Validation of smartphone imaging for leaf area index estimation in man-made plantations of needle and broad-leaved stands in Chitgar Forest Park, Iran

Document Type : Research article

Authors

1 Ph.D. Student., Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Associate Prof., Department of Wood and Paper Sciences and Technology, Ka. C., Islamic Azad University, Karaj, Iran

3 Corresponding author, Prof., Department of Forestry and Forest Economics, Faculty of Natural Resources, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

4 Prof., Department of Forestry and Forest Economics, Faculty of Natural Resources, University College of Agriculture and Natural Resources, University of Tehran, Karaj, ‎Iran

5 Assistant Prof., Department of Forestry, Faculty of Agriculture and Natural Resources, University of Tabriz, Ahar, ‎Iran

Abstract

Background and Objectives: Indirect methods for measuring the leaf area index (LAI) such as hemispherical photography using a digital camera equipped with a fisheye lens, offer high accuracy; however, they are cost-prohibitive and logistically challenging for field applying. Recent technological advancements in smartphones and the availability of clip-on fisheye lenses have enabled the potential use of these devices for LAI estimation. This study aimed to compare the accuracy of hemispherical images from smartphones compared to digital cameras in estimating LAI in species of Robinia pseudoacacia L., Pinus eldarica Medw., Celtis australis L., and Fraxinus rotundifolia Mill. in Chitgar Forest Park in Tehran County, Iran.
Methodology: A total of 169 sampling points were selected across four stands, including 47 points in R. pseudoacacia, 36 in P. eldarica, 47 in F. rotundifolia, and 39 in C. australis. For sampling, the first point was randomly selected at each location, and subsequent photographs were taken at 10-meter intervals beneath the canopy. At each point, two hemispherical images were taken simultaneously using both digital camera and a smartphone. All photographs were taken at a height of 0.5 meters under pre-sunrise conditions to ensure uniform lighting. The leaf area index was calculated from images using GLA software, data normality was assessed in SPSS, followed by paired t-tests. For linear regression modeling, 70% of the data was randomly selected for model calibration, while the remaining 30% was used for validation. This analysis was carried out in Microsoft Excel. The F-test was applied to evaluate the overall significance of regression coefficients. Additionally, the root mean square error (RMSE) and relative root mean square error (RRMSE) were calculated to quantify the regression model’s error, and bias was used to determine the model's underestimation or overestimation.
Results: The mean LAI measured by the digital camera was 0.8 (±0.27 SD) for R. pseudoacacia, 0.89 (±0.15 SD) for P. eldarica, 1.24 (±0.16 SD) for C. australis, and 0.77 (±0.27 SD) for F. rotundifolia. These values ​​for the smartphone were 0.66 (±0.31 SD), 0.83 (±0.19 SD), 1.07 (±0.14 SD), and 0.75 (±0.3 SD), respectively. The paired t-test revealed statistically significant differences in mean values (p < 0.05) for R. pseudoacacia, P. eldarica, and C. australis, while F. rotundifolia showed no significant difference (p > 0.05). The mean difference values were positive for all species, with the highest value observed in C. australis (0.2) and the lowest in F. rotundifolia (-0.028). All simple linear regression models were statistically significant based on F-tests. Also, based on the t-test, intercept was not significant in all linear regression models. The coefficients of determination (R²) values were determined 0.53 for R. pseudoacacia, 0.59 for P. eldarica, 0.73 for C. australis, and 0.71 for F. rotundifolia. Evaluation using the 30% validation dataset yielded RMSE and RRMSE values of 0.07 and 9% for P. eldarica, 0.1 and 10% for C. australis, 0.18 and 25% for F. rotundifolia and 0.18 and 33% for R. pseudoacacia. Smartphone measurements without modeling consistently underestimated values across all species, most markedly in C. australis with a 13.7% underestimation compared to reference values.
Conclusion: In the present study, digital camera data were used solely as a reference for model calibration and validation. After model development, the smartphone can independently estimate the leaf area index without requiring camera data. However, the model has only been validated under the conditions and species examined, and its application to other regions or species would require further calibration and validation. Moreover, a mid-range smartphone and a clip-on fisheye lens to achieve a 180-degree field of view. Although their accuracy and image quality are lower than digital cameras, they can serve as an effective and practical alternative for remote areas or large-scale field monitoring, particularly in species with homogeneous canopy structures.
 
Keywords: Fisheye lens, hemispherical photography, linear regression, model validation.

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References

- Arietta, A.Z.A., 2022. Estimation of forest canopy structure and understory light using spherical panorama images from smartphone photography. Forestry, 95(1): 38-48.
- Beeles, K.L., Tourville, J.C. and Dovciak, M., 2022. Characterizing canopy openness across large forested landscapes using spherical densiometer and smartphone hemispherical photography. Journal of Forestry, 120(1): 37-50.
- Bianchi, S., Cahalan, C., Hale, S. and Gibbons, J.M., 2017. Rapid assessment of forest canopy and light regime using smartphone hemispherical photography. Ecology and Evolution, 7(24): 10556-10566.
- Bréda, N., 2008. Leaf area index. In: Jørgensen, S.E. and Fath, B.D. (eds.), Encyclopedia of Ecology. Elsevier, pp. 2148-2154.
- Chen, J.M., 1996. Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands. Agricultural and Forest Meteorology, 80(2-4): 135-163.
- Chianucci, F., 2020. An overview of in situ digital canopy photography in forestry. Canadian Journal of Forest Research, 50(3): 227-242.
- Confalonieri, R., Foi, M., Casa, R., Aquaro, S., Tona, E., Peterle, M., ... and Acutis, M., 2013. Development of an app for estimating leaf area index using a smartphone: PocketLAI. Computers and Electronics in Agriculture, 91(0): 31-40.
- Confalonieri, R., Foi, M., Casa, R., Aquaro, S., Tona, E., Peterle, M., ... and Acutis, M., 2013. Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods. Computers and Electronics in Agriculture, 96: 67-74..‏
- Díaz, G.M., 2023. Optimizing forest canopy structure retrieval from smartphone-based hemispherical photography. Methods in Ecology and Evolution, 14(3): 875-884.
- Fang, H., Baret, F., Plummer, S. and Schaepman-Strub, G., 2019. An overview of global leaf area index (LAI): Methods, products, validation, and applications. Reviews of Geophysics, 57(3): 739-799.
- Fournier, R.A. and Hall, R.J., 2017. Hemispherical photography in forest science: Conclusions, applications, limitations, and implementation perspectives: 287-302. In: Fournier, R.A. and Hall, R.J. (Eds.). Hemispherical Photography in Forest Science: Theory, Methods, Applications. Managing Forest Ecosystems, Vol. 28. Springer, Dordrecht, 306p.
- Hosseini, S.N., Fattahi, R., Ebrahimi Pak, N.A. and Veysi, Sh., 2023. Estimation and evaluation of reference evapotranspiration using ERA5 dataset. Iranian Journal of Soil and Water Research, 54(2): 353-368 (In Persian with English summary).
- Hussain, S., Teshome, F.T., Tulu, B.B., Awoke, G.W., Hailegnaw, N.S. and Bayabil, H.K., 2025. Leaf area index (LAI) prediction using machine learning and UAV based vegetation indices. European Journal of Agronomy, 168: 127557.
- Jonckheere, I., Fleck, S., Nackaerts, K., Muys, B., Coppin, P., Weiss, M. and Baret, F., 2004. Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology, 121(1-2): 19-35.
- Khosravi, S., Namiranian, M., Ghazanfari, H. and Shirvani, A., 2012. Estimation of leaf area index and assessment of its allometric equations in oak forests: Northern Zagros, Iran. Journal of Forest Science, 58(3): 116-122.
- Miri, N., Fatehi, P., Darvishsefat, A.A., Pir Bavaghar, M. and Homolová, L., 2024. Leaf area index estimation in the Zagros forests of Iran using Sentinel-2 image and Gaussian Process Regression. Iranian Journal of Forest and Poplar Research, 31(4): 323-337 (In Persian with English summary).
- Moosavi, M., Jalilvand, H. and Asadi, H., 2016. Soil Seed Bank and above-ground vegetation at Chitgar Forest Park of Tehran. Iranian Journal of Forest and Poplar Research, 24(3): 474-484 (In Persian with English summary).
- Myneni, R.B., Hoffman, S. and Knyazikhin, Y., 2018. Leaf Area Index. Agronomie, 20(1): 3-22.
- Rahimikhoob, H., Delshad, M. and Habibi, R., 2023. Leaf area estimation in lettuce: Comparison of artificial intelligence-based methods with image analysis technique. Measurement, 222: 113636.
- Raj, R., Walker, J.P., Vinod, V., Pingale, R., Naik, B. and Jagarlapudi, A., 2021. Leaf area index estimation using top-of-canopy airborne RGB images. International Journal of Applied Earth Observation and Geoinformation, 96: 102282.
- Tering, R.R.G., Branzuela, N.E., Batiancela, M.A. and Tutor, R.P., 2025. Influence of elevation on Falcataria moluccana [Miq.] Barneby & J.W. Grimes gall rust incidence and severity in Agusan del Norte, Philippines. Jurnal Sylva Lestari, 13(1): 90-101.
- Tian, X., Jia, X., Da, Y., Liu, J. and Ge, W., 2025. Evaluating the sensitivity of vegetation indices to leaf area index variability at individual tree level using multispectral drone acquisitions. Agricultural and Forest Meteorology, 364: 110441.
- Wang, J., Xiong, Q., Lin, Q. and Huang, H., 2018. Feasibility of using mobile phone to estimate forest Leaf Area Index: A case study in Yunnan Pine. Remote Sensing Letters, 9(2): 180-188.
- Weiss, M., Baret, F., Smith, G.J., Jonckheere, I. and Coppin, P., 2004. Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling. Agricultural and Forest Meteorology, 121(1): 37-53.
- Welles, J.M., 1990. Some indirect methods of estimating canopy structure. Remote Sensing Reviews, 5(1): 31-43.
- Yan, G., Hu, R., Luo, J., Weiss, M., Jiang, H., Mu, X., … and Zhang, Z.Z., 2019. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology, 265: 390-411.
- Yang, J., Fang, J., Lu, D., Li, C., Shuai, X., Zheng, F. and Chen, H., 2025. Age-related changes in stand structure, spatial patterns, and soil physicochemical properties in Michelia macclurei plantations of South China. Life, 15(6): 917.

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