Integrating ARKit 6 (Arboreal Forest app) in woodland mensuration: A case study in the Zagros woodlands, Iran

Authors

  • Elham Karimzadeh Jafari Department of Forestry, Faculty of Agricultural and Natural Resources, Lorestan University, Khorramabad, Lorestan, Iran.
  • Javad Soosani Department of Forestry, Faculty of Agricultural and Natural Resources, Lorestan University, Khorramabad, Lorestan, Iran
  • Yousef Erfanifard Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran. and IDEAS NCBR sp. z o.o., Warsaw, Poland

DOI:

https://doi.org/10.15287/afr.2026.4056

Keywords:

crown diameter, cumulative distribution functions (CDFs), height, LiDAR, probability density functions (PDF), TruPulse 360B

Abstract

As augmented reality (AR) technology continues to evolve, its application in forestry measurement is gaining increasing attention. While previous research has largely focused on diameter at breast height (DBH) measurements, expanding AR-based tools to other tree parameters is essential for assessing forest structure, biomass, and growth. This study evaluates the effectiveness of ARKit 6 (Arboreal Forest app, AF), a LiDAR-integrated mobile tool, for measuring tree height and crown diameter, by comparing its measurements with those of TruPulse 360B (TP), a professional-grade laser rangefinder. A statistical framework employing probability and cumulative distribution functions was applied to 215 broadleaved and coniferous trees, with goodness-of-fit tests (Kolmogorov-Smirnov and Anderson-Darling) indicating that Weibull and Log-Normal distributions best described the measurements. Agreement between tools was assessed using Bland-Altman analysis and error metrics including bias, mean absolute error (MAE), and root mean square error (RMSE). For height, the bias was -0.01 m, MAE 0.125 m, and RMSE 0.211 m, while for crown diameter, bias -0.59 m, MAE 2.02 m, and RMSE 2.70 m, indicating strong agreement between AF and TP. Descriptive statistics indicated similar means, standard deviations, and skewness values, suggesting that AF provides measurements comparable to TP. Minor differences in kurtosis and skewness suggest how each tool handles extreme values. These findings highlight AF as a cost-efficient and reliable alternative to professional-grade tools for tree height and crown diameter estimation. As AR technology evolves, its role in forestry is expected to expand toward real-time data integration, modeling, and ecological assessments.

References

Ahamed A., Foye J., Poudel S., Trieschman E., Fike J., 2023. Measuring tree diameter with photogrammetry using mobile phone cameras. Forests 14(10): 2027. https://doi.org/10.3390/f14102027

Atkins J.W., Bhatt P., Carrasco L., Francis E., Garabedian J.E., Hakkenberg C.R., Hardiman B.S., Jung J., Koirala A., et al., 2023. Integrating forest structural diversity measurement into ecological research. Ecosphere 14(9): e4633. https://doi.org/10.1002/ecs2.4633

Borz S.A., Morocho Toaza J.M., Forkuo G.O., Marcu M.V., 2022. Potential of Measure App in estimating log biometrics: A comparison with conventional log measurement. Forests 13(7): 1028. https://doi.org/10.3390/f13071028

Borz S.A., Morocho Toaza J.M., Proto A.R.,

2024. Accuracy of two LiDAR-based augmented reality apps in breast height diameter measurement. Ecological Informatics 81: 102550. https://doi.org/10.1016/j.ecoinf.2024.102550

Celes C.H.S., de Araujo R.F., Emmert F., Lima A.J.N., Campos M.A.A., 2019. Digital approach for measuring tree diameters in the Amazon forest. Floresta e Ambiente 26: e20160384. https://doi.org/10.1590/2179-

8087.038416

Chioni C., Maragno A., Pianegonda A., Ciolli M., Favargiotti S., Massari G.A., 2023. Low-cost 3D virtual and dynamic reconstruction approach for urban forests: The Mesiano University Park. Sustainability 15(19): 14072. https://doi.org/10.3390/su151914072

Clark N.A., Wynne R.H., Schmoldt D.L., 1999. A review of past research on dendrometers. Forest Science 46(4): 570-576. https://doi.org/10.1093/forestscience/46.4.570

Coops N.C., Tompalski P., Goodbody T.R.H., Queinnec M., Luther J.E., Bolton D.K., White J.C., Wulder M.A., van Lier O.R., Hermosilla T., 2021. Modelling lidar-derived estimates of forest attributes over space and time: A review of approaches and future trends. Remote Sensing of Environment 260: 112477. https://doi.org/10.1016/j.rse.2021.112477

Dalla Corte A.P., Souza D.V., Rex F.E., Sanquetta C.R., Mohan M., Silva C.A., Almeyda Zambrano A.M., Prata G., Alves de Almeida D.R., et al., 2020. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture 168: 105815. https://doi.org/10.1016/j.compag.2020.105815

Erfanifard Y., Sheikholeslami N., 2017. Competitive interactions of Persian oak coppice trees (Quercus brantii var. persica) in a pure dry woodland revealed through point pattern analysis. Folia Geobotanica 52(2): 113-127. https://doi.org/10.1007/s12224-017-9280-3

Ferrara C., Puletti N., Guasti M., Scotti R., 2023. Mapping understory vegetation density in Mediterranean forests: Insights from airborne and terrestrial laser scanning integration. Sensors 23(1): 511. https://doi.org/10.3390/s23010511

Follett M., Nock C.A., Buteau C., Messier C.,

2016. Testing a new approach to quantify growth responses to pruning among three temperate tree species. Arboriculture & Urban Forestry 42(3): 133-145. https://doi.org/10.48044/jauf.2016.012

Gollob C., Ritter T., Kraßnitzer R., Tockner A., Nothdurft A., 2021. Measurement of forest inventory parameters with Apple iPad Pro and integrated LiDAR technology. Remote Sensing 13(15): 3129. https://doi.org/10.3390/rs13163129

Gorgoso-Varela J.J., Adedapo S.M., Ogana F.N.,

2024. A comparison of probability density functions fitted by moments and maximum likelihood estimation methods used for diameter distribution estimation. Forests,

15(3), 425. https://doi.org/10.3390/f15030425

Gülci S., Yurtseven H., Akay A.O., Akgul M.,

2023. Measuring tree diameter using a LiDAR-equipped smartphone: A comparison of smartphone- and caliper-based DBH. Environmental Monitoring and Assessment

195: 678. https://doi.org/10.1007/s10661-023-11366-8

Howie N.A., De Stefano A., 2024. Measuring tree diameter using LiDAR equipped iPad: An evaluation of ForestScanner and Arboreal Forest applications. Forest Science 70(4): 304-310. https://doi.org/10.1093/forsci/fxae017

Liang X., Kankare V., Hyyppä J., Wang Y., Kukko A., Haggrén H., Yu X., Kaartinen H., Jaakkola A., et al., 2016. Terrestrial laser scanning in forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing 115: 63-77. https://doi.org/10.1016/j.isprsjprs.2016.01.006

Liu C., Zhang S.Y., Lei Y., Newton P.F., Zhang L.,

2004. Evaluation of three methods for predicting diameter distributions of black spruce (Picea mariana) plantations in central Canada. Canadian Journal of Forest Research

34(12): 2424–2432. https://doi.org/10.1139/X04-117

Luetzenburg G., Kroon A., Bjørk A.A., 2021. Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences. Scientific Reports 11: 22221. https://doi.org/10.1038/s41598-021-01763-9

Luoma V., Saarinen N., Wulder M.A., White J.C., Vastaranta M., Holopainen M., Hyyppä J., 2017. Assessing precision in conventional field measurements of individual tree attributes. Forests 8(2): 38. https://doi.org/10.3390/f8020038

Magnuson R., Erfanifard Y., Kulicki M., Gasica T.A., Tangwa E., Mielcarek M., Sterenczak K.,

2024. Mobile devices in forest mensuration: A review of technologies and methods in single tree measurements. Remote Sensing 16: 3570. https://doi.org/10.3390/rs16193570

Molinier M., López-Sánchez C.A., Toivanen T., Korpela I., Corral-Rivas J.J., Tergujeff R., Häme T., 2016. Relasphone—Mobile and participative in situ forest biomass measurements supporting satellite image mapping. Remote Sensing 8(10): 869. https://doi.org/10.3390/rs8100869

Panagiotidis D., Abdollahnejad A., Surový P., Chiteculo V., 2016. Determining tree height and crown diameter from high-resolution UAV imagery. International Journal of Remote Sensing 37(24): 5831-5852. https://doi.org/10.1080/01431161.2016.1264028

Sadeghian H., Naghavi H., Maleknia R., Soosani J., Pfeifer N., 2022. Estimating the attributes of urban trees using terrestrial photogrammetry. Environmental Monitoring and Assessment 194: 625. https://doi.org/10.1007/s10661-022-10294-3

Sandim A., Amaro M., Silva M.E., Cunha J., Morais S., Marques A., Ferreira A., Lousada J. L., Fonseca T., 2023. New technologies for expedited forest inventory using smartphone applications. Forests 14(8): 1553. https://doi.org/10.3390/f14081553

Shen Y., Huang R., Hua B., Pan Y., Mei Y., Dong M., 2023. Automatic tree height measurement based on three-dimensional reconstruction using smartphone. Sensors 23(1): 7248. https://doi.org/10.3390/s230107248

Song J., Huang Q., Zhao Y., Song W., Fan Y., Lu C., 2023. Automatic extraction of forest inventory variables at the tree level by using smartphone images to construct a three-dimensional model. Forests 14(6): 1081. https://doi.org/10.3390/f14061081

Su J., Fan Y., Mannan A., Wang S., Long L., Feng Z., 2024. Real-time estimation of tree position, tree height, and tree diameter at breast height point, using smartphones based on monocular SLAM. Forests 15: 939. https://doi.org/10.3390/f15060939

Su Y., Wu Z., Zheng X., Qiu Y., Ma Z., Ren Y., Bai Y., 2025. Harmonizing remote sensing and ground data for forest aboveground biomass estimation. Ecological Informatics 86: 103002. https://doi.org/10.1016/j.ecoinf.2025.103002

Tatsumi S., Yamaguchi K., Furuya N., 2023. ForestScanner: A mobile application for measuring and mapping trees with LiDAR-equipped iPhone and iPad. Methods in Ecology and Evolution 14(10): 1603-1609. https://doi.org/10.1111/2041-210X.14072

Vastaranta M., Latorre E.G., Luoma V., Saarinen N., Holopainen M., Hyyppä J., 2015. Evaluation of a smartphone app for forest sample plot measurements. Forests 6(4): 1179-1194. https://doi.org/10.3390/f6041179

Wang F., Heenkenda M.K., Freeburn J.T., 2022. Estimating tree Diameter at Breast Height (DBH) using an iPad Pro LiDAR sensor. Remote Sensing Letters 13(6): 568-578. https://doi.org/10.1080/2150704X.2022.2051635

Yang M., Zhou X., Peng C., Li T., Chen K., Liu Z., Li P., Zhang C., Tang J., Zou Z., 2023. Developing allometric equations to estimate forest biomass for tree species categories based on phylogenetic relationships. Forest Ecosystems 10: 100130. https://doi.org/10.1016/j.fecs.2023.100130

Zhao Y., Im J., Zhen Z., Zhao Y., 2023. Towards accurate individual tree parameters estimation in dense forest: optimized coarse-to-fine algorithms for registering UAV and terrestrial LiDAR data. GIScience & Remote Sensing

60(1): 2197281. https://doi.org/10.1080/15481603.2023.2197281

Downloads

Published

2026-02-25

Issue

Section

Research article