Evaluating the efficiency of two close-range photogrammetry and smartphone LiDAR approaches in estimating the characteristics of diameter at breast height and stem height of trees (Case study: Oak forests of Lorestan province, Iran)

Document Type : Research article

Authors

1 Ph.D. Student of Forestry, Faculty of Natural Resources, Lorestan University, Khorramabad, Iran

2 Corresponding author, Associate Prof., Department of Forestry, Faculty of Natural Resources, Lorestan University, Khorramabad, Iran

3 Associate Prof., Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran

4 Associate Prof., Department of Forestry, Faculty of Natural Resources, Lorestan University, Khorramabad, Iran

Abstract

Background and objectives: Over the last two decades, the use of 3D remote sensing technologies such as mobile phone LiDAR and photogrammetric methods to extract point cloud information in forestry has grown significantly. Mobile phone LiDAR, due to its laser scanning capability, allows accurate 3D measurements of objects in a short time for single trees. This study measured crown base height and diameter at breast height under similar atmospheric and lighting conditions using close-range photogrammetry and mobile phone LiDAR approaches. The measurement process time from data collection to dense point cloud generation was also considered. The aim was to evaluate and compare the performance of these two methods in terms of accuracy for breast diameter and stem height estimation and acquisition time for 3D tree stem modeling.
Methodology: Sixteen individual oak trees were selected from four central Zagros sites in western Iran. The close-range photogrammetry method involved capturing images with a 360-degree rotation around the tree under manual focus and adequate lighting. For mobile LiDAR, the stem was scanned by walking around the tree. After image acquisition, processing steps included interior orientation, camera location determination, and input of control and check lengths previously marked on the stems, to produce 3D visualizations and dense point clouds. The iPhone LiDAR data were processed using Scaniverse software. Control and check lengths were measured directly with calipers before data collection. In Metashape, distances between check and control points were optimized by marking. Statistical parameters including RMSE, RMSE%, MAE, and MAE% were calculated by comparing intervals from photogrammetry and LiDAR point clouds to direct caliper measurements.
Results: The photogrammetry workflow and processing steps averaged across the 16 trees are tabulated. Obtaining a dense point cloud via photogrammetry requires completing ten processing steps. Processing time depends on the system used. In terms of timing, close-range photogrammetry requires ten steps after data acquisition to produce dense point clouds, whereas mobile phone LiDAR only involves two stages: scanning and direct processing. Results showed that the time needed to generate dense point clouds using photogrammetry was roughly 21 times longer than with mobile LiDAR for each tree. The photogrammetry approach yielded better accuracy for crown base height (RMSE = 6.63%), while mobile phone LiDAR performed better for breast height diameter estimation (RMSE = 1.86%).
Conclusion: Zagros oak trees, being monoecious and seed-propagated, provide a suitable test case for applying these technologies in forest cover studies. Tree characteristics such as thin crowns, sufficient light penetration to the stem, low altitude, and low crown branching facilitate measuring stem height. Comparing photogrammetric and LiDAR methods remains challenging due to different quality and accuracy criteria used since these technologies emerged. Ultimately, the choice between these technologies depends on whether the focus is single-tree or mass tree measurements.
 
 

Highlights

- An, P., Fang, K., Zhang, Y., Jiang, Y. and Yang, Y., 2022. Assessment of the trueness and precision of smartphone photogrammetry for rock joint roughness measurement. Measurement, 188: 110598.

- Çakir, G.Y., Post, C.J., Mikhailova, E.A. and Schlautman, M.A., 2021. 3D LiDAR scanning of urban forest structure using a consumer tablet. Urban Science, 5: 88.

- Chase, P.P.C., Clarke, K.H., Hawkes, A.J., Jabari, Sh. and Jakus, J.S., 2022. Apple iPhone 13 Pro LiDAR accuracy assessment for engineering applications. Proceedings of Transforming Construction with Reality Capture Technologies: The Digital Reality of Tomorrow. Fredericton, New Brunswick, Canada, 23-25 Aug. 2022: 10p.

- Corradetti, A., Seers, T.D., Billi, A. and Tavani, S., 2021. Virtual outcrops in a pocket: the smartphone as a fully equipped photogrammetric data acquisition tool. GSA Today, 31: 4-9.

- Fallah, M., Matkan, A.A. and Aghighi, H., 2024. Estimation of height and diameter at breast height of forest trees with multi-scale individual tree detection method and machine learning algorithms using airborne LiDAR data. Iranian Journal of Forest and Poplar Research, 32(2): 112-131 (In Persian with English summary).

- Fol, C.R., Murtiyoso, A. and Griess, V.C., 2022. Evaluation of Azure Kinect derived point clouds to determine the presence of microhabitats on single trees based on the Swiss standard parameters. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43(B2-2022): 989-994.

- Forsman, M., Borlin, N. and Holmgren, J., 2016. Estimation of tree stem attributes using terrestrial photogrammetry with a camera rig. Forests, 7(3): 61.

- Ganz, S., Käber, Y. and Adler, P., 2019. Measuring tree height with remote sensing—a comparison of photogrammetric and LiDAR data with different field measurements. Forests, 10: 694.

- Gollob, C., Ritter, T., Kraßnitzer, R., Tockner, A. and Nothdurft, A., 2021. Measurement of forest inventory parameters with Apple iPad Pro and integrated LiDAR technology. Remote Sensing, 13: 3129.

- Holcomb, A., Tong, L. and Keshav, S., 2023. Robust single-image tree diameter estimation with mobile phones. Remote Sensing, 15: 772.

- Hyyppä, J., Virtanen, J.P., Jaakkola, A., Yu, X., Hyyppä, H. and Liang, X., 2017. Feasibility of Google Tango and Kinect for crowdsourcing forestry information. Forests, 9(1): 6.

- Iglhaut, J., Cabo, C., Puliti, S., Piermattei, L., O’Connor, J. and Rosette, J., 2019. Structure from motion photogrammetry in forestry: A review. Current Forestry Reports, 5: 155-168.

- Jaud, M., Kervot, M., Delacourt, C. and Bertin, S., 2019. Potential of smartphone SfM photogrammetry to measure coastal morphodynamics. Remote Sensing, 11: 2242.

- Jiang, R., Jáuregui, D.V. and White, K.R., 2008. Close-range photogrammetry applications in bridge measurement: Literature review. Measurement, 41(8): 823-834.

- Karimzadeh Jafari, E. and Soosani, J., 2021. The efficiency of augmented reality technology in smartphones for estimating the height of trees (case study: green space conifers of Lorestan Factulty Agriculture and Natural Resources). Forest and Wood Products, 207(2): 197-207 (In Persian with English summary).

- Karimzadeh Jafari, E., Soosani, J., Varshosaz, M. and Naghavi, H., 2023. Investigatin the accuracy of iPhone LiDAR in preparing point clouds of tree trunks (Case study: Middle Zagros - oak forests of Lorestan province). Journal of Geomatics Science and Technology, 12(3): 63-73 (In Persian with English summary).

- Lim, K., Treitz, P., Wulder, M., St-Onge, B. and Flood, M., 2003. LiDAR remote sensing of forest structure. Progress in Physical Geography: Earth and Environment, 27: 88-106.

- Luetzenburg, G., Kroon, A. and Bjørk, A.A., 2021. Evaluation of the Apple iPhone 12 Pro LiDAR for an application in geosciences. Scientific Reports, 11: 22221.

- McGlade, J., Wallace, L., Reinke, K. and Jones, S., 2022. The potential of low-cost 3D imaging technologies for forestry applications: Setting a research agenda for low-cost remote sensing inventory tasks. Forests, 13: 204.

- Micheletti, N., Chandler, J.H. and Lane, S.N., 2015. Investigating the geomorphological potential of freely available and accessible structure-from-motion photogrammetry using a smartphone. Earth Surface Processes and Landforms, 40: 473-X486.

- Murtiyoso, A., Hristova, H., Rehush, N. and Griess, V.C., 2022. Low-cost mapping of forest under-storey vegetation using spherical photogrammetry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-2/W1: 185-190.

- Naeimaei, R. and Ghanbari Parmehr, E., 2023. Assessing the influence of image network and image texture on the quality of 3D point cloud production in close-range photogrammetry. Journal of Remote Sensing and Geoinformation Research, 1(2): 189-204 (In Persian with English summary).

- Naghibi Rad, S.A., Darvishsefat, A.A., Fatehi, P., Namiranian, M., Saadatseresht, M. and Boroumand, M., 2024. Evaluation of Octree-Based Segmentation (OBS) method to seperate ground point based on the handheld laser scanner data. Iranian Journal of Forest, 16(1): 137- 155 (In Persian with English summary).

- Pavlis, T.L., Langford, R., Hurtado, J. and Serpa, L., 2010. Computer-based data acquisition and visualization systems in field geology: Results from 12 years of experimentation and future potential. Geosphere, 6: 275-294.

- Poorazimy, M., Shataee Jouibary, Sh., Mohammadi, J. and Aghababaei, H., 2023. Feasibility of single-polarized TanDEM-X data for Hyrcanian forest height estimation (Case study: Shast-Kalateh forest). Iranian Journal of Forest, 15(3): 329-343 (In Persian with English summary).‏

- Sadeghian, H., Naghavi, H., Maleknia, R. and Sosani, J., 2022. Estimating the quantitative characteristics of seedlings using terrestrial close-range photogrammetry. Journal of Forest Research and Development, 7(4): 639-561 (In Persian with English summary).

- Schuon, S., Theobalt, C., Davis, J. and Thrun, S., 2008. High-quality scanning using time-of-flight depth superresolution. Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Anchorage, Alaska, USA, 23-28 Jun. 2008: 7p.

- Tatsumi, S., Yamaguchi, K. and Furuya, N., 2023. ForestScanner: A mobile application for measuring and mapping trees with LiDAR-equipped iPhone and iPad. Methods in Ecology and Evolution, 14: 1603-1609.

- Tavani, S., Billi, A., Corradetti, A., Mercuri, M., Bosman, A., Cuffaro, M., Seers, T. and Carminati, E., 2022. Smartphone assisted fieldwork: Towards the digital transition of geoscience fieldwork using LiDAR-equipped iPhones. Earth-Science Reviews, 227: 103969.

- Trochta, J., Krůček, M., Vrška, T. and Král, K., 2017. 3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR. PLoS ONE, 12(5): e0176871.

- van Leeuwen, M. and Nieuwenhuis, M., 2010. Retrieval of forest structural parameters using LiDAR remote sensing. European Journal of Forest Research, 129(4): 749-770.

- Vogt, M., Rips, A. and Emmelmann, C., 2021. Comparison of iPad Pro®’s LiDAR and TrueDepth capabilities with an industrial 3D scanning solution. Technologies, 9: 25.

- Xu, Z., Shen, X. and Cao, L., 2023. Extraction of forest structural parameters by the comparison of structure from motion (SfM) and backpack laser scanning (BLS) point clouds. Remote Sensing, 15: 2144.

- Zamani, P., Mohajeri, S.H. and Samadi, A., 2019. Application of structure from motion (SFM) method to determine the bed surface particles sizes in gravel bed rivers. Iranian Journal of Soil and Water Research, 50: 215-230 (In Persian with English summary).

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References

- An, P., Fang, K., Zhang, Y., Jiang, Y. and Yang, Y., 2022. Assessment of the trueness and precision of smartphone photogrammetry for rock joint roughness measurement. Measurement, 188: 110598.
- Çakir, G.Y., Post, C.J., Mikhailova, E.A. and Schlautman, M.A., 2021. 3D LiDAR scanning of urban forest structure using a consumer tablet. Urban Science, 5: 88.
- Chase, P.P.C., Clarke, K.H., Hawkes, A.J., Jabari, Sh. and Jakus, J.S., 2022. Apple iPhone 13 Pro LiDAR accuracy assessment for engineering applications. Proceedings of Transforming Construction with Reality Capture Technologies: The Digital Reality of Tomorrow. Fredericton, New Brunswick, Canada, 23-25 Aug. 2022: 10p.
- Corradetti, A., Seers, T.D., Billi, A. and Tavani, S., 2021. Virtual outcrops in a pocket: the smartphone as a fully equipped photogrammetric data acquisition tool. GSA Today, 31: 4-9.
- Fallah, M., Matkan, A.A. and Aghighi, H., 2024. Estimation of height and diameter at breast height of forest trees with multi-scale individual tree detection method and machine learning algorithms using airborne LiDAR data. Iranian Journal of Forest and Poplar Research, 32(2): 112-131 (In Persian with English summary).
- Fol, C.R., Murtiyoso, A. and Griess, V.C., 2022. Evaluation of Azure Kinect derived point clouds to determine the presence of microhabitats on single trees based on the Swiss standard parameters. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43(B2-2022): 989-994.
- Forsman, M., Borlin, N. and Holmgren, J., 2016. Estimation of tree stem attributes using terrestrial photogrammetry with a camera rig. Forests, 7(3): 61.
- Ganz, S., Käber, Y. and Adler, P., 2019. Measuring tree height with remote sensing—a comparison of photogrammetric and LiDAR data with different field measurements. Forests, 10: 694.
- Gollob, C., Ritter, T., Kraßnitzer, R., Tockner, A. and Nothdurft, A., 2021. Measurement of forest inventory parameters with Apple iPad Pro and integrated LiDAR technology. Remote Sensing, 13: 3129.
- Holcomb, A., Tong, L. and Keshav, S., 2023. Robust single-image tree diameter estimation with mobile phones. Remote Sensing, 15: 772.
- Hyyppä, J., Virtanen, J.P., Jaakkola, A., Yu, X., Hyyppä, H. and Liang, X., 2017. Feasibility of Google Tango and Kinect for crowdsourcing forestry information. Forests, 9(1): 6.
- Iglhaut, J., Cabo, C., Puliti, S., Piermattei, L., O’Connor, J. and Rosette, J., 2019. Structure from motion photogrammetry in forestry: A review. Current Forestry Reports, 5: 155-168.
- Jaud, M., Kervot, M., Delacourt, C. and Bertin, S., 2019. Potential of smartphone SfM photogrammetry to measure coastal morphodynamics. Remote Sensing, 11: 2242.
- Jiang, R., Jáuregui, D.V. and White, K.R., 2008. Close-range photogrammetry applications in bridge measurement: Literature review. Measurement, 41(8): 823-834.
- Karimzadeh Jafari, E. and Soosani, J., 2021. The efficiency of augmented reality technology in smartphones for estimating the height of trees (case study: green space conifers of Lorestan Factulty Agriculture and Natural Resources). Forest and Wood Products, 207(2): 197-207 (In Persian with English summary).
- Karimzadeh Jafari, E., Soosani, J., Varshosaz, M. and Naghavi, H., 2023. Investigatin the accuracy of iPhone LiDAR in preparing point clouds of tree trunks (Case study: Middle Zagros - oak forests of Lorestan province). Journal of Geomatics Science and Technology, 12(3): 63-73 (In Persian with English summary).
- Lim, K., Treitz, P., Wulder, M., St-Onge, B. and Flood, M., 2003. LiDAR remote sensing of forest structure. Progress in Physical Geography: Earth and Environment, 27: 88-106.
- Luetzenburg, G., Kroon, A. and Bjørk, A.A., 2021. Evaluation of the Apple iPhone 12 Pro LiDAR for an application in geosciences. Scientific Reports, 11: 22221.
- McGlade, J., Wallace, L., Reinke, K. and Jones, S., 2022. The potential of low-cost 3D imaging technologies for forestry applications: Setting a research agenda for low-cost remote sensing inventory tasks. Forests, 13: 204.
- Micheletti, N., Chandler, J.H. and Lane, S.N., 2015. Investigating the geomorphological potential of freely available and accessible structure-from-motion photogrammetry using a smartphone. Earth Surface Processes and Landforms, 40: 473-X486.
- Murtiyoso, A., Hristova, H., Rehush, N. and Griess, V.C., 2022. Low-cost mapping of forest under-storey vegetation using spherical photogrammetry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-2/W1: 185-190.
- Naeimaei, R. and Ghanbari Parmehr, E., 2023. Assessing the influence of image network and image texture on the quality of 3D point cloud production in close-range photogrammetry. Journal of Remote Sensing and Geoinformation Research, 1(2): 189-204 (In Persian with English summary).
- Naghibi Rad, S.A., Darvishsefat, A.A., Fatehi, P., Namiranian, M., Saadatseresht, M. and Boroumand, M., 2024. Evaluation of Octree-Based Segmentation (OBS) method to seperate ground point based on the handheld laser scanner data. Iranian Journal of Forest, 16(1): 137- 155 (In Persian with English summary).
- Pavlis, T.L., Langford, R., Hurtado, J. and Serpa, L., 2010. Computer-based data acquisition and visualization systems in field geology: Results from 12 years of experimentation and future potential. Geosphere, 6: 275-294.
- Poorazimy, M., Shataee Jouibary, Sh., Mohammadi, J. and Aghababaei, H., 2023. Feasibility of single-polarized TanDEM-X data for Hyrcanian forest height estimation (Case study: Shast-Kalateh forest). Iranian Journal of Forest, 15(3): 329-343 (In Persian with English summary).‏
- Sadeghian, H., Naghavi, H., Maleknia, R. and Sosani, J., 2022. Estimating the quantitative characteristics of seedlings using terrestrial close-range photogrammetry. Journal of Forest Research and Development, 7(4): 639-561 (In Persian with English summary).
- Schuon, S., Theobalt, C., Davis, J. and Thrun, S., 2008. High-quality scanning using time-of-flight depth superresolution. Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Anchorage, Alaska, USA, 23-28 Jun. 2008: 7p.
- Tatsumi, S., Yamaguchi, K. and Furuya, N., 2023. ForestScanner: A mobile application for measuring and mapping trees with LiDAR-equipped iPhone and iPad. Methods in Ecology and Evolution, 14: 1603-1609.
- Tavani, S., Billi, A., Corradetti, A., Mercuri, M., Bosman, A., Cuffaro, M., Seers, T. and Carminati, E., 2022. Smartphone assisted fieldwork: Towards the digital transition of geoscience fieldwork using LiDAR-equipped iPhones. Earth-Science Reviews, 227: 103969.
- Trochta, J., Krůček, M., Vrška, T. and Král, K., 2017. 3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR. PLoS ONE, 12(5): e0176871.
- van Leeuwen, M. and Nieuwenhuis, M., 2010. Retrieval of forest structural parameters using LiDAR remote sensing. European Journal of Forest Research, 129(4): 749-770.
- Vogt, M., Rips, A. and Emmelmann, C., 2021. Comparison of iPad Pro®’s LiDAR and TrueDepth capabilities with an industrial 3D scanning solution. Technologies, 9: 25.
- Xu, Z., Shen, X. and Cao, L., 2023. Extraction of forest structural parameters by the comparison of structure from motion (SfM) and backpack laser scanning (BLS) point clouds. Remote Sensing, 15: 2144.
- Zamani, P., Mohajeri, S.H. and Samadi, A., 2019. Application of structure from motion (SFM) method to determine the bed surface particles sizes in gravel bed rivers. Iranian Journal of Soil and Water Research, 50: 215-230 (In Persian with English summary).
Iranian Journal of Forest and Poplar Research
Volume 33, Issue 2 - Serial Number 100
October 2025
Pages 101-117

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