Stem Measurements and Taper Modeling Using Photogrammetric Point Clouds

Fang, Ruochen; Strîmbu, Bogdan M.

doi:10.3390/rs9070716

Cited by 26 publications

(26 citation statements)

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“…The main advantage of terrestrial CRP is that it produces similar outputs with lower hardware costs when compared to TLS systems. Only a few recent studies have explored this new approach [24][25][26][27][28]; they reported a root mean square error of DBH estimation from CRP that varied from 0.91 cm to 6.79 cm.…”

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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“…We created the point cloud using structure-from-motion [18][19][20][21], as implemented in Agisoft version 1.3.4 [22]. The Agisoft parameters used to process the images were based on the recommendations of Fang and Strimbu [15,23].…”

Section: Tree Segmentation From the Above Canopy Acquired Point Cloudsmentioning

confidence: 99%

“…A similar procedure, and consequently point cloud, was obtained from the images captured with GoPro camera. We produced separate point clouds for each device by processing the images with Agisoft version 1.3.4 [22] using the parameters of Fang and Strimbu [15,23]. The major difference between the two-point clouds (i.e., from Nikon and from GoPro) being their positioning, one in relative coordinates (i.e., disconnected from reality-Nikon), and the other relatively positioned in the correct region (i.e., no accuracy-GoPro).…”

Section: Generation Of Point Clouds From Below Canopy Imagesmentioning

confidence: 99%

“…We produced the PPC from the images collected above-canopy based on the study of Fang and Strimbu [15]. The options used for image alignment and point densifications were: half size images, 100,000 key points, 40,000 tie points, and the outliers identified assuming that no meaningfully small details are present (i.e., the aggressive depth filtering option).…”

Section: Initial Geo-referencingmentioning

confidence: 99%

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Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

2019

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Accurate and precise location of trees from data acquired under-the-canopy is challenging and time-consuming. However, current forestry practices would benefit tremendously from the knowledge of tree coordinates, particularly when the information used to position them is acquired with inexpensive sensors. Therefore, the objective of our study is to geo-reference trees using point clouds created from the images acquired below canopy. We developed a procedure that uses the coordinates of the trees seen from above canopy to position the same trees seen below canopy. To geo-reference the trees from above canopy we captured images with an unmanned aerial vehicle. We reconstructed the trunk with photogrammetric point clouds built with a structure-from-motion procedure from images recorded in a circular pattern at multiple locations throughout the stand. We matched the trees segmented from below canopy with the trees extracted from above canopy using a non-rigid point-matching algorithm. To ensure accuracy, we reduced the number of matching trees by dividing the trees segmented from above using a grid with 50 m cells. Our procedure was implemented on a 7.1 ha Douglas-fir stand from Oregon USA. The proposed procedure is relatively fast, as approximately 600 trees were mapped in approximately 1 min. The procedure is sensitive to the point density, directly impacting tree location, as differences larger than 2 m between the coordinates of the tree top and the bottom part of the stem could lead to matching errors larger than 1 m. Furthermore, the larger the number of trees to be matched the higher the accuracy is, which could allow for misalignment errors larger than 2 m between the locations of the trees segmented from above and below.needed. Stand level information is particularly needed for intermediate cuts, when the decision is taken considering all the trees not only a portion of them. Although total height of each tree in a stand can be relatively easy estimated from point clouds, lidar or photogrammetric, there are at least two attributes that are difficult to estimate accurately and precisely for all trees: dbh and location. Many algorithms have been developed to estimate dbh from above canopy data (e.g., point clouds or images) with significant success [4][5][6][7]. However, while expected accuracy is in many instances achieved, the precision of estimating dbh from above canopy is limited, which restricts its operational usage. The effort placed in estimation of tree location mirror the one for tree size, but the success was clearly less impressive, as the accuracy is still measured in meters [8].Point clouds play a significant role in forest operations, particularly in road design and road maintenance [9,10]. Point clouds generated without an active sensor, such as lidar, or stereopsis are sometimes labeled photogrammetric point clouds [11] or phodar [12]. Photogrammetric point clouds (PPC) made inroads in harvesting, but they were employed similarly with aerial laser scanning, as the applications were based on t...

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“…Several authors have used terrestrial SfM photogrammetry to reconstruct trees within plots [20][21][22][23][24][25] while others focused on individual trees [12,19,[26][27][28][29]. The root mean square error (RMSE) of the DBH estimation is generally more accurate for studies focused on individual trees, where the RMSE is mainly < 1 cm; whereas, for those in which whole plots are reconstructed, the RMSE is a couple of centimetres.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive monitoring of annual trunk increments by terrestrial structure from motion photogrammetry

et al. 2020

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Annual trunk increments are essential for short-term analyses of the response of trees to various factors. For instance, based on annual trunk increments, it is possible to develop and calibrate forest growth models. We investigated the possibility of estimating annual trunk increments from the terrestrial structure from motion (SfM) photogrammetry. Obtaining the annual trunk increments of mature trees is challenging due to the relatively small growth of trunks within one year. In our experiment, annual trunk increments were obtained by two conventional methods: measuring tape (perimeter increment) at heights of 0.8, 1.3, and 1.8 m on the trunk and increment borer (diameter increment) at a height of 1.3 m on the trunk. The following tree species were investigated: Fagus sylvatica L. (beech), Quercus petraea (Matt.) Liebl. (oak), Picea abies (L.) H. Karst (spruce), and Abies alba Mill (fir). The annual trunk increments ranged from 0.9 cm to 2.4 cm (tape/perimeter) and from 0.7 mm to 3.1 mm (borer/diameter). The data were collected before-and after-vegetation season, besides the data collection increment borer. When the estimated perimeters from the terrestrial SfM photogrammetry were compared to those obtained using the measuring tape, the root mean square error (RMSE) was 0.25-1.33 cm. The relative RMSE did not exceed 1% for all tree species. No statistically significant differences were found between the annual trunk increments obtained using the measuring tape and terrestrial SfM photogrammetry for beech, spruce, and fir. Only in the case of oak, the difference was statistically significant. Furthermore, the correlation coefficient between the annual trunk increments collected using the increment borer and those derived from terrestrial SfM photogrammetry was positive and equal to 0.6501. Terrestrial SfM photogrammetry is a hardware low-demanding technique that provides accurate three-dimensional data that can, based on our results, even detect small temporal tree trunk changes.

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Stem Measurements and Taper Modeling Using Photogrammetric Point Clouds

Cited by 26 publications

References 50 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

Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

Non-destructive monitoring of annual trunk increments by terrestrial structure from motion photogrammetry

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