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

Mokroš, Martin; Liang, Xinlian; Surový, Peter; Valent, Peter; Čerňava, Juraj; Chudý, František; Tunák, Daniel; Saloň, Šimon; Merganič, Ján

doi:10.3390/ijgi7030093

Cited by 89 publications

(85 citation statements)

References 30 publications

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“…Research on the application of photogrammetry (both terrestrial and airborne) in forestry has increased after the invention of the structure-from-motion (SfM) and multi-view stereo (MVS) techniques, which are closely related to computer vision and learning [14,18]. Forests are measured photogrammetrically at the stand [1], plot [15,17], and tree levels [10,18]. Our study provides the results of the 3D capture and reconstruction of individual trees that can potentially be applied towards quality and quantity estimations [22], the creation of high-precision tree models for growth models and simulations [23], etc.…”

Section: Discussionmentioning

confidence: 99%

High Precision Individual Tree Diameter and Perimeter Estimation from Close-Range Photogrammetry

Mokroš

Výbošťok

Tomaštík

et al. 2018

Forests

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Close-range photogrammetry (CRP) can be used to provide precise and detailed three-dimensional data of objects. For several years, CRP has been a subject of research in forestry. Several studies have focused on tree reconstruction at the forest stand, plot, and tree levels. In our study, we focused on the reconstruction of trees separately within the forest stand. We investigated the influence of camera lens, tree species, and height of diameter on the accuracy of the tree perimeter and diameter estimation. Furthermore, we investigated the variance of the perimeter and diameter reference measurements. We chose four tree species (Fagus sylvatica L., Quercus petraea (Matt.) Liebl., Picea abies (L.) H. Karst. and Abies alba Mill.). The perimeters and diameters were measured at three height levels (0.8 m, 1.3 m, and 1.8 m) and two types of lenses were used. The data acquisition followed a circle around the tree at a 3 m radius. The highest accuracy of the perimeter estimation was achieved when a fisheye lens was used at a height of 1.3 m for Fagus sylvatica (root mean square error of 0.25 cm). Alternatively, the worst accuracy was achieved when a non-fisheye lens was used at 1.3 m for Quercus petraea (root mean square error of 1.27 cm). The tree species affected the estimation accuracy for both diameters and perimeters.

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Section: Discussionmentioning

confidence: 99%

High Precision Individual Tree Diameter and Perimeter Estimation from Close-Range Photogrammetry

Mokroš

Výbošťok

Tomaštík

et al. 2018

Forests

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“…Meanwhile, image-based point clouds provide a low-cost alternative to the LS-based point clouds (Liang et al 2015;Forsman et al 2016a;Hyyppä et al 2017;Tomaštík et al 2017;Mokroš et al 2018). Forest in situ measurements by non-professional users, such as forest owners, has become feasible by using, e.g., a cellphone camera.…”

Section: Introductionmentioning

confidence: 99%

Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements

et al. 2019

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Background: Lately, terrestrial point clouds have drawn attention as a new data source for in situ forest investigations. So far, terrestrial laser scanning (TLS) has the highest data quality among all terrestrial point cloud data in terms of geometric accuracy and level of detail (IEEE Transact Geosci Remote Sens 53: 5117-5132, 2015). The TLS point clouds processed by automated algorithms can provide certain individual tree parameters at close to required accuracy in practical applications. However, all terrestrial point clouds face a general challenge, which is the occlusions of upper tree crowns. An emerging technology called unmanned-aerial-vehicle (UAV) -borne laser scanning (ULS) potentially combines the strengths of above and under canopy surveys.Results: The performance of ULS are evaluated in 22 sample plots of various forest stand conditions in a boreal forest. The forest parameter estimates are benchmarked through a comparison with state-of-the-art terrestrial mechanisms from both static terrestrial and mobile laser scanning. The results show that in easy forest stand conditions, the performance of ULS point cloud is comparable with the terrestrial solutions. Conclusions: This study gives the first strict evaluation of ULS in situ observations in varied forest conditions. The study also acts as a benchmarking of available active remote sensing techniques for forest in situ mensuration. The results indicate that the current off-the-shelf ULS has an excellent tree height/tops measurement performance. Although the geometrical accuracy of the ULS data, especially at the stem parts, does not yet reach the level of other terrestrial point clouds, the unbeatable high mobility and fast data acquisition make the ULS a very attractive option in forest investigations.

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“…Principally, it should be feasible to use the proposed algorithm together with point cloud data obtained with other sensors, such as mobile laser scanners [42,43] or close range photogrammetry [44][45][46][47][48], as long as a comparable point-cloud density is achieved. For point clouds with a lower density, e.g., obtained from airborne laser scanning (ALS), other approaches are required [49][50][51][52][53].…”

Section: Sensormentioning

confidence: 99%

Automatic Assessment of Crown Projection Area on Single Trees and Stand-Level, Based on Three-Dimensional Point Clouds Derived from Terrestrial Laser-Scanning

Ritter

Nothdurft

2018

Forests

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Abstract:Crown projection area (CPA) is a critical parameter in assessing inter-tree competition and estimating biomass volume. A multi-layer seeded region growing-based approach to the fully automated assessment of CPA based on 3D-point-clouds derived from terrestrial laser scanning (TLS) is presented. Independently repeated manual CPA-measurements in a subset of the stand serve as the reference and enable quantification of the inter-observer bias. Allometric models are used to predict CPA for the whole stand and are compared to the TLS-based estimates on the single tree-and stand-level. It is shown that for single trees, the deviation between CPA measurements derived from TLS data and manual measurements is on par with the deviations between manual measurements by different observers. The inter-observer bias propagates into the allometric models, resulting in a high uncertainty of the derived estimates at tree-level. Comparing the allometric models to the TLS measurements at stand-level reveals the high influence of crown morphology, which only can be taken into account by the TLS measurements and not by the allometric models.

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

Cited by 89 publications

References 30 publications

High Precision Individual Tree Diameter and Perimeter Estimation from Close-Range Photogrammetry

High Precision Individual Tree Diameter and Perimeter Estimation from Close-Range Photogrammetry

Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements

Automatic Assessment of Crown Projection Area on Single Trees and Stand-Level, Based on Three-Dimensional Point Clouds Derived from Terrestrial Laser-Scanning

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