Accuracy of Structure from Motion models in comparison with terrestrial laser scanner for the analysis of DBH and height influence on error behaviour

Panagiotidis, Dimitrios; Surový, Peter; Kuželka, Karel

doi:10.17221/92/2015-jfs

Cited by 31 publications

(25 citation statements)

References 25 publications

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“…A couple of data acquisition methods can be used. For example, in the stop-and-go method, the operator takes images in a static position and then moves to another position and takes another static image [24,30]. The second approach is a mobile method.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Close-Range Photogrammetry Image Collection Methods for Estimating Tree Diameters

Mokroš

Liang

Surový

et al. 2018

IJGI

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Abstract:The potential of close-range photogrammetry (CRP) to compete with terrestrial laser scanning (TLS) to produce dense and accurate point clouds has increased in recent years. The use of CRP for estimating tree diameter at breast height (DBH) has multiple advantages over TLS. For example, point clouds from CRP are similar to TLS, but hardware costs are significantly lower. However, a number of data collection issues need to be clarified before the use of CRP in forested areas is considered effective. In this paper we focused on different CRP data collection methods to estimate DBH. We present seven methods that differ in camera orientation, shooting mode, data collection path, and other important factors. The methods were tested on a research plot comprised of European beeches (Fagus sylvatica L.). The circle-fitting algorithm was used to estimate DBH. Four of the seven methods were capable of producing a dense point cloud. The tree detection rate varied from 49% to 81%. Estimates of DBH produced a root mean square error that varied from 4.41 cm to 5.98 cm. The most accurate method was achieved using a vertical camera orientation, stop-and-go shooting mode, and a path leading around the plot with two diagonal paths through the plot. This method also had the highest rate of tree detection (81%).

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Section: Introductionmentioning

confidence: 99%

Evaluation of Close-Range Photogrammetry Image Collection Methods for Estimating Tree Diameters

Mokroš

Liang

Surový

et al. 2018

IJGI

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“…Liang et al [17] were the first to investigate the feasibility of terrestrial SfM point clouds on forest plots in comparison with those from TLS, followed by a few others [39][40][41][42][43].…”

Section: Discussionmentioning

confidence: 99%

“…Using our data, stem curves could be reconstructed reliably up to about 3 m above ground. The use of terrestrial SfM photogrammetry for deriving tree heights by capturing images at three different levels was tested by [40]. They conclude that accuracy decreases with tree height due to larger distances from the sensor (i.e., lower resolution at the object), lower point density because of lower image overlap, and increasing difficulty in distinguishing stem points.…”

Section: Treesmentioning

confidence: 99%

“…The imaging environment in a natural forest poses a significant challenge due to the lighting conditions, tree density and occlusions by branches, and shrubs. To the best of our knowledge, only a small number of studies have evaluated terrestrial SfM photogrammetry in natural forests at plot scale (with diameters ranging from 10 to 40 m) to acquire forest inventory parameters such as DBH and tree positions [17,[39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

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Terrestrial Structure from Motion Photogrammetry for Deriving Forest Inventory Data

et al. 2019

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The measurements of tree attributes required for forest monitoring and management planning, e.g., National Forest Inventories, are derived by rather time-consuming field measurements on sample plots, using calipers and measurement tapes. Therefore, forest managers and researchers are looking for alternative methods. Currently, terrestrial laser scanning (TLS) is the remote sensing method that provides the most accurate point clouds at the plot-level to derive these attributes from. However, the demand for even more efficient and effective solutions triggers further developments to lower the acquisition time, costs, and the expertise needed to acquire and process 3D point clouds, while maintaining the quality of extracted tree parameters. In this context, photogrammetry is considered a potential solution. Despite a variety of studies, much uncertainty still exists about the quality of photogrammetry-based methods for deriving plot-level forest attributes in natural forests. Therefore, the overall goal of this study is to evaluate the competitiveness of terrestrial photogrammetry based on structure from motion (SfM) and dense image matching for deriving tree positions, diameters at breast height (DBHs), and stem curves of forest plots by means of a consumer grade camera. We define an image capture method and we assess the accuracy of the photogrammetric results on four forest plots located in Austria and Slovakia, two in each country, selected to cover a wide range of conditions such as terrain slope, undergrowth vegetation, and tree density, age, and species. For each forest plot, the reference data of the forest parameters were obtained by conducting field surveys and TLS measurements almost simultaneously with the photogrammetric acquisitions. The TLS data were also used to estimate the accuracy of the photogrammetric ground height, which is a necessary product to derive DBHs and tree heights. For each plot, we automatically derived tree counts, tree positions, DBHs, and part of the stem curve from both TLS and SfM using a software developed at TU Wien (Forest Analysis and Inventory Tool, FAIT), and the results were compared. The images were oriented with errors of a few millimetres only, according to checkpoint residuals. The automatic tree detection rate for the SfM reconstruction ranges between 65% and 98%, where the missing trees have average DBHs of less than 12 cm. For each plot, the mean error of SfM and TLS DBH estimates is −1.13 cm and −0.77 cm with respect to the caliper measurements. The resulting stem curves show that the mean differences between SfM and TLS stem diameters is at maximum −2.45 cm up to 3 m above ground, which increases to almost +4 cm for higher elevations. This study shows that with the adopted image capture method, terrestrial SfM photogrammetry, is an accurate solution to support forest inventory for estimating the number of trees and their location, the DBHs and stem curve up to 3 m above ground.

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“…For a metric evaluation of the differences between models generated by the mobile phones and those generated by a professional camera, a statistical evaluation of the measured geometric differences between the models was done utilizing the Weibull distribution. The Weibull distribution is a continuous probability distribution and it was applied as it is typically used in reliability analyses and used to determine the accuracies of structure-from-motion models [43]. The distribution is defined by the probability density function given in Equation (2) below.…”

Section: Assessment Of the Accuracy Of Models Generated From Mobile Pmentioning

confidence: 99%

Exploiting Low-Cost 3D Imagery for the Purposes of Detecting and Analyzing Pavement Distresses

2020

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Road pavement conditions have significant impacts on safety, travel times, costs, and environmental effects. It is the responsibility of road agencies to ensure these conditions are kept in an acceptable state. To this end, agencies are tasked with implementing pavement management systems (PMSs) which effectively allocate resources towards maintenance and rehabilitation. These systems, however, require accurate data. Currently, most agencies rely on manual distress surveys and as a result, there is significant research into quick and low-cost pavement distress identification methods. Recent proposals have included the use of structure-from-motion techniques based on datasets from unmanned aerial vehicles (UAVs) and cameras, producing accurate 3D models and associated point clouds. The challenge with these datasets is then identifying and describing distresses. This paper focuses on utilizing images of pavement distresses in the city of Palermo, Italy produced by mobile phone cameras. The work aims at assessing the accuracy of using mobile phones for these surveys and also identifying strategies to segment generated 3D imagery by considering the use of algorithms for 3D Image segmentation to detect shapes from point clouds to enable measurement of physical parameters and severity assessment. Case studies are considered for pavement distresses defined by the measurement of the area affected such as different types of cracking and depressions. The use of mobile phones and the identification of these patterns on the 3D models provide further steps towards low-cost data acquisition and analysis for a PMS.

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Accuracy of Structure from Motion models in comparison with terrestrial laser scanner for the analysis of DBH and height influence on error behaviour

Cited by 31 publications

References 25 publications

Evaluation of Close-Range Photogrammetry Image Collection Methods for Estimating Tree Diameters

Evaluation of Close-Range Photogrammetry Image Collection Methods for Estimating Tree Diameters

Terrestrial Structure from Motion Photogrammetry for Deriving Forest Inventory Data

Exploiting Low-Cost 3D Imagery for the Purposes of Detecting and Analyzing Pavement Distresses

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