Validation of smartphone imaging for leaf area index estimation in man-made needle and broad-leaved plantations 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, provide high accuracy; however, they are often costly and logistically challenging for field applications. Recent advances in smartphone technology and the availability of clip-on fisheye lenses have created new opportunities for low-cost LAI estimation. This study aimed to evaluate the accuracy of hemispherical images acquired with a smartphone relative to those obtained with a digital camera for estimating LAI in stands of Robinia pseudoacacia L., Pinus eldarica Medw., Celtis australis L., and Fraxinus rotundifolia Mill. in Chitgar Forest Park,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. The first sampling point in each stand was selected randomly, and subsequent photographs were taken at 10-m intervals beneath the canopy. At each point, two hemispherical images were captured simultaneously using a digital camera and a smartphone. All photographs were taken at a height of 0.5 m under pre-sunrise conditions to ensure uniform lighting. LAI values were derived from the images using GLA software. Data normality was assessed in SPSS, followed by paired t-tests. For linear regression modeling, 70% of the data were randomly selected for model calibration and the remaining 30% for validation. Regression analyses were conducted in Microsoft Excel. The F-test was used to evaluate the overall significance of regression models, while root mean square error (RMSE), relative root mean square error (RRMSE), and bias were calculated to assess model performance.
Results: The mean LAI values measured by the digital camera were 0.80 (±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. Corresponding smartphone-derived values were 0.66 (±0.31 SD), 0.83 (±0.19 SD), 1.07 (±0.14 SD), and 0.75 (±0.30 SD), respectively. Paired t-tests revealed significant differences (p < 0.05) between the two methods for R. pseudoacacia, P. eldarica, and C. australis, whereas no significant difference was found for F. rotundifolia (p > 0.05). Mean differences were positive for all species, with the largest difference observed in C. australis (0.20) and the smallest in F. rotundifolia (-0.028). All simple linear regression models were statistically significant according to the F-test, while intercept terms were not significant based on the t-test. The coefficients of determination (R²) were 0.53 for R. pseudoacacia, 0.59 for P. eldarica, 0.73 for C. australis, and 0.71 for F. rotundifolia. Validation using the remaining 30% of the dataset produced RMSE and RRMSE values of 0.07 and 9% for P. eldarica, 0.10 and 10% for C. australis, 0.18 and 25% for F. rotundifolia, and 0.18 and 33% for R. pseudoacacia. Direct smartphone measurements consistently underestimated LAI across all species, with the greatest underestimation observed in C. australis (13.7%) relative to reference values.
Conclusion: In this study, digital camera measurements were used solely as reference data for model calibration and validation. Once calibrated, the developed models enable smartphones to estimate LAI independently without requiring camera-derived measurements. However, the models were validated only for the species and environmental conditions examined and would require additional calibration and validation before application to other species or regions. Despite the lower image quality and accuracy of a mid-range smartphone equipped with a clip-on fisheye lens compared with a digital camera, this approach offers a practical and cost-effective alternative for LAI estimation in remote locations and large-scale field monitoring programs, particularly in stands with relatively homogeneous canopy structures.

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References

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