Estimating tree stem diameters and volume from smartphone photogrammetric point clouds

Marzulli, Maria Immacolata; Raumonen, Pasi; Greco, Roberto; Persia, Manuela; Tartarino, Patrizia

doi:10.1093/forestry/cpz067

Cited by 34 publications

(24 citation statements)

References 41 publications

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“…Note that the RMSE value of 0.886 cm was significantly lower. Previous studies using the SfM method also gained DBH estimates with similar RMSE values from 0.911 cm to 5.07 cm [36][37][38][39][40]. These RMSE ranges may represent the potential error of the SfM method because most dendrometers can provide adequate measurements of the outside bark diameter for field inventory applications [41].…”

Section: Three-dimensional Tree Structurementioning

confidence: 76%

Applicability of Structure-from-Motion Photogrammetry on Forest Measurement in the Northern Ethiopian Highlands

et al. 2021

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Ethiopia is one of the countries with the most degraded forest resources. Information on tree structure is needed at some points in the process to assess the appropriateness of forest management. The objectives are to examine whether the Structure from Motion (SfM)-based photogrammetry can be used to derive the forest structural parameters, and how the tree structural parameters can vary by location. In this study, the possible applicability of low-cost SfM-based photogrammetry was evaluated for forest management and conservation purposes in the Adi Zaboy watershed of the Northern Ethiopian highlands. In the watershed, dwarf Acacia etbaica was sparsely distributed. Consequently, the full three-dimensional point clouds of the individual trees were generated, which provided a wide variety of tree structural parameters in a non-destructive manner. The R2 values for tree height, canopy width, and stump diameter were 0.936, 0.891, and 0.808, respectively, and the corresponding RMSE values were 0.128 m, 0.331 m, and 0.886 cm. In addition, differences in forest structure and composition were caused by differences in the environment. The SfM-based photogrammetry would provide fundamental information to meet the demand of sustainable forest management from a morphological point of view, especially in forests of Ethiopian highlands.

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Section: Three-dimensional Tree Structurementioning

confidence: 76%

Applicability of Structure-from-Motion Photogrammetry on Forest Measurement in the Northern Ethiopian Highlands

et al. 2021

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“…In this paper, a high-precision sensor is attached to the smartphone to improve the measurement accuracy, and a single photo solution method of photogrammetry is applied to the monitoring of forest resources based on smartphone. Compared with other forest resource monitoring methods based on smartphones [26,[42][43][44][45], this method can directly measure the position, DBH, and height Mobile Information Systems of trees. e measurement accuracy will not be affected by different configurations of smartphones, so this method has the advantage of strong universality.…”

Section: Discussionmentioning

confidence: 99%

New Method for Forest Resource Data Collection Based on Smartphone Fusion with Multiple Sensors

Fan

Dong

Chen

et al. 2020

Mobile Information Systems

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Tree parameter measurement is an important part of forest resource monitoring. Smartphones play an important role in forest resource surveys. Although sensors inside smartphones, such as gyroscopes and angle sensors, can meet the needs of the public for entertainment or games, the measurement accuracy in professional forest resource monitoring is slightly insufficient. In this paper, a method of collecting tree measurement factors based on personal smart space fusion with a variety of high-precision sensors is proposed. First of all, a high-precision attitude sensor measurement module and a laser ranging module are organically integrated and packaged in a black box. The smartphone is then connected to the sensor box using a magnet sheet, and the working personnel can determine key parameters in the forest stand by holding it. Finally, in order to verify the accuracy of the method, the measured values in this paper are compared with the reference values. The root mean square error (RMSE) of the tree position in the X and Y directions was 0.114 m and 0.147 m, the relative deviations (rBias) were 0.95% and 0.39%, and the average RMSE was 0.186 m. The RMSEs measured by tree height and diameter at breast height (DBH) were 0.98 m and 2.24 cm, the relative root mean square error (rRMSE) was 5.87% and 13.46%, and the relative deviations (rBias) were −1.40% and −1.06%, respectively. Therefore, the method of forest stand parameter measurement based on personal smart space fusion multitype sensors proposed in this paper can be effectively applied to forest resource data collection.

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“…QSM is often used to study the extraction of AGB or the angle and radius of first-order branches of trees for the automatic identification of tree species [23] and few studies on the relatively simple diameter at breast height of trees [34]. However, there are few studies on the fine structure of fruit trees and a few applications in agriculture.…”

Section: The Challenge Of Extracting Fruit Tree Branch Information Using Qsmmentioning

confidence: 99%

Apple Tree Branch Information Extraction from Terrestrial Laser Scanning and Backpack-LiDAR

et al. 2020

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The branches of fruit trees provide support for the growth of leaves, buds, flowers, fruits, and other organs. The number and length of branches guarantee the normal growth, flowering, and fruiting of fruit trees and are thus important indicators of tree growth and yield. However, due to their low height and the high number of branches, the precise management of fruit trees lacks a theoretical basis and data support. In this paper, we introduce a method for extracting topological and structural information on fruit tree branches based on LiDAR (Light Detection and Ranging) point clouds and proved its feasibility for the study of fruit tree branches. The results show that based on Terrestrial Laser Scanning (TLS), the relative errors of branch length and number are 7.43% and 12% for first-order branches, and 16.75% and 9.67% for second-order branches. The accuracy of total branch information can reach 15.34% and 2.89%. We also evaluated the potential of backpack-LiDAR by comparing field measurements and quantitative structural models (QSMs) evaluations of 10 sample trees. This comparison shows that in addition to the first-order branch information, the information about other orders of branches is underestimated to varying degrees. The root means square error (RMSE) of the length and number of the first-order branches were 3.91 and 1.30 m, and the relative root means square error (NRMSE) was 14.62% and 11.96%, respectively. Our work represents the first automated classification of fruit tree branches, which can be used in support of precise fruit tree pruning, quantitative forecast of yield, evaluation of fruit tree growth, and the modern management of orchards.

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Estimating tree stem diameters and volume from smartphone photogrammetric point clouds

Cited by 34 publications

References 41 publications

Applicability of Structure-from-Motion Photogrammetry on Forest Measurement in the Northern Ethiopian Highlands

Applicability of Structure-from-Motion Photogrammetry on Forest Measurement in the Northern Ethiopian Highlands

New Method for Forest Resource Data Collection Based on Smartphone Fusion with Multiple Sensors

Apple Tree Branch Information Extraction from Terrestrial Laser Scanning and Backpack-LiDAR

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