) of 2.08 cm.…”

Section: Introductionmentioning

confidence: 99%

The Use of a Hand-Held Camera for Individual Tree 3D Mapping in Forest Sample Plots

et al. 2014

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This paper evaluated the feasibility of a terrestrial point cloud generated utilizing an uncalibrated hand-held consumer camera at a plot level and measuring the plot at an individual-tree level. Individual tree stems in the plot were detected and modeled from the image-based point cloud, and the diameter-at-breast-height (DBH) of each tree was estimated. The detected-results were compared with field measurements and with those derived from the single-scan terrestrial laser scanning (TLS) data. The experiment showed that the mapping accuracy was 88% and the root mean squared error of DBH estimates of individual trees was 2.39 cm, which is acceptable for practical applications and was similar to the results achieved using TLS. The main advantages of the image-based point cloud data lie in the low cost of the equipment required for the data collection, the simple and fast field measurements and the automated data processing, which may be interesting and important for certain applications, such as field inventories by landowners who do not have supports from external experts. The disadvantages of the image-based point cloud data include the limited capability of mapping small trees and complex forest stands. OPEN ACCESSRemote Sens. 2014, 6 6588

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Estimation of Tree Stem Attributes Using Terrestrial Photogrammetry

Cited by 20 publications

References 7 publications

Estimation of Tree Stem Attributes Using Terrestrial Photogrammetry with a Camera Rig

Estimation of Tree Stem Attributes Using Terrestrial Photogrammetry with a Camera Rig

Forest Data Collection Using Terrestrial Image-Based Point Clouds From a Handheld Camera Compared to Terrestrial and Personal Laser Scanning

The Use of a Hand-Held Camera for Individual Tree 3D Mapping in Forest Sample Plots

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