Estimating Tree Position, Diameter at Breast Height, and Tree Height in Real-Time Using a Mobile Phone with RGB-D SLAM

Yi, Fan; Zhong-ke, Feng; Mannan, Abdul; Khan, Tauheed Ullah; Shen, Chaoyong; Saeed, Sajjad

doi:10.3390/rs10111845

Cited by 72 publications

(84 citation statements)

References 32 publications

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“…Mobile LiDAR, which can be mounted on vehicles, backpacks, and UAVs (unmanned aerial vehicles), and is capable of scanning while moving, is an alternative to terrestrial LiDAR that allows effective data collection over wide ranges. Due to this advantage, DBHs and tree heights have been estimated using mobile LiDAR at the forest scale (Fan et al 2018;Pierzchała et al 2018) and the urban street scale (Zhao et al 2018. In urban street conditions, there are obstacles such as cars and pedestrians to contend with when scanning using mobile LiDAR.…”

Section: Introductionmentioning

confidence: 99%

Estimating the heights and diameters at breast height of trees in an urban park and along a street using mobile LiDAR

et al. 2019

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Because trees can positively influence local environments in urban ecosystems, it is important to measure their morphological characteristics, such as height and diameter at breast height (DBH). However, measuring these data for each individual tree is a time-consuming process that requires a great deal of manpower. In this study, we investigated the feasibility of using mobile LiDAR to estimate tree height and DBH along urban streets and in urban parks. We compared measurements from a mobile LiDAR unit with field measurements of tree height and DBH in urban parks and streets. The height-above-ground and Pratt circle fit methods were applied to calculate tree height and DBH, respectively. The LiDAR-estimated tree heights were highly accurate albeit slightly underestimated, with a root mean square error of 0.359 m for the street trees and 0.462 m for the park trees. On the other hand, the estimated DBHs were moderately accurate and overestimated, with a root mean square error of 3.77 cm for the street trees and 8.95 cm for the park trees. Densely planted trees in the park and obstacles in urban areas result in "shadows" (areas with no data), reducing accuracy. Irregular trunk shapes and scanned data that did not include full data point coverage of every trunk were the reasons for the errors. Despite these errors, this study highlights the potential of tree measurements obtained with mobile terrestrial LiDAR platforms to be scaled up from point-based locations to neighborhood-scale and city-scale inventories.

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Section: Introductionmentioning

confidence: 99%

Estimating the heights and diameters at breast height of trees in an urban park and along a street using mobile LiDAR

et al. 2019

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“…Those authors mapped five dense forest plots, compared the results with manual field measurements, and reported an RMSE range from 0.90 to 1.90 cm. Reference [4] estimated the tree DBH of nine square plots using simultaneous localization and mapping (SLAM) algorithms paired with a time-of-flight (TOF) camera. The DBH estimations had a 0.33 mm (1.78%) BIAS and a 1.26 cm (6.39%) RMSE.…”

Section: Discussionmentioning

confidence: 99%

“…Our results also suggest that the device can estimate tree positions accurately, with the resulting BIAS (−8.55 to 14.88 cm on the x-axis and −12.07 to 24.49 cm on the y-axis), RMSE (12.94-33.96 cm and 17.78-28.43 cm on the x-axis and the y-axis, respectively), and Ed (30.06 cm) being small. Reference [4] estimated the tree position using the SLAM algorithms paired with a TOF camera and reported an RMSE of 0.12 m, regardless of the axis directions. Their results, however, were based on sample plots on a flat site with no weeds and small shrubs; therefore, the actual use of their method in dense forests needs further verification.…”

Section: Discussionmentioning

confidence: 99%

“…The SLAM algorithm, convex hull algorithm, and point cloud algorithm used in other studies [4,25,27,37] usually need mobile phones or computers with 32-or 64-bit capacity. The device reported in this paper only needs the STC15, an 8-bit microprocessor, with the price being less than one dollar.…”

Section: Discussionmentioning

confidence: 99%

“…Diameter at breast height (DBH), measured at a height of 1.3 m on the bole of a tree, is the most commonly measured tree attribute [1][2][3], whether for inventory, management, or research purposes, since many tree and forest attributes [4,5], such as basal area, volume [6], and stand density [7], are derived from DBH measurements [8]. Therefore, developing tools that can measure tree DBH accurately, efficiently, and conveniently is always desirable.…”

Section: Introductionmentioning

confidence: 99%

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An Integrated Method for Coding Trees, Measuring Tree Diameter, and Estimating Tree Positions

Sun

Fang

Weng

et al. 2019

Sensors

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Accurately measuring tree diameter at breast height (DBH) and estimating tree positions in a sample plot are important in tree mensuration. The main aims of this paper include (1) developing a new, integrated device that can identify trees using the quick response (QR) code technique to record tree identifications, measure DBH, and estimate tree positions concurrently; (2) designing an innovative algorithm to measure DBH using only two angle sensors, which is simple and can reduce the impact of eccentric stems on DBH measures; and (3) designing an algorithm to estimate the position of the tree by combining ultra-wide band (UWB) technology and altitude sensors, which is based on the received signal strength indication (RSSI) algorithm and quadrilateral localization algorithm. This novel device was applied to measure ten 10 × 10 m square plots of diversified environments and various tree species to test its accuracy. Before measuring a plot, a coded sticker was fixed at a height of 1.3 m on each individual tree stem, and four UWB module anchors were set up at the four corners of the plot. All individual trees’ DBHs and positions within the plot were then measured. Tree DBH, measured using a tree caliper, and the values of tree positions, measured using tape, angle ruler, and inclinometer, were used as the respective reference values for comparison. Across the plots, the decode rate of QR codes was 100%, with an average response time less than two seconds. The DBH values had a bias of 1.89 mm (1.88% in relative terms) and a root mean square error (RMSE) of 5.38 mm (4.53% in relative terms). The tree positions were accurately estimated; the biases on the x-axis and the y-axis of the tree position were −8.55–14.88 cm and −12.07–24.49 cm, respectively, and the corresponding RMSEs were 12.94–33.96 cm and 17.78–28.43 cm. The average error between the estimated and reference distances was 30.06 cm, with a standard deviation of 13.53 cm. The device is cheap and friendly to use in addition to its high accuracy. Although further studies are needed, our method provides a great alternative to conventional tools for improving the efficiency and accuracy of tree mensuration.

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Development of a novel noninvasive quantitative method to monitor Siraitia grosvenorii cell growth and browning degree using an integrated computer‐aided vision technology and machine learning

Zhu

Mohsin

Zaman

et al. 2021

Biotech & Bioengineering

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The rapid, accurate and noninvasive detection of biomass and plant cell browning can provide timely feedback on cell growth in plant cell culture. In this study, Siraitia grosvenorii suspension cells were taken as an example, a phenotype analysis platform was successfully developed to predict the biomass and the degree of cell browning based on the color changes of cells in computer‐aided vision technology. First, a self‐made laboratory system was established to obtain images. Then, matrices were prepared from digital images by a self‐developed high‐throughput image processing tool. Finally, classification models were used to judge different cell types, and then a semi‐supervised classification to predict different degrees of cell browning. Meanwhile, regression models were developed to predict the plant cell mass. All models were verified with a good agreement by biological experiments. Therefore, this method can be applied for low‐cost biomass estimation and browning degree quantification in plant cell culture.

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Estimating Tree Position, Diameter at Breast Height, and Tree Height in Real-Time Using a Mobile Phone with RGB-D SLAM

Cited by 72 publications

References 32 publications

Estimating the heights and diameters at breast height of trees in an urban park and along a street using mobile LiDAR

Estimating the heights and diameters at breast height of trees in an urban park and along a street using mobile LiDAR

An Integrated Method for Coding Trees, Measuring Tree Diameter, and Estimating Tree Positions

Development of a novel noninvasive quantitative method to monitor Siraitia grosvenorii cell growth and browning degree using an integrated computer‐aided vision technology and machine learning

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